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Page 4
Executive summary
Page 8
Section 1
Understanding the value of customer experience
Page 13
Section 2
Defining analytics and business intelligence
Page 19
Section 3
Customer experience in communications and the broader
digital services value chain – applying analytics for
improved performance
Page 28
Section 4
Analysis: Service providers discuss present and future
actions and plans for addressing analytics in customer
Page 35
Section 5
Conclusions and recommendations
Page 38
Section 6
TM Forum’s contribution to analytics to improve the
customers’ experience
Page 48
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Executive summary
Seeking to increase profitability,
communications service providers (CSPs) are
increasingly focusing on customer experience
initiatives to differentiate themselves. This
is driven by recognition by CSPs of the
connection between customer experience
and profitability. The impact of a customer’s
experience on a service provider’s bottom line
is becoming increasingly clear through recent
studies across all industries; in fact, Bain &
Company research revealed that a 5 percent
improvement in customer retention rates
can yield as much as a 75 percent increase
in profits for companies across a range of
As the link between customer experience
and profitability is becoming more evident than
ever before, we thought it important to explore
the fundamentals of customer experience in
the communications industry. In addition, we
believe analytics can play a key role in each
phase of the customer lifecycle, as well as
in the planning and implementation of each
component of differentiation.
In this TM Forum report, we address the
relevant issues, strategies and priorities
developing in organizations striving to become
more customer-focused and profitable – in
the near future and mid term – through the
judicious application of customer experiencerelated analytics.
At TM Forum, we define customer
experience as the result of the sum of
observations, perceptions, thoughts and
feelings arising from interactions and
relationships between customers and their
service provider(s). Almost every customer
touch point – whether directly or indirectly
linked to service providers and their partners
– contributes to customers’ perception,
satisfaction, loyalty, and ultimately profitability.
We believe that analytics, both traditional and
real-time, can be used to:
n better understand customer motivation and behavior;
n to suggest attractive offers and predict the likelihood of offers being accepted;
n to model and improve customer experience-
related processes;
n to measure performance improvement and customer satisfaction;
n predict the likelihood of a customer churning, ideally so that action can be taken to avoid it.
In addition, analytics can be used to predict
customer lifetime value (CLV), which can be
used across the customer lifecycle to improve
both customer experience and profitability.
With so many touch points to manage and
so many possibilities for the deployment of
analytics, we have broken this report into
sections we thought would be most useful to
our members:
Section 1: Understanding the value of
customer experience
This section argues that CSPs must shun
the one-size-fits-all, altruistic approach of the
past, and view improvements in customer
experience as a profitable business strategy.
It explores how leading companies are
approaching customer experience to improve
their bottom line, focusing in particular on a
company achieving extraordinary business
results – despite global recession – through
mastery of virtually all aspects of customer
In that vein, we go on to explain the
relationship between customer experience,
customer loyalty and increased profitability.
Finally, it includes a detailed discussion of CLV
Analytics can be deployed across
almost every aspect of an enterprise
as an important driver of customer experience
strategies, and outlines why the clearest path
to profitability is through customer retention
and maximization of CLV.
Section 2: Defining analytics and business
Section 2 begins with a broad definition of
analytics: “processes and applications that
use data to support actionable insight for a
functional process in a specific context.” While
this definition may seem general, it is important
to recognize that analytics operate in a vast
domain, with myriad data sources, processes,
contexts, forms of insight, and recipients.
In fact, analytics can be deployed across
almost every aspect of the enterprise, and
even extended out across a broader market
or value chain. Given the range of the options,
and the market interest, the need for a general
definition is clear and the potential for market
confusion great. The key here, of course, is to
determine where the greatest payback lies for
the CSP and the achievability of that payback.
It continues by describing various
components of analytics and positioning them
within an overall business intelligence (BI)
context. Components include data and text
mining, data visualization and dashboards,
forecasting, query and reporting tools,
model management and quality assurance/
process optimization tools, as well as
important supporting functions like access
for the consumers of the information, data
repositories, and data integration.
It then briefly overviews leaders in analytics
deployment, including Google, Amazon, and
NetFlix. Interestingly, while these companies
are widely known for their recommendation
engine capabilities, they also rely heavily on
process optimization and quality assurance to
maintain their competitive positions.
Some CSPs will find it difficult, at best, to
duplicate the success of these companies,
given the data management and integration
challenges arising from their long histories
of diverse lines of businesses, geographic
expansion, mergers and acquisitions, purposebuilt networks, and stove pipe operations.
Despite these challenges, analytical
applications and BI components remain
powerful tools for better understanding
market and operational trends and processes,
and supporting decision making to improve
business and operational performance, and
Section 3: Customer experience in
communications – applying analytics for
improved performance
This section of the report discusses the various
stages of the customer lifecycle and the
applicability of analytics. More specifically, it
draws upon six areas in which service providers
can differentiate themselves, including product
and service portfolio, marketing and sales,
service quality, customer support, billing,
charging and cost management, and brand. It
then discusses opportunities for analytics in
each area in the context of the lifecycle. Some
examples of analytics include:
“Many service providers
are challenged by their
legacy systems, islands of
business processes and
customer data stores. In
addition current economic
pressures are driving
much tactical activity…”
nCLV calculation
nsegmentation modeling and analysis,
npredictive analytics for product adoption trends
nproduct profitability analysis
nprice plan analysis
ncampaign analysis
ncustomer retention
nreal-time service performance aggregation, correlation and reporting
nnext best action/real-time offer management
nprocess optimization through revenue assurance
nbrand value modeling and monitoring.
Section 4 – Service providers discuss
actions and plans for addressing analytics in
customer experience.
Section 4 explores the results of 20 interviews
conducted with executives in leading service
provider organizations across the world. Many
service providers are challenged by their legacy
systems, islands of business processes and
customer data stores. In addition, current
economic pressures are driving much tactical
activity, mostly focused on reducing cost and
cycle time in specific areas. The top three near
term priorities by far were; reducing support
costs, improving customer retention, and
increasing customer satisfaction
Looking to the future, many service providers
are more conservative than they were last
year, but senior management seems to still be
embracing the idea of an holistic approach to
customer experience, and in some cases the
seeds of implementation are being sown.
Looking ahead over the next two to four
years, service providers are more cautious,
but still plan a more holistic approach.
While reducing support costs and improving
customer retention remain important, CSPs
plan to increase focus on:
n improving data management,
n solving customer experience issues across organizations,
n increasing process agility around customer responsiveness.
Most expect analytics to play a key role.
As importantly, most respondents expect a
stronger focus on digital services enablement.
They also expect to deliver on the promise of
the new services and business models that are
discussed more and more often in the industry
The biggest concern with the survey results
was that, when asked about eight different
areas for investment in analytics, all were
ranked as very important, and current efforts in
these areas were generally viewed as needing
improvement. At a time when budgets are
squeezed to say the least, CSPs will need to
make some tough decisions to narrow their
priorities for analytics’ deployment.
CSPs believe their biggest challenges for
analytics’ deployment are total costs, data
integration issues, and overall complexity.
As for critical success factors, topping the
list were data quality, clear business cases,
management commitment and well understood
business problems.
Section 5 Conclusions & recommendations
We close the report with a series of
recommendations for service providers
approaching customer experience initiatives.
They address:
1. Understand the big picture
Service providers must develop and manage
an enterprise-wide vision of all aspects of
customer interaction, and where analytics
fit best if they are to deliver an appropriate
experience to customers. An overall,
enterprise-wide view is most certainly how the
customer experiences a provider; when that
customer interacts with individual departments
within the CSP, the service provider can only
understand what the customer experiences if
they draw data from all parts of the enterprise.
2. Pick your places
A successful customer experience strategy
does not require the CSP to be world class at
everything, and in fact CSPs probably cannot
afford to excel in every aspect. CSPs must
determine which areas are most important
for them to succeed and where they can get
payback from analytics. The concept here is that
the customer experience strategy and the related
analytics deployment strategy must be tailored
and affordable. After all, as we said at the outset,
this is not an exercise in altruism, but rather a
way to enhance competitiveness and profitability.
3. Mix in some small, fast deployments
CSPs are typically large scale enterprises, and
their systems and projects reflect that and
consequently tend to take considerable time
to implement. By choosing a few small but
impactful areas where analytics can be quickly
deployed, CSPs can quickly realize benefits
and gain some momentum. Selected real-time
analytics may be a good place to start.
4.Consider a continuous improvement
It makes sense to approach analytics for
customer experience from a continuous
improvement perspective given the scope and
complexity of the industry, the volatility of the
larger digital value chain, the broad scope of
analytics-related opportunities and limitations
on investment capital.
Customer experience analytics hinge
upon data’s accuracy and accessibility
5. Manage customer data as a corporate
Yes, this is a valuable corporate asset, but
CSPs need to recognize that comprehensive
data integration is a long term project. Almost
every aspect of customer experience analytics
hinges upon the accuracy and accessibility
of data. Data management programs must
address quality issues, and ensure data
accessibility and usability – but they must also
walk before they can run.
6. Privacy
Privacy is a critical aspect of data managementA single publicly disclosed violation of local
privacy laws can have significant impact on a
company’s brand, and ultimately its relationship
with its customers.
7. Gain top management support
For analytics to become pervasive and add
enterprise wide value, top management must
‘walk the walk”, embracing fact-based decision
making, pushing for more and better data,
and recognizing achievement when efforts
succeed. Senior management must also drive
priorities for targeting analytics applications,
setting the vision, determining affordability,
allocating appropriate resources, ensuring
cross-functional coordination, and removing
barriers to success.
8. Take advantage of existing frameworks
TM Forum has a number of useful artifacts in
this space for service providers and vendors
alike – most notably its Information Framework
(SID), in addition to the Applications
Framework (TAM) and Business Process
Frameworks (eTOM), all of which are elements
of TM Forum Frameworx Integrated Business
In addition, TM Forum’s Managing Customer
Experience Collaboration Program is addressing
a number of issues. Finally, the Forum’s
Business Benchmarking Program can be
used as a source of business intelligence (for
information about all of these aspects, see
Section 6 on page 38).
9. Find ways to partner with your suppliers
While the applications and implementation
world is far from perfect, it is clear from our
research that some companies are better than
others at engaging and drawing successful
engagements from their suppliers. Both
CSPs and suppliers struggling in this regard
should do a fresh assessment of themselves
and their expectations, engagement styles
and strategies. Service providers should also
include evaluations of cultural fit, experience
and methodology into their supplier selection
10. Develop your people.
Analysts must acquire and master a broad
variety of skills, including quantitative/technical
expertise, business knowledge and process
design abilities, relationship building, and
consulting and coaching skills to help others.
It is important for employers to recognize
these requirements and traits, and to create
appropriate growth opportunities for analysts.
Activity in customer experience has moved
slowly so far in the communications industry,
but service providers are clearly waking up
to the value of customer retention and the
importance of optimization of CLV. Improving
customer experience can do nothing but help
service providers in this venture, improving
their profitability, and positioning them as more
valuable than ever to current and potential
business partners.
Section 6 - TM Forum’s contribution
to analytics to improve the customers’
A brief outline of the various ways TM Forum
is supporting members, from standards to best
practices, Catalyst Projects, documentation,
publications and research, and the new
Business Metrics Development Program and
Data Analytics Team.
“A single publicly disclosed violation of local privacy laws can
have significant impact on a company’s brand, and ultimately its
relationship with customers.”
Section 1
Understanding the value
of customer experience
Buffeted by economic woes and market forces,
especially in mature markets, communications
service providers (CSPs) increasingly focus
on improving customer experience. In fact, it
seems difficult to find a major communiqué by
a C-level executive in the developed world that
does not include something on “meeting and
exceeding customers’ needs”. Yet frequently
in customer satisfaction studies by prominent
firms, CSPs fall short of the leadership
demonstrated by other industries that take
customer-centric approaches to their bottomline strategies. Consider the following:
Despite the continued impact of global
economic crisis, in July 2010, Apple Computer
posted record revenue and net quarterly profit.
Those who attribute the results primarily to the
iPhone 4 launch should note that Apple also
shipped 33 percent more Macintosh computers
than the same period the previous year. Even
sales of the iPod line increased by 8 per cent in
a highly commoditized, shrinking media player
market. Finally, Apple began selling iPads
during the quarter, with total sales of more
than 3 million units.
What does Apple have that the others lack?
Well, some great products (and services) to
be sure, but it also excels at customer service
and support, marketing, and distribution, and
has one of the strongest brands globally.
Its products are useful, simple to use, easy
to acquire and augment, high quality, and
considered very cool. They also evoke such
an emotional response from many of Apple’s
customers, that they turn up their noses at
competitive products.
In other words, Apple appears to have
mastered virtually every aspect of customer
experience, and the resultant loyalty of its
customer base – even in difficult financial
times. Through that unwavering customer
focus, Apple continues to drive its revenues
and profits to new heights.
Other customer loyalty leaders like Wal-Mart,
Google, Toyota and Honda are also doing well
by focusing on customer experience as an
essential driver of profitability.
Service providers should note this
performance and ask themselves how they
might leverage the same principles to increase
their own profitability. After all, that is what
customer experience and loyalty are all about:
To successfully manage all the critical touch
points of customer experience, CSPs must
shun the one-size-fits-all approach. They can
no longer afford to view customer service
fundamentally as an act of altruism – that
mentality dates back to the industry’s civil
service days, when CSPs were typically
government organizations that were critical to
economic development and public safety.
As regulators and public officials have
pushed, and continue to push, service
providers to new heights of reliability – using
incentives and punishments – most CSPs
already have some of the fundamental building
blocks of customer service in place. Yet despite
that history and experience, service providers
still lag other industries in providing what is
seen as good customer service.
As we observed in our 2009 Insights
Research report, Customer Experience
Management: Driving Loyalty & Profitability
there has been a resurgence in interest by
CSPs. More and more of them have stated
ambitions to catch up other industries, and
they are realizing that good customer service
is a powerful strategy for increasing business
performance and profitability, not an act of
good will.
CSPs are recognizing the connection
between customer experience and profitability,
as demonstrated in many studies. For example,
Customer experience extends to domains
beyond the direct control of the service provider
according to research by Bain & Company, a
5 percent improvement in customer retention
rates can yield as much as a 75 percent
increase in profits for companies across a
range of industries.
After decades of customer experience
strategy formulation, Bain partner and noted
business author, Frederick Reichheld, considers
“would you recommend us to a friend?” as the
ultimate question for a customer. How many
times have you or your friends recommended
an iPod, iPhone or a Mac? What do your
children recommend to their peers? Their peers
to them?
There are certain steps service providers
have to take to create more personalized
relationships with their customers, as well as
reduce churn and increase profitability, all while
becoming leaner and more agile.
First, they have to define customer
experience. At TM Forum, we define it as the
result of the sum of observations, perceptions,
thoughts and feelings arising from interactions
and relationships between customers and
their service provider(s). Virtually every
customer touch point – whether directly or
indirectly linked to service providers and their
partners – contributes to customer perception,
satisfaction, loyalty, and ultimately profitability.
Gaining leadership in customer experience
and satisfaction will not be a simple task,
as it is affected by virtually every customerfacing aspect of the service provider, and in
turn impacts the service provider deeply –
especially on the all-important bottom line. The
scope of issues affecting customer experience
is complex and dynamic.
With new services, devices and applications
extending the basis of customer experience
to domains beyond the direct control of the
service provider, it is likely to increase in
complexity and dynamism.
In this report, we explore the fundamentals
of customer experience in the communications
industry, and look at how analytical applications
can be used to improve customer experience.
Customer loyalty = increased profits
As stated earlier, customer experience
programs are not fundamentally altruistic
exercises, but a strategic means of improving
competitiveness and profitability in the short
and long term. Loyalty is essential to deriving
long term profits from customers.
Some of the earliest loyalty programs date
back to the 1930s, when packaged goods
companies offered embedded coupons for
rewards to buyers, and eventually retail chains
began offering reward programs to frequent
shoppers. These programs continued for
decades but were leapfrogged in the 1980s by
more aggressive programs from the airlines.
This movement was led by American
Airlines, which launched the first full-scale
loyalty marketing program of the modern era
with the AAdvantage frequent flyer scheme.
It was the first to reward frequent fliers with
notional air miles that could be accumulated
and later redeemed for free travel.
Other airlines and travel providers were
quick to grasp the incredible value of providing
customers with an incentive to use their
company exclusively. Within a few years,
dozens of travel industry companies launched
similar initiatives and now loyalty programs
are achieving near-ubiquity in many service
industries, especially those in which it is
difficult to differentiate offerings by product
The belief is that increased profitability will
result from customer retention efforts because:
Figure 1-1: Customer loyalty driven profit opportunities
nThe cost of acquisition occurs only at the
beginning of a relationship: the longer the relationship, the lower the amortized cost;
nAccount maintenance costs decline as a
percentage of total costs, or as a percentage
of revenue, over the lifetime of the
nLong term customers tend to be less inclined
to switch and less price sensitive which
can result in stable unit sales volume and
increases in dollar-sales volume;
nLong term customers may initiate word-ofmouth promotions and referrals, which cost
the company nothing and arguably are the
most effective form of advertising;
nLong-term customers are more likely to
buy ancillary products and higher margin
supplemental products;
nLong term customers tend to be satisfied
with their relationship with the company
and are less likely to switch to competitors,
making market entry or competitors gaining
market share difficult;
nRegular customers tend to be less expensive
to service, as they are familiar with the
processes involved, require less ‘education’,
and are consistent in their order placement;
nIncreased customer retention and loyalty
makes the employees’ jobs easier and more
satisfying. In turn, happy employees feed
back into higher customer satisfaction in a
virtuous circle.
Figure 1-2 represents a high-level example
of a virtuous cycle driven by customer
satisfaction and loyalty, depicting how
superiority in product and service offerings, as
well as strong customer support by competent
employees, lead to higher sales and ultimately
profitability. As stated above, this is not a new
concept, but succeeding with it is difficult. It
has eluded many a company driven to achieve
profitability goals.
Of course, for this circle to be virtuous, the
customer relationship(s) must be profitable.
profit - referrals
profit- lower support costs
profit - increased spending
base profit
Period 1
Period 2
Period 3
Period 4
Figure 1-2: The virtuous circle of customer loyalty
Training and
of employees
Higher sales
& profits
Complex account structures may not
be understood or properly represented
Striving to maintain the loyalty of unprofitable
customers is not a viable business strategy.
It is, therefore, important that marketers can
assess the profitability of each customer (or
customer segment), and either improve or
terminate relationships that are not profitable.
This means each customer’s ‘relationship
costs’ must be understood and compared to
their ‘relationship revenue’.
Customer lifetime value (CLV) is the most
commonly used metric here, as it is generally
accepted as a representation of exactly how
much each customer is worth in monetary
terms, and therefore a determinant of exactly
how much a service provider should be willing
to spend to acquire or retain that customer.
CLV models make several simplifying
assumptions and often involve the following
nChurn rate represents the percentage of
customers who end their relationship with a
company in a given period;
nRetention rate is calculated by subtracting the churn rate percentage from 100;
nPeriod/horizon equates to the units of
time into which a customer relationship can
be divided for analysis. A year is the most
commonly used period for this purpose.
Customer lifetime value is a multi-period
calculation, often projecting three to
seven years into the future. In practice,
analysis beyond this point is viewed as
too speculative to be reliable. The model
horizon is the number of periods used in the
nPeriodic revenue is the amount of revenue
collected from a customer in a given
period (though this is often extended
across multiple periods into the future to
understand lifetime value), such as usage
revenue, revenues anticipated from cross
and upselling, and often some weighting for
referrals by a loyal customer to others;
nRetention cost describes the amount of
money the service provider must spend, in a
given period, to retain an existing customer.
Again, this is often forecast across multiple
periods. Retention costs include customer
support, billing, promotional incentives and
so on;
nDiscount rate means the cost of capital
used to discount future revenue from a
customer. Discounting is an advanced
method used in more sophisticated CLV
nProfit margin is the projected profit as a
percentage of revenue for the period. This
may be reflected as a percentage of gross or
net profit. Again, this is generally projected
across the model horizon to understand
lifetime value.
A strong focus on managing these inputs
can help service providers realize stronger
customer relationships and profits, but there
are some obstacles to overcome in achieving
accurate calculations of CLV, such as the
complexity of allocating costs across the
customer base. There are many costs that
serve all customers which must be properly
allocated across the base, and often a simple
proportional allocation across the whole base
or a segment may not accurately reflect the
true cost of serving that customer;
This is made worse by the fragmentation
of customer information, which is likely to
be across a variety of product or operations
groups, and may be difficult to aggregate due
to different representations.
In addition, there is the complexity of
account relationships and structures to
take into consideration. Complex account
structures may not be understood or properly
represented. For example, a profitable
customer may have a separate account for a
second home or another family member, which
may appear to be unprofitable. If the service
provider cannot relate the two accounts, CLV
is not properly represented and any resultant
cancellation of the apparently unprofitable
account may result in the customer churning
from the profitable one.
In summary, if service providers are to
realize strong customer relationships and their
attendant profits, there must be a very strong
focus on data management. This needs to
be coupled with analytics that help business
managers and those who work in customerfacing functions offer highly personalized
solutions to customers, while maintaining
profitability for the service provider.
It’s clear that acquiring new customers
is expensive. Advertising costs, campaign
management expenses, promotional service
pricing and discounting, and equipment subsidies
make a serious dent in a new customer’s
profitability. That is especially true given the rising
subsidies for smartphone users, which service
providers hope will result in greater profits from
profits from data services profitability in future.
The situation is made worse by falling prices and
greater competition in mature markets.
Customer acquisition through industry
consolidation isn’t cheap either. A North
American service provider spent about $2,000
per subscriber in its acquisition of a smaller
company earlier this year. While this has allowed
it to leapfrog to become the largest mobile
service provider in the country, it required a total
investment of more than $28 billion (including
assumption of the acquiree’s debt).
While many operating cost synergies clearly
made this deal more attractive to the acquiring
company, this is certainly an expensive way to
acquire customers: the cost per subscriber in
this case is not out of line with the prices others
have paid for acquisitions.
While growth by acquisition certainly increases
overall revenues, it often creates tremendous
challenges for profitability. Organic growth
through increased customer loyalty and retention
is a more effective driver of profit, as well as a
stronger predictor of future profitability. Service
providers, especially those in mature markets,
are increasingly recognizing this and taking steps
toward a creating a more personalized, flexible
and satisfying experience for their customers.
In summary, the clearest path to profitability
for companies in virtually all industries is
through customer retention and maximization
of lifetime value. Service providers would do
well to recognize this and focus attention on
profitable customer relationships.
“While growth by acquisition certainly increases overall revenues, it
often creates tremendous challenges for profitability. Organic growth
through increased customer loyalty and retention is a more effective
driver of profit, as well as a stronger predictor of future profitability.”
Analytics are used to improve customers’
experience in many industries
Section 2
Defining analytics and business intelligence
Analytics are increasingly being used across
industries to improve the performance of
leading organizations. Many examples exist
in industries as diverse as financial services,
media and entertainment, gaming, and even
professional sports. Analytics have also been
used in the communications industry for some
time, though perhaps in a less inconsistent
manner than in prominent companies in other
To understand the nature and value of
analytics, it’s important to have a working
definition. Given the level of interest around
the subject, many have come up with their
own definitions, creating considerable
confusion in the market. Further, there is a
lack of clarity around analytics (or analytical
applications) and business intelligence (BI).
In fact, when we asked respondents to our
surveys for their working definitions, we got a
number of different answers.
For the purpose of this report, we will use
a broad definition of analytics, defining them
as “processes and applications that use data
to support actionable insight for a functional
process in a specific context.”
While this definition is quite general, it is
important to recognize that analytics operate in
a vast world, with data sourced from internal
warehouses, operational data stores, external
data feeds and structured or even unstructured
messages among other sources. Processes
may include almost any business, technical
or operational process. Contexts could be
historical, current, real-time, or future/predictive
as well as involving almost any location,
technology, device, service, customer status,
or any other business or technical aspect.
Supporting actionable insight could mean
providing a discrete answer, one or more
recommendations, or a prediction or forecast.
Recipients of the result might be an employee,
customer, partner, supplier, data store, or
another application or device. Given the range
of the options, and the market interest, the
need for a general definition is clear and the
potential for market confusion great.
There are many components of analytics.
Data and text mining
Data mining is an iterative process of creating
predictive and descriptive models to support
decision making, by uncovering previously
unknown trends and patterns in vast amounts
of data from across the enterprise. Text mining
applies the same analysis techniques to
unstructured, text-based documents.
Data visualization and dashboards
Data visualization adds advanced graphical
renditions of results to analytics and
exploratory data analysis, leading to better
analyses, faster decisions and more effective
presentations of analytic results. Dashboards
in particular often provide simpler, more
personalized views of relevant data to improve
understanding and evaluations of scenarios.
Forecasting applies analytical techniques,
such as time series, econometric modeling
and game theory, to predict outcomes based
on historical patterns and scenarios. It can
also be used to better understand past trends
and model business processes. Operations
research can use optimization, project
scheduling and simulation techniques to
identify the actions that will improve results as
Query and reporting
These tools allow analysts and users to link
to appropriate data stores to create relevant,
timely queries and reports.
Model management
This can be used to streamline the process of
creating, managing and deploying analytical
models, increasing professional productivity,
and reducing modeling errors.
Quality assurance/process optimization
These tools can be developed and deployed
to identify, monitor and measure the quality
of processes over time and apply root cause
analysis to complex problems. One of the
most important aspects of this area is process
optimization; analytical tools can be used to
model business processes and measure the
effectiveness and efficiency of the end-to-end
process, identifying actions that will improve
The use of analytics
Analytics are used across the enterprise for
a variety of purposes. Among other things,
analytics help enterprises understand revenue
and cost drivers, identify and gage financial
risk, understand value markets with the
most potential and associated value drivers,
measure supply chain performance, improve
the efficiency of internal processes, and
understand customers’ needs, customer
lifetime value, and loyalty.
Analytics are used across all industries, For
example analytics are used by financial services
companies to target high value customers and
manage risk, by retail companies to improve
their offers to customers and optimize their
inventories, by transportation companies to
measure on-time performance, by government
agencies to measure risk and fraud, and by
most companies to improve the efficiency of
their processes. Figure 2-1 illustrates a sample,
enterprise-wide view of analytics.
Web analytics have become particularly
popular recently. They are not just tools for
measuring website traffic, but can be also used
for business market research. Web analytics
applications can also help companies measure
the effects of non-web (such as print and
broadcast) advertising campaigns on website
traffic, for instance.
Google Analytics is the most popular
Figure 2-1: A high level view of enterprise wide analytics
we maximising shareholder value?
we understand our revenue and cost
drivers and their impact on the bottom line?
nDo we understand and proactively manage
our risk?
What markets?
What products and services?
n What key value drivers?
n What key performance indicators and measures?
is our network performing?
is it likely to perform in the future?
nHow are our services performing?
Are we spending too much on IT or too little?
n Is our IT service provisioning efficient?
n How do we increase speed to market?
Call resolution?
n How do we reduce process errors and write-
What do our customers want?
What type of customer should we be acquiring?
n Which customers do we want to retain?
n How do we value the revenues and costs of each customer?
What are our staffing needs?
What elements of our business strategy drives human resources (HR) and
workforce issues?
n Do our HR processes address employee needs?
n What is the cost of recruiting?
Amazon’s massive scale performance analytics
are as important as its recommendations
example. It is used to generate detailed
information for marketers about the visitors
to a particular website. Importantly, it can
track visitors from all referring entities,
such as search engines, display advertising,
email marketing, and digital collateral. Using
these tools, marketers can determine the
performance of the various referring entities.
Google uses its analytics in conjunction with
AdWords, its flagship advertising product,
to generate most of its revenues. AdWords
generates pay-per-click advertising, rendering a
variety of formatted ads. Google gained almost
$23 billion in ad revenues in 2009.
Leader in customer experience analytics is another leader in analytics
for customer experience, combining a variety
of applications of analytics to advance its
competitive position. Amazon famously
popularized the recommendation engine about
a decade ago with a system that suggests
items to customers based on what they and
others like them had previously bought, or that
they have browsed recently.
The system analyzes consumer purchases
by product descriptions, prices, ratings, and
other attributes, and then offers products from
Amazon’s vast inventory with similar attributes,
even offering low volume, ‘long tail’ products
if they meet the matching criteria. The goal is
to make the shopping process more effective,
but also to help the customers ‘discover’ what
they really want. Amazon views personalized
recommendations as a key differentiating
factor, and strives to create a ‘personal store’
experience for each customer.
Less well known but equally important are
Amazon’s performance analytics. While the
company tracks website performance across
about a dozen of its web properties, it also
uses simulation on an ongoing basis to model,
analyze and predict performance based on
predicted workloads. The primary goal of these
simulations is to help the company measure
website latency across the globe, but it is also
used to identify trends or issues, and simulate
different website usage scenarios, among
other things.
The simulations are done on a massive scale,
aiming to approximate the activity of almost
100 million active customer accounts across
their web properties. Of particular concern
to the company are seasonal peaks, such
as Black Friday (in the U.S., this is the day
after Thanksgiving, which is always a Friday,
and although it’s not an official holiday, many
Americans take a day’s leave and consequently
it has come to be seen in the U.S. as the first
official day of Christmas shopping) events.
Netflix also combines discovery analytics
with performance analytics and web
analytics to enhance its competitiveness.
According to company reports, Netflix is
the largest subscription service streaming
and mailing movies and TV series episodes.
Netflix currently has more than 15 million
subscribers. Its revenues for the first two
quarters of 2010 grew 26 percent, with
profits growing 38 percent. Subscribers are
reported to be growing at 41 percent while
the cost of acquiring a subscriber shrank 8.3
percent. NetFlix’s flagship analytic engine is
its Cinematch recommendation system, which
strives to create a personalized experience
for customers, not only in the selections of
material it makes, but also in how it presents
them to the subscriber.
Balancing distribution priorities
Netflix uses analytics to drive its controversial
‘throttling strategy’, balancing distribution
priorities across high use and low use
subscribers. Lower use subscribers are
given shipping priority, as they are the most
profitable customers, and of course, Netflix
wants to maintain their satisfaction and retain
them. The company also argues this is the
fairest approach.
Netflix also uses analytics in other areas (for
instance, valuing distribution rights), but the
suggestion and distribution analytics are at the
core of its customer experience strategy.
Analytics as part of a larger business
intelligence strategy
As complex as analytics are, they do not exist
in a vacuum. To be effective, they must be
deployed as part of broader BI strategy. Among
other things, this strategy must encompass:
Access for consumers of information
For analytics to realize their potential, their
results must be not only actionable, but
accessible to information consumers in an
appropriate form, at the right time. The BI
architecture must enable information consumers
– such as employees, customers, partners,
suppliers, or applications – to view results and
interact with business analytic applications
through a variety of facilities. These include
web browsers, portals, widgets or web services
using numerous devices, such as PCs, tablets,
mobile phones, kiosks and so on.
Data repositories
Data repositories are at the heart of any data
management strategy, and include various
types of data stores like data warehouses,
data marts, operational data stores, staged
data aggregations, and metadata repositories.
Although instances of these repositories often
do not exist as single entities, or use the same
technology, or reside in the same physical
location, it is important to the BI strategy that
there are methods to view them as single
logical repositories through federation. This all
sounds straightforward, but the sheer scope
and size of the various repositories makes
this a major challenge for almost all large
Data integration
This involves the functions and services
to source data, bring it into the warehouse
operating environment, improve its quality,
and format it for presentation. The data
must be extracted, cleansed, transformed,
aggregated, synchronized and loaded
according to established policies supporting
data warehousing, federation and information
security requirements. It may need both batchoriented and real-time master data management
capabilities, and must be able to execute within
the necessary production time windows.
Most importantly, it must deliver consistent,
trusted and verifiable information. Again, this
can be a monumental task in a large enterprise
where literally thousands of data sources
have been developed and deployed without
the benefit of common data definitions and
management policies. Moreover, as companies
rely increasingly on external data sources, they
control their data integration destiny even less.
Data management and data integration
are particular problems for service providers.
Unlike some more focused analytics leaders
(such as Google, Amazon, Netflix and so
on). CSPs have long histories of launching
various lines of businesses, expanding across
geographies, merging with or acquiring other
CSPs, and running their operations in a stove
pipe fashion.
In addition, historically their networks have
often been purpose built, with multiple,
separate networks or overlays managed
separately. This has created huge data
management and integration challenges for
CSPs, who are struggling to deal with the
resultant complexity.
Despite these challenges, analytical
applications and BI components remain
powerful tools for better understanding
market and operational trends and processes,
and supporting decision making to improve
business and operational performance,
and profitability. In the next section we will
see how these tools can be applied to the
customer lifecycle in the communications
Where are analytics going?
Having laid out the landscape in analytics and
BI, it is useful to look at trends that are likely
to develop over the next 12 to 18 months
across industries. These developments will be
important for CSPs to watch as they will create
both opportunities and challenges.
Technology is lowering the cost
of data warehousing dramatically
Self-service analytics empower end users
Self service analytics and BI tools for have
been available for a few years, but have
struggled to gain popularity across many
enterprises. With pressure on IT budgets
increasing, and information workers feeling
frustration with long backlogs on BI service
requests, self service analytics will likely regain
some visibility. Enterprises view this as a
way to cut development expense, shrink the
analytics development backlog, and expand the
scope of practical insights. Also contributing to
this trend is the availability of BI Software as
a Service (SaaS) offerings, which promise low
startup costs and near instant availability.
Social network and unstructured data
analysis bring powerful predictive analysis
Social networks have grown very quickly,
and likely will soon be part of most business
and personal applications, including mobile,
broadband, and streaming media services. In a
brand and reputation-driven, online economy,
or in select media and entertainment markets,
social networks help to make the difference
between success and mediocrity.
Many enterprises are adopting social network
monitoring and marketing tools, using analytics
to search unstructured data for opportunities to
better serve or even reach customers through
this knowledge. Many see 2010 as the year
social network analysis truly emerges as the
new frontier in advanced analytics, supporting
mining of behavioral, attitudinal, and other
affinities among individuals.
While social network content is only one
source of information, it can be used across
the customer lifecycle for CLV calculations,
segmentation, targeting, retention, and even
fraud analysis.
Low cost data warehousing spreads fast
analytics processing to new areas in the
Though analytics and BI can, and do, exist
independently from data warehouses, the
warehouses remain critical infrastructure for
many aspects of high performance reporting
and queries, and for applying analytics
across very large data stores. Over the last
few years, the emergence of low cost data
warehouse platforms and the migration of
data warehouses from specialized platforms to
general purpose computing infrastructure have
lowered platform costs dramatically.
This trend will continue over the next few
years, and even the newer configurations
will be pressed from a cost perspective
by emerging cloud-based warehousing.
Technology is playing a huge role here,
with massively parallel processing, solidstate drives, in-memory processing, storage
architectures and virtualized storage increasing
speed and lowering costs.
Lower costs make analytics more accessible
to a broader variety of business problems by
positively impacting return on investment.
Cloud on the horizon for data warehousing
As with other components of BI, data
warehouses are being targeted by cloud
computing suppliers, In fact, for web analytics,
the cloud is shaping up to be a preferred
platform. We will continue to see suppliers
introducing cloud, Software as a Service, and
virtualized deployments of their core analytic
capabilities, to offer public, private and hybrid
scenarios (see Insights Research report Cloud
Services: Issues and opportunities for service
providers and the Quick Insights report Cloud
services: The user’s perspective, both available
free to members from the TM Forum website).
This may not happen quickly, but the
industry is moving inexorably toward cloudbased services, which will supplement more
traditional data warehouses, licensed software,
and other deployment options.
Predictive modeling tools become more
Predictive analytics can be powerful tools,
helping business managers continually refine
strategies and plans based on flexible analyses
and forecasts that can leverage both historical
data and current event data.
Predictive analytics so far have been largely
the domain of statistics experts and highly
skilled data miners, but increasingly userfriendly predictive modeling tools are coming
to market. Sometimes they are stand-alone
toolsets, or more frequently now they are in
the guise of new capabilities within suppliers’
Much of the focus for new development
by suppliers is for mass market deployment,
using mechanisms such as wizards to ease
development and providing visual tools for
development and operation.
There are of course some issues to be
overcome before analytics can move forward.
They include:
n Information privacy concerns can slow the
deployment of social network analysis and
provoke regulators.
n Cloud-based deployments could perform
poorly, plus there are information privacy
issues, or the danger of becoming locked
into one supplier.
n Cloud-based solutions and low cost
appliances can encourage departmental
sub-optimization, which will not necessarily
benefit the organization overall.
n The general lack of capital to invest
could slow the adoption of new data
warehouse platforms.
Nevertheless, the trends outlined above
are positive for all types of businesses – and
in particular for data intensive industries
like communications – and the issues that
could potentially hold them back are being
n There is a lack of experience and skills
among end users, especially when dealing
with problematic data sets, which could
result in real difficulties and questionable
recommendations from analytics.
“Predictive analytics can be powerful tools, helping business managers continually
refine strategies and plans based on flexible analyses and forecasts that can
leverage both historical and current event data.”
As market penetration increases, so
does the cost of acquiring customers
Section 3
Customer experience in communications and
the broader digital services value chain –
applying analytics for improved performance
Seeking to increase profitability, communications
service providers (CSPs) increasingly turn to
initiatives in customer experience to differentiate
themselves. Four market-related trends are
driving customer experience, as depicted in
Figure 3-1. The trends are:
Figure 3-1: Market trends driving customer experience initiatives
nMarket maturity – while service providers
across the world have benefited from
the growth of mobile and broadband
communications services, markets for
today’s core communications products are
approaching, or have reached, full maturity.
Consequently, in most developed and many
emerging economies, it will take more than
excellence in core services to drive incremental
profitability. Customer experience is particularly
important in a mature market because as
market penetration increases, so does the cost
of acquiring customers. Also, as overall prices
stabilize (or decline), new, alternative services
or substitutes may come onto the market.
behavior &
nEconomic trends – fluctuations and changes
in economic trends often drive change in
the focus of customer experience programs.
For example, in difficult economic times,
customers’ discretionary spending may
become more conservative. Accordingly,
service providers’ focus may shift to
emphasize customer retention, shortening
cycle times and cost reduction. In better
economic times, the focus may target new
product/offer introduction, cross-selling/
upselling, and greater business agility. In this
report, TM Forum’s research clearly points to a
strong focus on cost reduction and retention,
but also plans for the future that lean strongly
toward revenue growth. Those plans are driven
by greater personalization in sales and service,
as well as increases in the effectiveness of
sales and marketing programs, and greater
speed in offering new products and services.
nEmerging technologies – though the
introduction of new technologies can often
energize markets, those technologies can
also shift the balance of market power. The
enablement and introduction of smartphones
in general (and the iPhone in particular), Web
2.0 applications (such as social networks),
and underlying technologies and frameworks
(such as service oriented architecture),
are all good examples of game-changing
technologies. For the communications
industry, these technologies can underpin
new services, but they can also be used to
create more efficient or effective delivery of
other aspects of customer experience. Our
research shows some conservatism in the
implementation of today’s programs, with an
expectation of more aggressive adoption of
new devices, application and technologies on
the horizon. It also shows growing acceptance
of the service provider’s role in digital
enablement of many third party solutions.
Service providers are turning to customer
experience more and more as a differentiator
as they seek to retain and upsell to customers,
as well as increase loyalty and attract new
subscribers. Their goal is to increase profitability
by focusing on customers with higher lifetime
value, and provide them with superior service.
They also hope to raise lifetime value by offering
a variety of attractive new services to those
customers as time goes by.
Nor is better customer experience only
about retaining and upselling regular services
to individuals, it can help a service provider
service secure a powerful position as enabler in
the emerging digital services value chain. It can
also support the development of a two-sided
business model for the CSP, to secure additional
revenues and profits.
Understanding customers’ preferences
A clear understanding of customers’ wants
and needs lies at the heart an effective
customer service strategy. While offers to
individual customers will become increasingly
personalized, it is important to recognize
that there is a common set of fundamental
characteristics for products and processes
that largely transcend industries and market
nCustomer behavior, preferences and
demands – in many respects, customer
demand is shaped by the preceding three
forces, but also by evolving demographic,
socio-political and attitudinal changes. An
example would be the predisposition of
young people toward instant communications
and social networking. While some
service providers may think of instant
communications as simply new services to be
offered, savvy companies know that they also
represent preferred channels of interaction
with these customers. They should also view
instant communications as channels through
which customers publicly critique their service
providers or recommend them to others. As
a result of that realization, several service
providers are taking some first steps to
leverage these channels.
Figure 3-2: The application of analytics across the digital value chain
Virtual / CSP / lifestyle
& support
transport &
core network
Service &
Bundling network
services, content
and wholesale
comms service
Manages customer
lifecycle and offers
products appropriate
for their lifestyle
They include:
These characteristics generally apply across
the customer lifecycle, as represented by
Figure 3-3.
n Better productivity as time is an increasingly
scarce commodity for most people, and
anything that can help save time, or make
better use of it, is attractive to consumers and
business customers alike.
n Simplicity and intuitiveness have become an
overriding issue for consumers over the last
decade with the explosion of new products,
services, and applications. Successful products
must be easy to find, acquire, use, upgrade
and maintain, or they are likely doomed to the
scrap heap.
n Convenience is very important; products and
services must be readily available given time
constraints and consumers’ ever shortening
attention spans.
n Risk – products and services must be
perceived as low risk by the customer.
Security, safety and reliability are especially
important characteristics, as are predictable
n The cool factor is key; products seen as
innovative, fun, cute or a boost to one’s
image or status have been shown to increase
desirability. Some aspects of personalization
(such as skinning) also fall into this category.
n Green – environmentally friendly products
are increasingly attractive to a broad audience.
Customer lifecycle
Service providers that develop strategies to
address these fundamentals across the customer
experience lifecycle will be at a considerable
competitive advantage. Addressing these issues
will help them acquire new customers, upsell to
current ones and increase overall profitability. The
advantage comes from being able to gain some
influence over customer experience, which TM
Forum defines as the “observations, perceptions,
thoughts and feelings arising from interactions
and relationships (direct and indirect) over an
interval of time between a customer and its
By applying the scope of customer experience
and the characteristics users demand to the
service providers’ business models, we have
identified six areas in which service providers can
differentiate themselves, including:
nProduct and service portfolio – the range
of products and services a CSP offers its
customers, including devices, connectivity
services, content, applications, and so on. This
includes pricing, acquisition and fulfillment;
Figure 3-3: Customer lifecycle – the customer’s view
Compelling products?
Attractive channel?
n Easy to determine the right solution?
n Can it be done quickly?
n Is it secure?
Simple process?
Convenient to use?
Easy to get support?
Compelling products?
Personalized interaction?
Easy to upgrade?
Can it be done quickly?
Is there any risk?
nMarketing and sales include pricing,
merchandising, offer management, campaign
management and initial fulfillment;
nService quality is the perceived quality
of services, including availability, usability,
sustainability, capacity, performance, stability
and security;
n Customer support refers to availability,
accessibility, breadth, speed and effectiveness
of support;
nBilling, charging and cost management –
much depends on the range and flexibility of
the billing and charging options available, and
enabling the customer to control costs based
on transparent billing information;
nBrand includes reputation for product
excellence, image, responsiveness and
Finally, there are additional investment
considerations that are important in developing
the customer experience differentiation
nWhich customers (or segments) are most
attractive from a customer lifetime value (CLV)
perspective? What investments are most likely
to attract and retain them? How to maximize
lifetime value?
nWhat is the investment budget, and the likely cost of capital during the investment period?
Ultimately, the service provider must maintain
appropriate levels of cash flow and profitability,
balancing the needs of both customers and
investors. There may also be variations on
funding strategies, such as success-based capital
in emerging markets, but with a bottom line
emphasis on matching investment capital with
initiatives that yield the highest return.
Analytics can play a key role in each phase of
the customer lifecycle, as well as in the planning
and implementation of each component of
differentiation. Here are some examples are key
analytical applications for each differentiation
Product and service portfolio
Clearly, an important aspect of customer
experience is having an attractive set of products
and services. Breadth is certainly important, but
the key to success lies in creating a series of
compelling offers – whether they are individual
services or, more likely, multi-service bundles that
appeal to the target customer base.
Bundling is a concept that emerged
approximately 15 years ago, yet service
providers still struggle to get that concept right.
While many converged operators offer a triple
play option, few have been able to translate
their assets into focused offers that target
segments. They instead opt to take a ‘moreis-better’ approach. While a few segments
(such as sports fans and movie mavens) are
sometimes well served, others are left to slog
through large swaths of content – effectively
acting as their own packagers.
Service providers should look to the web to
discover emerging trends that can be reflected in
their services. For example, the huge uptake of
social networking can be leveraged by offering a
package that encourages communication among
a pre-defined user group. Voice, messaging
and instant messaging services could all be
configured for use among that group with
the benefits of speed and convenience. Also,
leveraging services by integrating them tightly,
or taking an integrated multi-screen (handheld,
PC, TV, tablet) approach to service assets
could improve satisfaction, as well as generate
additional revenues. For example, allowing
customers to attach video trailers of new pay-perview titles to messages allows service providers
to leverage social networking principles and
promote products at the same time.
Perhaps an obvious example of a hot product
is the iPhone, which has been an acquisition
and retention engine for the companies offering
it. Some might argue that it hasn’t proved
profitable, though, as iPhone users tend to be
heavy users of flat-rate data plans, and Apple
retains control of its App Store. Regardless, the
top line revenues and subscriber gains have been
impressive. Notably, the iPhone offers many
of the key characteristics listed above under
Reduced cost and better services
are often the best twin outcomes
customer preferences, including productivity,
simplicity (ease of use), low risk, and definitely
the cool factor. Whether or not service providers
choose to go an iPhone-type route, it is clear that
a strong device portfolio, especially in wireless,
will be key to a strong customer experience.
Analytics can be used in a variety of ways to
improve product and offer development. For
nThrough segmentation analysis, CSPs can
develop products and offers that are better
aligned with their customer base;
nUsing predictive analytics to understand
adoption trends of features and functionality;
nBetter sales analysis to understand the profitability of products and bundles;
nGain greater knowledge of the level and
type of product acceptance through analysis of
unstructured text such as support logs, social
network content, blogs, and web reviews.
One of the most important strategies, and one
that often does not receive enough attention,
is eliminating barriers to usage. Simplicity and
convenience matter to customers. In fact, the
instantaneous nature of the web has driven
strong expectations of always on, easy to use
services among most customers. No matter
how good the service, content or device is, it
is at best a lost revenue opportunity and more
likely a significant cause of dissatisfaction if the
customer cannot get it to work.
One service provider we spoke with found
that incompatibilities in the default web browser
of one of its most promoted smartphones made
it difficult for users to acquire some of its new
video services, in some cases making it all but
impossible to turn on. Not only did this waste
promotion efforts and investments, it resulted
in frustrated customers and higher contact
center expenses. Certainly much of this could
have been avoided with proper process analysis
across devices.
Process analytics can help to eliminate
complexities, provide more predictable results
and lower costs for service providers. For
example, though some may not think of analytics
that model, measure and report on order-tocash flows under the umbrella of customer
experience, the speed and simplicity resulting
from the use of such analytics can have a positive
impact on customer experience and lower
process costs for the service provider. Reduced
cost and better service are the twin outcomes
many service providers are seeking.
Marketing and sales
Marketing and sales are in many cases the
workhorses of the acquisition cycle, and
important in retaining customers and improving
profitability. They are also drivers of important
touch points, supporting offer management and
educating the customer base through wellrun campaigns. Properly leveraged, they can
contribute to the bottom line in both good and
bad economic times. For example, the promotion
of new offers with simplified pricing models or
carefully targeted bundles can improve customer
perception, productivity, convenience and
simplicity, not to mention taking a greater share
of the customer’s wallet.
Intelligent, real-time offer management and
pricing optimization analytics can help make
offers more compelling by combining new
products on a trial pay-as-you-go basis, or offering
short term specials as incentives to customers
who might otherwise be unsure about an offer.
For example, something as simple as offering a
short term promotional price or perhaps some
free media product in exchange for an immediate
top-up could be compelling for some customers.
This last example is particularly critical during
difficult economic times, when discretionary
cash is harder to come by and the perception of
better value is an important factor. Of course,
the creation and implementation of such offers
may well require an upgrade of the marketing
systems, as well as other systems across the
fulfillment, billing and care areas.
Important analytical capabilities for marketing
nsegmentation models that consider
demographics, billing history, credit
worthiness, and loyalty
“Process analytics
can help to eliminate
complexities, provide
more predictable results
and lower costs for
service providers.”
nanalytics that develop customer lifetime value models for use in offer and engagement tactics;
nprofiling capabilities to identify customers who
might be candidates for additional services
(such as cross or upselling);
ncampaign management systems that integrate
with segmentation and profiling capabilities,
synchronize across multiple inbound and
outbound channels, and provide real-time
feedback on results;
nusing analytics to predict when a customer might be about to churn and suggest what to do about it.
Another important emerging capability in
marketing is that of next best action, an inbound
marketing technique that we discuss below as
part of customer support, as that is the context in
which it is often implemented.
Service quality
Service usage is, of course, the most common
experience the customer has with the service
provider. Despite that fact, monitoring the
quality of the service provided to individual
customers has been difficult for service providers
until recently. The difficulty lies in collecting
and aggregating data by customer rather than
asset or service, and to do so on an end-to-end
basis. Succeeding in doing this in real-time is
difficult, but important if a service provider is to
view networks and service performance as its
customers do.
This is a far more effective and specific picture
of a particular customer experience than given
by performance management systems, which
tend to either measure performance of networks,
or particular areas in networks, or perhaps the
experience of a group of users.
There are a variety of uses for the output of
these systems – including identifying poorly
performing network assets or devices – but
perhaps none is more powerful or useful than
providing an up-to-date view of a customer’s
experience to a customer service representative
(CSR) fielding a call in a service center. The quick,
accurate identification of problems can help
speed problem resolution and shorten the time
required by the CSR to satisfy the customer. It
can also lower the cost of support for the service
provider by shortening resolution cycle time,
making it a win-win for both CSP and customer.
Service providers can also use these systems
for proactive outreach, that is, informing a
customer of a corrected problem before the
customer discovers or reports it. This can
improve customer confidence and satisfaction,
and empower the service provider to capture
usage revenue it would have otherwise missed.
Again, this is all about profitability, not altruism,
as stressed in Section 1 of this report.
Key features for systems that manage service
quality include:
nscalability, and data correlation and reduction
to manage the sheer volume of network and
service data;
nnear real-time aggregation and reporting;
ncollecting and aggregating information, end-
to-end, from a broad variety of devices.
Strong data collection and analytical processes
are the keys to success, along with being able
to integrate results into core business and
operations processes.
Customer support
Customer support encompasses the response
to a variety of situations, from responding to
billing questions, to service problems, product
questions, and so on. Such a broad remit,
combined with the preference of human
contact by many high-touch customers can
create a challenging financial situation for
service providers. Most see the contact center
challenge as a balancing act between customer
satisfaction and operational expense. Some
keys to an effective, affordable customer care
strategy include:
nproviding an appropriate level of service based on a customer’s lifetime value;
nshortening cycle times;
nresolving problems on the first call;
nsupporting the customer through their preferred channel;
Next best action marketing is good
for customers and service providers
npersonalizing the experience;
nproactively caring for select customers, perhaps on particular issues.
Providing an appropriate level of service is
key to profitability. Building an effective support
strategy for a customer based on their lifetime
value, service portfolio and pricing plans is
the goal. This can be tricky because of the
variability of customer preferences. Certainly
self-care using web, interactive voice response
technologies and even messaging can be
effective. However, preferences vary among
business users and consumers, as well as high
tech and high touch customers, and among
young people and seniors. Understanding and
acting on particular customers’ orientation and
preferences is very important, and can drive
appropriate routing.
Driving down cycle times is a noble goal that
reduces cost for the service provider, while
improving the quality of customer service. This
can be accomplished by:
nproviding the CSR with the right customer
account information prior to or while call
routing is underway;
ntraining the CSR in questioning and problem-
solving techniques;
ndealing with simple questions through self-
Another key productivity aid for CSRs is rapid
access to relevant information from a service
quality management system on the trouble
experienced by a calling customer. Not only
does this shorten the amount of time needed
to determine the problem, it also lowers
the probability of misdiagnosis, and instills
confidence in the customer.
Important tasks for analytic applications
to support functions include providing
CLV calculations to determine appropriate
prioritization and dealing with the likelihood of a
customer churning by direct actions and offers.
Another effective approach is the analysis of
logs, and other information to improve the
efficiency of processes in specific contexts.
Service providers should be working
continually to reduce cycle time on core
services, as they need to master these areas
before moving on to more complex services in
There is a new opportunity in support
emerging through next best action marketing,
which is really more of an inbound strategy than
a support technique, but it is often delivered
by the support organization during the support
process. Unlike usual, outbound marketing
campaigns, next best action is suited to inbound
communication from customers as most of
them will expect a considered response to their
request, complaint or inquiry. Next best action
enables the CSP to respond to the customer’s
needs during the interaction, while ensuring that
the action taken also benefits the company.
Next best action relies on decision models
to help determine how to approach a customer
prior to, as well as during, an interaction. A
decision engine uses predictive statistical
modeling techniques to take into account
each customer’s expectations, likings and
probable behavior. The resultant approach
may be to make an offer, resolve a complaint,
or perhaps make another recommendation in
real-time, based on the customer’s response.
The supporting software combines the service
provider’s business rules with predictive and
adaptive analysis.
It’s still early days for next best action, but a
number of large service providers have already
used it successfully. Perhaps the most critical
dependency for the approach is the quality and
quantity of the customer data with which the
decision engine has to work. Account data and
history are important, and recently there has
been some discussion about including service
quality information as part of the input.
Billing, charging and cost management
There seem to be as many views of billing as
there are bills. Long time billing employees at
incumbent carriers may recall nostalgically the
paper bill as a monthly touch point, arriving in an
envelope stuffed with promotional materials and
the latest news from the operator. Customers’
“Service providers should
be working continually
to reduce cycle time on
core services, as they
need to master these
areas before moving on to
more complex services in
memories may not be so fond however: Large,
detailed and complex bills comprised charges
borne of complex tariffs, plans, additional
government taxes and fees. Many businesses
and wireless customers were happy with
them. Expecting customers to be accountants
will never be the goal of a customer-centric
organization, yet the inflexibility of legacy
systems has often created just that scenario, as
well as making billing increasingly expensive and
acting as an impediment to the introduction of
new products.
In addition, many customers have
approached new offers with real caution
because they have felt burned by higher than
expected data, messaging and roaming charges
without being made aware of the amounts they
were spending.
While service providers have come a long way
in simplifying tariffs and plans, they still have
a long way to go in many areas. Many still do
not make charges transparent to customers,
or provide them with spending control
mechanisms (for instance, allowing customers
to set their own usage limits) that would ease
customers’ concerns about taking up new
Service providers have been working to
control costs for some time now, especially by
consolidating legacy billing systems. Despite
that work, they have not been as successful in
implementing flexible rating and charging. As a
result, there are two emerging areas of interest:
real-time revenue management and dynamic,
policy-based billing.
Real-time revenue management is often
thought of in conjunction with prepaid wireless
services, but attempts to converge wireless
prepaid and postpaid systems has been an issue
for CSPs for the past decade. Some CSPs have
chosen to transform their OSS/BSS to support
convergent offerings, but others have opted to
leave the two silos alone.
Perhaps the strongest contributor here
in terms of analytics is revenue assurance.
It is generally thought of as a profitability
improvement tool, but it can help indirectly by
making bills more accurate, and consequently
reducing calls to call centers. In fact in many
ways, revenue assurance systems can behave
as operational analytical applications for the
revenue management process.
Analytics can also be used to assess
customers’ reception to pricing plans and billing
and charging operations through the analysis of
interaction logged in the call center.
In many ways, the first five areas we identified
are important to, or even define a brand, which,
in turn, has an impact on these elements.
Certainly it is essential to the overall perception
of the service provider. Brand also can be a
powerful acquisition and retention tool, and last
but not least, service providers can gain from
associating with other brands and products,
such as the likes of Apple or Google (Android)
As mentioned before, two perennially admired
brands are Apple and Apple’s
branding strategy in many ways focuses
on emotion, as its products conjure images
of lifestyle, imagination, liberty regained,
innovation, passion, hopes, dreams and
aspirations. To summarize, Apple’s branding
alludes to the concept of giving power to the
people through technology.
Its image also reflects simplicity not
only in the technology, but also in people’s
lives. For example, the iPod is not just an
attractive media player, but combined with
iTunes, it becomes an ‘in-your-pocket music
and digital media collection.’ Similarly, the
iPhone promotion does not just focus on the
attractiveness of the hardware or software,
but rather on application diversity and
effectiveness through Apple’s ‘there’s-an-appfor-that’ campaign (that is, to boost customer
productivity). The advertising then goes on to
demonstrate just how simple and convenient
(simplicity and convenience) it is to use. also has done an exemplary
job of creating one of the world’s strongest
brands in what could be considered by some to
be record time. The company achieved brand
recognition by realizing what its real business
Amazon has created one of the world’s
strongest brands in record time
is. “We’re not in the book business or the
music business. We’re in the customer service
business,” stated Amazon’s CEO, Chairman and
founder, Jeff Bezos.
Amazon’s highly effective brand positioning
is built on the concept that even though web
shoppers want the ease and convenience
of doing business online, they also want
personalized customer service. Based on this
fundamental insight, goes to
tremendous lengths to make sure the online
shopping experience supports its brand
It also is one of the leaders in developing web
communities, giving its loyal customers a place
to go even when they are not actually shopping
(or at least when they think they’re not).
Amazon’s brand has been updated during the
last few years to augment its ‘World’s most
customer-centric company’ strapline, used since
1997, with the ‘World’s largest selection’, which
focuses on choice and competitive pricing.
Amazon also encourages its partners to deliver
the lowest prices.
While CSPs may not be aiming for the same
brand as Amazon or Apple, they should be
working to develop a brand that is:
ntargeted – appropriate to the market and product set;
nclear – delivering an instantly comprehensible message;
ndesirable: – something customers want to have or be part of;
nunique – standing out in the crowd;
nmeaningful – matching customer expectations;
nconsistent – across all aspects of the company;
nrecognizable – clear, easily identified, repeated;
nactionable – can be leveraged and supported;
nextensible – supporting new products, partners.
customer value. This is typically accomplished
by creating a performance management model
and tracking changes over time to determine
drivers of brand value for a particular company.
It may also extend to comparing performance
against that of competitors to discover new
sources of value and impacting initiatives.
As can be seen from the examples in
each of the six areas influencing customer
experience, the fundamental underpinnings
of delivering excellent customer experience
involves capturing accurate and appropriate
information about the customer from across
the organization. That information needs
to be analyzed then routed to the ‘point of
opportunity’, so that it can be acted upon in the
most effective way.
The importance of analytics in customer
It is easy to see why analytics are among the
hottest topics for service providers looking to
improve their customers’ experience. Analytics
are embedded in almost all of the areas we have
discussed in this section.
While this may seem like a tall order, analytics
tools exist in every phase of the customer
lifecycle to support these goals. These tools
are highly dependent on collecting a variety of
information from far flung systems and even
network elements (for example, the home
subscriber server, the home location register,
SMS and so on) and in some cases require realtime mediation, aggregation, transformation and
analysis of data. Despite this complexity, the
payback in customer loyalty and profitability can
be well worth the difficulty.
Analytics can be used to better understand
the drivers of brand value, monitoring value
creation and its impact on enterprise and
Section 4
Analysis: Service providers discuss present
and future actions and plans for addressing
analytics in customer experience
In order to investigate firsthand how service
providers approach customer experience,
and how they use or plan to use analytical
applications, we conducted in-depth interviews
with senior executives within 20 service
providers around the world.
While the magnitude of different programs
varied, and business and geographic scopes
differed among the service providers, all
had active efforts underway, and all were far
enough along to at least discuss business
drivers, focus areas, program challenges and
critical success factors.
As we discovered in last year’s survey, many
of the programs have historically struggled
to make overall progress with customer
experience, and many service providers
still treat aspects of customer experience
as separate islands of business processes.
Additionally, service providers admitted they
were dealing with many issues tactically rather
than providing a company-wide, planned,
and coordinated program. There is still a fair
amount of independent tactical activity going
on today, but senior management seems to be
embracing the idea of an holistic approach to
customer experience, and in some cases the
seeds of implementation are being sown.
Driven at least partially by difficult economic
conditions, much of today’s approach focuses
on the here and now, taking out cost in specific
functions, incrementally improving support and
service quality, and struggling with a mass of
diverse, incomplete and often inaccurate data
sources. Progress here is further hindered
by the conservative investment environment
brought on by the global economic crisis.
Yet CSPs are cautiously optimistic looking
out over the next two to four years, though not
as optimistic as they were last year. They see a
more holistic approach to customer experience
and analytics, improving data management,
more cross functional focus on solving
customer experience issues, and increasing
agility around customer responsiveness.
As importantly, several respondents
expect to initiate a stronger focus on service
enablement, and to deliver on the promise of
the new services and business models that
are increasingly discussed in the industry
today. Improving their customers’ experiences
can do nothing but help them in this venture,
improving their profitability, and positioning
them as more valuable than ever to current and
potential business partners.
The service providers we interviewed came
from four segments across the world. The
largest segment, representing 45 percent of
Figure 4-1: Respondent profiles – views from across the industry
Converged operators
Wireless operators
Cable operators
Other 20%
Many service providers are struggling to
control costs while managing user experience
our respondents, were convergent suppliers
offering voice, data, wireless, and in some
cases other services. The majority of the
respondents were also in the process of rolling
out some form of video services. Most of
the converged carriers operated primarily in a
single country, though a few had a significant
regional presence.
Wireless mobile companies were the
second most common respondents,
comprising 30 percent of the base. Some of
the wireless mobile operators were multicountry operations. A few owned some fixed
infrastructure, but their fixed revenues were
dwarfed by their wireless operations.
Cable operators made up 20 percent of
the interview base. All but one of the cable
companies operated primarily in a single
country, though one had operations in several
countries, and one was a big wireless player as
Finally, we had one fixed-only service
operator with business just in one country.
The vast majority of those surveyed were
among the top-ranked operators in terms of
market share in the countries they served. In a
few cases, the mobile operators slipped below
this ranking in some countries. In only two
cases was one of the operators not among the
top three.
Drivers for programs: conservatism rules
Drivers for current customer experience
programs strongly reflected the difficult
economic times brought on by the global
economic crisis, and the increasingly mature
state of markets, especially in developed
Given the opportunity to provide their top
three drivers, reducing costs was the highest
priority. The most common target area for
cost reduction was customer support. Service
providers were addressing this in a variety of
different ways, including outsourcing selected
functions, implementation of customer selfservice and contact center consolidation.
This is balanced against the second and third
most popular concerns – reducing churn
and increasing customer satisfaction. While
understandable in a recession, this ranking
tends to confirm the view that many service
providers are struggling to control costs while
managing customers’ experience.
For service providers, the best way to
address the three drivers simultaneously
has been through process changes that
bring about reduced cycle times, and faster
problem resolution (preferably on first contact).
Several service providers have been working
to improve the information and analytical
tools available to their customer service
representatives (CSRs) at the point of contact.
Several have also experimented with next best
action capabilities (see Section 3, page 25 for a
description of next best action).
In addition to systems and infrastructure
changes, service providers also mention better
CSR training and retention programs as a
means for improving customer experience.
Empowering CSRs with the training and
authority to solve customers’ problems without
escalation can be a tremendous satisfier for
customers, as it shortens the resolution time,
and instills more confidence in the service
provider. It can also reduce support costs, but
only if CSPs can retain personnel after training.
Other areas of focus for cost reduction
include improved self-service capabilities,
streamlining and automation of the fulfillment
process, and systems consolidation. Many
believe that self-service holds great promise,
but have not yet seen rapid uptake of the
facilities by their customers.
The next priority is improvement of
marketing campaigns, with 35 percent of
respondents including it in their top three
priorities. While those who were most
enthusiastic about improving sales seemed
to be companies where service markets were
still growing, there were some representing
mature markets as well. Those respondents
remained focused primarily on improvements
in segmentation, offer development and
management, and opportunity management,
with a few including improvements in contact
and activity management.
Figure 4-2: Drivers for current programs – conservatism still rules
service providers feel suppliers need to do a
better job in adapting their software to best
practices, and training service providers on
those best practices. In addition, suppliers
need to improve application configurability for
situations where service providers choose to
deviate from expected processes, or customize
them for competitive advantage.
Many participants in the survey expressed
concerns regarding meeting financial and
schedule goals, and 35 percent of respondents
cited these issues as among the top three
challenges. In many cases, the root of the
problems could be found in combinations
of other issues, such as COTS functionality
or data quality. Program managementrelated issues such as change management,
expectation management, poor requirements
definition and lack of timely problem resolution
also contributed to challenges.
In fact, change management was often
cited as the most complex, if not the most
difficult issue to resolve from a program
management perspective. That was especially
true for service providers that did not deploy
a single program office to manage programs.
cycle time
Challenges in executing customer experience
When asked about their top three challenges in
executing their customer experience programs,
service providers were vocal, and spent some
time sorting out priorities.
The biggest issue overall was the state of
customer data accuracy and usability. This
was also a key finding last year’s Strategic
Transformation for the Digital Economy Insights
Research report (available from the Forum’s
website). Several service providers cited the
difficulty of converting data housed in legacy
systems or network platforms, especially when
streams from multiple legacy platforms with
disparate data were being consolidated into a
single system. The issue was cited most by
the convergent and cable operators, but was
also an issue by some in the mobile segment
as well. This is not surprising, given the stove
pipe nature of legacy OSS and the existence of
critical data in all manner of formats in network
elements and systems.
Following closely was the issue of legacy
integration capabilities: While most felt their
systems worked well in performing their original
duties, legacy systems were cited as being
inflexible and difficult to adapt to new business
issues, as well as their integration with other
systems being very complex – all of which
means time and money to the service provider.
Next came the issue of commercial offthe-shelf products (COTS) functionality. This
applied primarily to more recently implemented
systems. A number of service providers
expressed concern over the lack of clarity
in feature and function definitions, lack of
flexibility in application configuration, lack of
extensibility to new services, and in one case
admitted ambiguity concerning the original
requirements definitions.
With the analysis, it became apparent that
Some 30 percent of respondents included
service management initiatives in their top
three priorities, with much of the current
focus revolving around data collection and
performance analytics.
“Several service providers
cited the difficulty of
converting data housed in
legacy systems or network
platforms, especially
when streams from
multiple legacy platforms
with disparate data were
being consolidated into a
single system.”
Creating a more responsive, personal
and flexible experience is a priority
Figure 4-3: Challenges – creating data driven relationships from legacy infrastructure
Availability of
critical skills
Top management
Prioritization of
Managing change
Achieving financial
COTS functional fit
Data accuracy/
Legacy systems
Understanding and tracking the impact of all
the changes across various aspects of multiple
programs was an exceedingly difficult task. A
lack of a clear conflict resolution or process
across multiple programs – or (in one case)
lack of accountability around reporting changes
that impacted other processes – put pressure
on schedules and program costs. Most
respondents felt formal change management
structures and processes were extremely
important, and a few cited organizational
discipline problems.
Organizational change difficulties were
expressed as a concern by 30 percent of the
respondents. The most common concern was
the acceptance or non-acceptance of change
by the target organization – especially where
consolidation of either systems, processes
and/or organizations were involved. A few
respondents also cited concerns brought on by
changes in a separate entity, as those changes
sometimes have an unforeseen impact on
downstream organizations. While the impact
may have been caused by insufficient planning
or change management processes, it was
seen more as an organizational issue by the
Availability of critical skills was cited as an
issue by one-fifth of respondents. This was
most consistently an issue of technology
management skills within service providers
implementing new technology, such as web
services. A few also brought up issues with
knowledge and skills around best practices
and process definition, or the ability to think
creatively regarding new business practices.
Finally, a number of service providers
commented on the difficulty of determining
where to focus. Given the breadth of the
problem, the complexity inherent in each
of the components, and the state of the
infrastructure, service providers felt they had
gone through years of a process that they
likened those to ‘plugging leaks in a sinking
boat.’ They thought it would be better to
step back, gain a broader understanding of
what should be accomplished, assess their
current state, and begin the planning process.
Respondents also noted that the most
effective planning was driven by the business
organization, rather than by an attempt to
modernize the IT infrastructure with the latest
Future goals for customer experience
The next question posed to service providers
revolved around plans for the future. Most
respondents were comfortable with looking
ahead two to four years.
Service providers were more conservative
in their response this year than last, with
customer retention and cost reduction heading
the list. Creating a more responsive, flexible,
and personalized experience for customers
remains a priority. This will require more agile,
responsive processes and systems, especially
as digital service enablement becomes more
important operationally.
At the heart of these efforts is a push for
‘customer intelligence.’ Service providers
increasingly understand the importance of
accurate, timely, comprehensive data on
customers, better analytics and the role they
Analytical application priorities
We then asked respondents to rank a variety
of analytics categories. We asked for rankings
in terms of overall importance and also current
capability, with 5 being the highest score.
At the top was a tie between contact center
Figure 4-4: Moving to the future – cost control and customer retention lead the way
New customer
Improve service
Improve offer
Improve customer
data quality
Improve marketing
Improve process
Improve customer
Reduce customer
support costs
play in customer satisfaction – especially at the
point of opportunity (that is when the customer
is engaged by an agent, a self-service screen,
an interactive voice response, a text or instant
Another important effort is that around sales
and marketing effectiveness. Most of the
respondents felt that once the global economic
crisis finally eases, there would be considerable
momentum in their companies directed toward
deeper penetration of existing accounts, as well
as new customer acquisition. In addition, there
would be a stronger focus on new products
and offers. To gain maximum benefit from
these initiatives, service providers will need to
improve their sales and marketing effectiveness,
especially in the areas of opportunity
management and campaign management.
Improving service quality remains a priority
for many service providers, and was seen as a
key driver of customer retention and important
to new services’ success.
Finally, new customer acquisition will remain
a priority for many. Particular interest again was
expressed in tools like campaign management
and in some cases improvements in service
quality to attract these customers.
While many of our respondents’ current work
deals with the here and now, such as lowering
costs and cleaning up data drawn from the
labyrinth of legacy systems and network
elements, service providers are planning a
more holistic approach in the longer term. They
will focus on improving data management,
looking to solve customer experience issues
across organizations, and increasing agility and
In the end, improving customers’ experiences
will help service providers improve their
profitability, and position them as more attractive
business partners in the digital value chain.
and customer retention for overall importance.
This reflected the earlier priorities around
cost reduction and retention. Both received
relatively high scores for capabilities, given
basic capabilities CSPs have in place today,
though contact center analytics edged out
retention tools.
Product management and revenue
management followed closely behind with
scores of 4.1, though product management
received a slightly higher score in current
capability. Product management reflected both
the need to assess products for profitability, but
also to plan for a large increase in the number
of products and offers available in the future.
Revenue management reflected the need to
accurately and efficiently capture all revenue to
increase profitability.
Service quality management came next,
tied with opportunity management and offer
management. Interestingly, service quality
management scored somewhat higher with
wireless companies than with converged
operators. Most of the concern with service
quality management (SQM) reflected the need
Service providers seem to lack priorities
and think everything important
to manage performance of rapidly growing data
services. Offer management reflected concerns
about the need to target and personalize,
especially with respect to data services.
Opportunity management concerns mostly
reflected execution issues, and being able to get
the right offer to the customer at the right time.
Sales performance management received the
lowest score in terms of capability, but also in
Interestingly, though there are clear
differences in priorities in other questions, all
of the areas cited here were close together
in importance. This is a worry as it seems to
reflect confusion about investment priorities. In
effect, by having a range between 3.9 and 4.2,
CSPs seem to feel that everything is important.
They will need to make hard decisions around
priorities, as addressing all of these needs
at once will be a daunting task, especially
combined with other challenges.
Barriers to analytics implementation: cost,
complexity and data integration
We asked respondents to name their top three
barriers to analytics implementation. Three
issues led the long list. Tied for first were overall
cost and data integration issues. Respondents
felt that analytics packages were expensive to
buy and implement, though many felt there was
great potential value. Data integration has long
been an issue for analytics, especially among
large incumbents with decades-old legacy
systems and data stores.
A number of respondents cited difficulties
with previous projects as concerns. Many of
these projects did not achieve their goals,
due to data quality issues, lack of business
value, or lack of business participation. Some
respondents felt that this history would be
difficult to overcome, though most felt they had
learned lessons from earlier projects.
Next came employee resistance to change.
Some respondents, especially in older
companies, felt that employees would be
resistant to ‘giving up their old spreadsheets in
favor of fancy new tools’, though they agreed
that the newer tools would drive better cross
“Respondents felt that
analytics packages were
expensive to buy and
implement, though many
felt there was great
potential value. Data
integration has long been
an issue for analytics,
especially among large
incumbents with decadesold legacy systems and
data stores.”
Figure 4-5: Analytical application priorities
5 very high capability
very important 5
Sales performance
Service quality
Contact center
not important
at all
Current capability
0 no capability
Current tools
Skills availability
Inability to
demonstrate value
License costs
Historical project
Data integration
No single supplier
Figure 4-7: Critical success factors – data quality and clear business cases win the day
Single vendor
Rapid development
Ease of use
Vendor support
Strong program
Business IT partnership
Well understood
business problems
commitment issues
Clear business
Critical success factors: data quality and
clear business cases
Finally, we asked respondents for their
thoughts on critical success factors for analytics
deployments. Again we received a long list of
responses, reflecting the complexity perceived.
Not surprising, data quality and relevance lead
the list, showing recognition of the importance
of data to the success of analytics. Also
important is the business case, especially given
the perceived expense of analytic solutions.
Management commitment and well
understood business problems came next,
reflecting the need for cross functional
collaboration and high level focus necessary for
large project success.
Tied for fourth were strong program
management, again reflecting cross functional
efforts and focus, and business IT partnerships,
indicating the need to collaborate to solve
real business problems and fit with business
Finally, some CSPs cited suppliers’ support,
ease of use and rapid development capabilities
as important.
While respondents clearly see value in the
deployment of analytics, business conditions,
complexity and some previous project
shortfalls have created some serious concerns.
In addition, as cited in Figure 4-5 (see
previous page), there is little differentiation
in importance of key functions. CSPs need to
clarify their priorities and focus; not everything
is equally important. Suppliers need to work
closely with CSPs to help them identify and
quantify opportunities and business value,
determine realistic returns and, where
possible, simplify the planning and deployment
Figure 4-6: Barriers to analytics implementation – cost, complexity and data integration
Data quality &
functional understanding. This was reinforced by
20 percent responding that current tools were
The inability of suppliers to demonstrate
business value was cited by a few respondents,
as was lack of skills availability within CSPs.
Departmental data silos were also cited as a
problem by a few.
CSPs cannot afford to excel in every
aspect, they must choose carefully
Section 5
Conclusions and recommendations
Compiling this report involved speaking to
20 service providers and a similar number
of suppliers, as well as perusal of countless
strategy documents, white papers, product
descriptions, and other documents. Overall,
we found that although many analytics
and business intelligence (BI) programs for
customer experience are in the early stages
of implementation, there are some lessons
already learned. As a result, we feel able to
make some important recommendations:
1. Understand the big picture
We believe that virtually every point of
touch between customers and their service
providers or partners contributes to customer
perception, satisfaction, loyalty, and ultimately
to the profitability of the service provider.
Analytics can be used to model, measure or
predict aspects of customer experience. In
addition, there are numerous opportunities
to apply process analytics to improve internal
processes, saving time and money.
Service providers must develop and manage
an enterprise-wide vision of all aspects of
customer interaction, and figure out where
analytics fit best if they are to deliver an
appropriate experience to customers.
An overall, enterprise-wide view is most
certainly how the customer experiences
a provider – that customer interacts with
individual departments within the CSP – and to
understand that experience it must draw data
from all parts of the enterprise.
2. Pick your places
While all service providers perform similar
functions, their customer experience related
strengths, weaknesses, strategies, priorities
and programs may differ significantly. A
successful customer experience strategy
does not mean the CSP has to be world
class at everything, and in fact CSPs probably
cannot afford to excel in every aspect. Rather
CSPs must determine which areas are most
important for them to succeed and where they
can get payback by using analytics.
Properly applied analytics can help to
determine the areas to be targeted, model and
improve the processes impacting those areas,
and model, predict and measure some of the
results of these actions. The concept here is
that the customer experience strategy and the
related analytics deployment strategy must be
tailored and affordable.
One of the concerns we have here is that
there was little differentiation in importance
in a variety of focus areas. CSPs will need to
make tough decisions in addressing issues
with limited budgets if they cannot prioritize.
3. Mix in some small, fast deployments
CSPs are typically big enterprises, and their
systems and projects reflect that; they tend to
be large scale and so take a considerable time
to implement. This is true as well of traditional
analytics and BI projects. They tend to deal
with highly complex problems, require large
and diverse data sets (often of questionable
quality) and often encounter unforeseen
problems. In fact, 35 percent of respondents
named ‘historical project problems’ as a
serious barrier in our survey, and 20 percent
cited ‘inability to demonstrate value’.
By choosing a few small but impactful areas
where analytics can be quickly deployed, CSPs
can rapidly realize benefits and gain some
momentum. Selected real-time analytics may
be a good place to start here, as they tend to
draw from current data, and do not require
the collection and rationalization of mounds of
historical data.
4. Consider a continuous improvement
It makes sense to approach analytics for
customer experience from a continuous
improvement perspective given the scope and
complexity of the industry, the volatility of the
larger digital value chain, the broad scope of
analytics-related opportunities and limitations
on investment capital.
A good starting place might be modeling and
measuring the impact on customer experience
of a particular process, or set of processes,
to make the most effective changes to them.
The next step could then be to apply process
oriented analytics to determine how to make
the process more efficient and decrease cycle
time. From there, a CSP could apply a similar
approach to other key processes. A handful
of our respondents are already pursuing this
5. Manage customer data as a corporate
asset, but…
We encourage CSPs to recognize that
comprehensive data integration is a long term
project. Almost every aspect of customer
experience analytics hinges upon the accuracy
and accessibility of data. Unfortunately, this
data is found in every nook and cranny of
the service provider organization, in every
imaginable format, and at times in conflict with
the similar data from other sources.
Data management programs must, therefore,
address quality issues, and ensure data
accessibility and usability, but they must walk
before they can run.
One approach a few of our respondents
discussed was to focus the most attention on
a handful of critical data elements for initial
improvement. Once they were improved, the
group moved incrementally onto another small
but critical set of elements. This saved time
and money, and demonstrated recognition of
the value to business users – an important
concept for IT organizations.
6. Pay attention to privacy laws
Privacy laws vary greatly between geographies,
and must be respected. A single publicly
disclosed violation of privacy laws can have
a big impact on a company’s brand, and
ultimately its relationship with its customers. It
is not enough to have policies in data privacy;
training of all customer contact employees
is extremely important and analytics project
participants must be trained as well. Finally, all
applications dealing with sensitive data should
be reviewed as to their access and distribution
policies and controls.
7. Gain top management support
Top management sponsorship and approval
is essential because of the scope, crossfunctional nature and complexity of planning
and executing an analytics strategy. It cuts
across the various processes and organizations
that impact customer experience.
For analytics to become pervasive and add
enterprise-wide value, top management must
walk the walk, embracing fact-based decision
making, pushing for more and better data,
and recognizing achievement when efforts
Senior management must also drive priorities
for analytics applications targeting. Customer
experience improvement, after all, is a
business strategy, management needs to direct
which parts of the business need attention
and can benefit the most from analytics. Not
only must top management set the vision,
it must determine affordability, allocate
appropriate resources, ensure cross-functional
coordination, and remove some of the barriers
that will inevitably pop up during the course of
8. Take advantage of frameworks and
Any help with best practices, data
management and domain frameworks will be
useful because of the breadth and complexity
of the problem. For example, TM Forum has
a number of useful artifacts in this space for
service providers and vendors alike – most
TM Forum’s Managing Customer Experience
Collaboration Program addresses many issues
notably its Information Framework (SID), in
addition to the Applications Framework (TAM)
and Business Process Frameworks (eTOM),
all of which are well established elements of
the TM Forum Frameworx Integrated Business
It provides an industry-agreed, service
oriented approach for rationalizing operational
IT, processes, and systems that enables
service providers to reduce their operational
costs and improve business agility.
In addition, TM Forum’s Managing Customer
Experience collaboration program is addressing
a number of relevant issues (see the next
section). Finally, the Forum’s Business Metrics
Development Programs can be used as a
source of business intelligence, and help
dive the deployment of analytical capabilities,
especially in targeting process improvement
(see page 46).
9. Don’t forget your supplier partners
Some 35 percent of respondents expressed
strong concerns about the effectiveness of
commercial off the shelf components in our
survey, yet 35 percent also spoke glowingly
of the partnerships they had forged with their
suppliers and the consequent success they had
Another 20 percent of CSPs said that their
suppliers could not create compelling business
cases for their products, yet 25 percent more
listed suppliers support and participation as
critical to the success of analytics’ deployment.
While the applications and implementation
world is far from perfect, it is clear that some
companies are better than others at engaging
and drawing successful engagements from
their suppliers.
Both CSPs and suppliers struggling in
this regard should do a fresh assessment
of themselves and their expectations,
engagement styles and strategies. Service
providers should also include evaluations of
cultural fit, experience and methodology into
their vendor selection criteria.
10. Develop your people
Good analysts are hard to come by as they
must acquire and master a broad variety of
skills, including quantitative and technical
expertise, business knowledge and process
design abilities, relationship building, and
consulting, and coaching skills to help others.
Analysts are also highly motivated by
challenging and interesting work, allowing
them to hone their talents and gain a sense of
personal progress. It is important for employers
to recognize these requirements and traits,
and to create appropriate growth opportunities
for analysts, if they want to keep them as
We believe that these are the key
recommendations for CSPs looking to improve
their customer experience capabilities through
the use of analytics.
What is important to remember here is that
while the overall effort may seem daunting,
the payback for companies that have made
the commitment and are executing has been
worthwhile. We believe that service providers
that can differentiate themselves with a
superior overall customer experience will be
winners not only in their traditional service
markets, but also as enablers of the digital
value chain, and that analytics will continue
to play an important role as a catalyst for
customer experience and process leadership.
We hope you enjoyed this report and found
it useful.
“We believe that service providers that can differentiate themselves with a superior
overall customer experience will be winners, not only in their traditional service
markets, but also as enablers in the digital value chain…”
Section 6
TM Forum’s contribution to analytics to
improve the customers’ experience
Specific to the telecommunications industry is
that customer experience is the result of the
sum of observations, perceptions, thoughts
and feelings arising from interactions and
relationships (direct and indirect) over an
interval of time between a customer and their
communications service provider (CSP) when
using a service.
Customer experience analytics (CEA) uses
software to identify and analyze customer
behavior patterns within and across multiple
access points. CEA solutions use sophisticated
data modeling techniques to analyze
customers’ experiences with a company.
Customers contact companies for a variety
of reasons (service, sales, feedback) and use
a variety of methods to interact (websites,
phone, kiosks, mobile devices, and so on).
Previous approaches to measuring and
managing customer experience (at the
individual access point, or within a specific
department) have included customer
relationship management (CRM) and customer
experience management (CEM) applications,
which are typically aligned with individual lines
of business.
Customer experiences include not only
interactions through traditional channels, such
as purchases, customer service requests
and call center communications but also,
increasingly, through social CRM channels
such as Twitter and Facebook. To manage
the customer experience, companies need
to create a strategy that encompasses
all customers’ touch points across the
CEM is the collection of processes a
company uses to track, oversee and organize
every interaction between a customer and the
organization throughout the customer lifecycle.
The goal of CEM is to optimize interactions
from the customer’s perspective and, as a
result, foster customer loyalty.
Until quite recently, the means for
determining the level of customer experience
was limited to analyzing historic data, usually
collected and stored in data warehouses,
tracking customer interactions with customer
support representatives, market research,
uptake of marketing offers, call records, data
usage and churn analysis. Historic analysis, by
its very nature, yields relatively old information,
by which time the situation, if poor, is very
difficult and somtimes all but impossible to
retrieve – for instance, if a customer has
left in search of a better experience with a
Today’s CSPs need to monitor customer
experience in real-time and be able to address
issues as they happen. In many cases,
proactive customer management is used to
foresee and address potential issues before
they affect the customer. Being able to monitor
the customer’s experience in real-time came
about after the introduction of sophisticated
network and system management tools, used
primarily for the purpose of problem alarms,
revenue assurance and fraud monitoring. The
combination of all three was quickly recognized
as the basis of an effective CEM system.
However, CEM’s goals are best achieved
where CSPs have undertaken some form of
transformation exercise, moving towards an allIP, real-time network supported by interactive
business support systems (BSS). The very
nature of being able to address and interrogate
or monitor all network elements, BSS or
Operational Support Systems (OSS), is core to
effective and proactive CEM.
Monitoring the elements of customer
experience is not the whole story, of course.
The analysis of the data collected and the
Monitoring the elements of customer
experience is not the whole story
actions taken to ensure an optimum customer
experience has become a critical component.
Providing this information in real-time also
allows the CSP to monetize the results by
profiling customers for the purpose of target
marketing or advertising, both for themselves
and on behalf of third parties.
Knowing and understanding the customer
is considered key to any successful CSP
operation and TM Forum is addressing how
CSPs can best capitalize on CEM utilizing a
combination of its own Initiatives, They include:
n Frameworx – the only integrated business
architecture which provides an industry-agreed,
service oriented approach for rationalizing
operational IT, processes, and systems that enables
service providers to significantly reduce their
operational costs and improve business agility;
n research and publications;
n Catalyst Projects; and
n white papers.
The highlights are summarized here.
Managing Customer Experience Program1
Competitive differentiation for advanced and
converged services will rely on more than
traditional service performance targets. Roll-ups
of metrics related to networks, applications,
and IT infrastructure are no longer enough
for matching service quality to customer
expectations. To truly manage the customer
experience, CSPs have to build end-to-end
views of not only the customer and services
consumed, but also of the preferences,
behaviors, personas and social network
affiliations that define the customer.
By understanding what defines their
customers, CSPs have a better chance of
meeting not only present day, but also future
expectations in a proactive manner. With an
emphasis on management of the pre-custom,
pre-service aspects of the customer/provider
relationship, service providers can work toward
building loyalty among their customers.
Loyalty comes from understanding the
customer experience from even before the first
contact with the CSP, all the way through to the
point where a customer either recommends the
service to another person, does not recommend
the service or churns to another CSP.
At the same time as building an understanding
of that complete lifecycle, CSPs must also grasp
their growing value chains, which tend to hide
or distort their visibility of processes, people
and operations supporting new generation
services. To assure services and better manage
customers’ perceptions, service providers
have to monitor complicated service level
agreements (SLAs), cooperative partnerships,
revenue settlements and rebates, and different
types of conflict resolutions. These are
sometimes radically different to those they are
accustomed to.
To address both the end-to-end view of the
customer lifecycle and of the value chain, TM
Forum’s Managing the Customer Experience
Program takes a phased approach to:
nusing analytics for measuring and managing service quality;
ndefining key service quality metrics at each point along the service delivery network;
nidentifying service quality issues and the
necessary accounting and rebating
information, usage information, and problem
resolution information;
ndefining management capabilities to support each step in the service delivery network;
nspecifying appropriate interfaces and
application program interfaces (APIs) to
enable the interchange of such information
electronically between the various providers in
a service value network.
CEM Analytics and Frameworx2
Although the newly formed Data Analytics Team3
has barely had time to ascertain exactly how and
where CEA fits, it has, through its CEM Control
Center Catalyst4 (see page 46) identified areas
of the Business Process Framework (eTOM)5
that impact customer experience and need to be
included in its analytics exercise.
The team analyzed both data and process
information to understand the Catalyst
Project’s contribution to the Business Process
Framework which impacts both the Operations,
Fulfillment, Assurance, Fulfillment and Billing
and Revenue Management (OFAB) and Strategy
Infrastructure and Product (SIP) verticals in
Figures 6-1 (right down to Level 4, highlighted in
grey) and 6-2 (where they are also highlighted in
the grey).
Their work highlighted the missing links within
the Business Process Framework between
CEM processes for:
Figure 6-1: The operations, fulfillment, assurance and billing/revenue management processes impacted
nplanning processes (mainly in the strategy/
product area);
ncustomer interaction operational processes;
nservice assurance and service quality processes
Figure 6-2: The strategy, infrastructure and project processes impacted
In addition this project is the first time that
the Business Process Framework processes
have provided direct feedback to the strategy
processes (such as for executives) and linked
operative processes through simulations and
predictive analytics to the SIP area.
Operations support
& readiness
Customer relationship
Billing & revenue
Customer interface management
Bill payments & receivables mgt.
support &
Qas / sla
Bill invoice
Bill inquiry
billing events
Retention & loyalty
Service management &
support &
& activation
guiding &
mediation &
Resource management &
support &
Resource Data Collection & Distribution
Supplier/partner relationship
support &
S/P problem
reporting &
S/P requisition
S/P performance
S/p settlements
& payments
Supplier/partner interface management
Strategy, infrastructure & product
Strategy & commit
Infrastructure lifecycle management
Product lifecycle management
Marketing & offer management
strategy &
Product & other
business planning
& commitment
Product & other
portfolio strategy,
policy & planning
Product & other
portfolio capability
& retirement
& promotion
Sales &
Product marketing
& customer performance assessment
Service &
capability delivery
development &
Resource &
operations capability
Supply chain
Supply chain
development &
charge management
Supply chain
Service development & management
strategy &
planning &
Resource development & management
Resource &
strategy & policy
Resource &
technology plan &
Supply chain development & management
Supply chain
strategy & policy
Supply chain
& commitment
The Data Analytics Team has its
own TM Forum Online Community
In the enterprise area, enterprise planning and
revenue assurance management gain significant
advantages through operative analytics and
predictive processes. The scenarios shown in
Figure 6-3 provide a transparent picture of how
revenue assurance processes can be affected.
Specific process benefits include:
nPredictive analytics provides considerable
advantages to the communications service
providers (CSPs) and are not only support
nThe viability and existence of mature tools
emphasizes the importance of the decision
and predictive processes within the service
providers CEM processes highlighted in the
Business Process Framework processes; and
nThe Catalyst Project provides motivation for
further investigation of the position of decision
analytics processes within the Business
Process Framework – not only for product
management related processes.
Data Analytics Team6
The Data Analytics Team was originally formed
under the Revenue Management Market
Support Center7 as the Decision Analytics
special interest group (SIG). The Data Analytics
Team has generated so much interest that it has
split out into its own Online Community on the
Forum’s website. Its primary charter is to help
service providers make the best use of business
intelligence (BI) and analytics tools.
This team is focusing on bridging the gap
between ‘raw’ BI technology and the specific
business needs of a CSP, as well as pre-defining
how to use BI in the CSP environment including:
nmanaging CSPs’ business processes;
ncollecting, analyzing and presenting CSPs’
data such as orders, data records (xDRs),
tickets, and so on;
nkey performance indicators (KPIs) for CSPs to achieve operational excellence;
ntaking BI down to the next level within a CSP, using day to day operational tools;
nusing analytics with reference to TM Forum
Frameworx to provide better customer experience.
Figure 6-3: The enterprise processes impacted
Strategic &
ITIL release
& deployment
Manage revenue
assurance policy
Manage revenue
Support revenue
The team is actively seeking more direct CSP
participation in identifying and prioritizing critical
problem areas for the team’s next round of
TR149 The Holistic End-to-end Customer
Experience Report
The Holistic End-to-end Customer Experience
Report8 describes how customer experience
and Service Quality Management (SQM) has
evolved to meet the need for assuring end-toend quality across the customers’ experience
when services are delivered through value
chains of cooperating providers. It supports
business scenarios and requirements described
in TR148 Managing the Quality of Customer
Experience (see page 42).
It was designed to supplement TM Forum
Frameworx where consistent design principles
have been applied across areas that have been
designed independently without an end-to-end
customer experience viewpoint.
The Report models what customer experience
is, the customer and user needs that must
be satisfied to provide good and improved
customer satisfaction, and is based on recent
industry research and standards.
It highlights the importance of understanding
customer and user relationships, and the group
memberships in which they participate, to
deliver better customer and user satisfaction.
The Report also describes a technique called
Key Factor Analysis Methodology (KFAM) for
systematically relating technical performance
measurements to customers’ needs, and hence
customer experience, as well as product and
service features, and SLAs offered by providers.
This technique’s strength is that it can be used
by TM Forum members to track changes in
these dependencies over time, and across
market segments.
The End-to-End (e2e) Holistic Customer
Experience (CE) is an ecosystem of six APIs,
and a set of application areas that need to be
designed and specified as a set, to enable
measurement and improvements in customer
experience across a value chain.
The e2e Holistic CE view essentially
identifies a set of Application Framework
(TAM) applications and interfaces that must
be delivered as a consistent set with common
information models and e2e holistic customer
experience metrics. It is an end-to-end design of
a subset of the Application Framework.
The e2e Holistic CE metrics are based on
meeting the priority requirements in TR148 and
extrapolated from the results available from
the TM Forum Business Metrics Development
Program (see page 46), the SLA Management
team and the TM Forum Interface Program
(TIP). A specific requirement for metrics used in
a value chain is that they meet a benchmarking
standard; that means both the measurement
tools and the organizations are calibrated against
the standard.
In a sense some aspects of these applications
are nothing new, but these developments are:
n some of the business services supported by these APIs;
n the integration of knowledge in CEM systems;
n new optimization features of these applications;
n the relationships between them;
n the notions of virtualized service and resource management;
n the requirements for e2e Holistic CE metrics measurement methods.
By applying these new capabilities it is
possible to:
n track customer experience;
n predict trends;
n proactively modify and optimize product offers
made to customer segments;
n trouble shoot service problems;
n build an improved level of customer satisfaction and loyalty.
TR148 Managing the Quality of Customer
Improving customer experience though
customer-centric methods and analytics is an
important issue for the digital media services
industry as it introduces innovative products,
while at the same time striving to increase
average revenue per user and increase
customer loyalty.
The TM Forum report, TR148 Managing the
Quality of Customer Experience, examines the
factors that influence customer experience
and a number of business scenarios for the
delivery of digital media services such as IPTV,
mobile TV, enterprise IP virtual private networks
and smartphones – all of which are delivered
through a value chain of cooperating providers.
The objective of the scenarios presented
is to examine a range of possible delivery
mechanisms from the perspective of
managing end-to-end service quality across the
cooperating partners; and to work out what
industry standards are required to deliver and
assure high quality service to end customers
and other users.
The main challenge is to establish the impact
a customer experience-centric view has on:
nmeasuring customer satisfaction;
ndiscovering where CE/SQM measurements are needed in the value chain;
nestablishing what CE/SQM metrics and measurements are needed.
CSPs need to be able to monitor and manage
the experience and satisfaction of customers
and users at an individual level and an
Providing a comprehensive ‘navigational’
infrastructure for the product manager
aggregate level, measured over a range of time
intervals. These metrics are needed to support
monitoring, trouble shooting (individual problem
identification, and resolution) and the reporting
processes of a service provider.
The objective is to provide pragmatic solutions
and a roadmap that can be evolved from what
we consider the state of the art now towards
a fully customer-centric, end-to-end service
management solution for the industry.
This report was developed as part of the
e2e Service Quality Management Program. It
sets out, in broad terms, the requirements for
improving customer experience for services
across a value chain, the challenges of drawing
up SLAs and assuring e2e service quality from
a customer-centric viewpoint. Most of all, the
report identifies what needs to be added to
the established and important disciplines of
resource and network-based measures, which
come under the general heading of SQM.
It outlines what customer experience is,
the customers’ and users’ needs that must
be satisfied to provide good and improving
customer satisfaction, and is based on recent
industry research and standards.
CEM Control Center Catalyst10
The CEM Control Center Catalyst Project was
launched by TM Forum’s Data Analytics Team
at Management World 2010. It demonstrated a
new approach to product management, where
operational monitoring, data management
and processing, decision engineering and
design approaches are used to provide a
comprehensive ‘navigational’ infrastructure for
the product manager.
It showed how, by using this approach,
the product manager could simultaneously
balance cost, revenue, and investments that
benefit customer experience KPIs to maximize
outcomes of interest. It also illustrated how
operational monitoring could be used to
manage repairs to the rollout process, as well
as reconsidering decisions based on changes to
key assumptions.
In a typical telecom environment, data to
support systematic decision-making can feel
Figure 6-4: CEM control center overview
Implement product
analyze product
monitor individual
Adjust product base line
like too much or too little. On the one hand,
the amount of information available, when
combined with the expertise of strategic
planners with different backgrounds and
experience, can be overwhelming.
When launching new products into new
markets, where the past is an unreliable guide
to the future, data that provides guidance for
critical decision-making elements (such as
pricing demand functions, demand for the
product in particular geographies, or the cost of
OSS/BSS implementation), may be missing or
This is particularly true of customer
experience-related information, as there
is little industry expertise reflecting how
customers’ overall impression of a CSP and
its brand is shaped by touch points (such as
the ordering process or sales experience), or
how that affects brand reputation, which could
in turn, impact customer behavior, such as
the willingness to pay a higher price. Product
managers make decisions that take brand into
account, however, so their expertise is indeed
there, but is not captured systematically, or
available for continuous improvement.
This Catalyst demonstrated three approaches
to effective product management in this
environment, as illustrated in Figure 6-4 above.
First, it shows how customer experience data
can be effectively gathered, summarized, and
made available in drill-down detail to product
managers as input to effective decision
Next, it showed how this information could
be used for product management decisions,
using a decision engineering and modeling
approach. The model provides decision makers
with holistic, forward-looking information about
how investments in customer experience
will achieve the operators’ goals surrounding
margins and brand. In addition, it provides a
comprehensive set of KPIs and associated
thresholds that indicate a potential issue.
Then, it demonstrated how, following the
product launch, operations can be carefully
monitored in an operations center so that
problems can be readily detected and repaired.
Rollout problems can be fixed tactically, and
addressed through a case management system.
In addition, the early awareness of incorrect
assumptions allows for course corrections in a
product launch to be rapidly and systematically
reviewed and implemented.
Dashboards provided a mechanism to help
the project manager understand the experience
of a large group of customers. As part of the
Catalyst demonstration, they contained data
from about a half million customers. Each
one was represented by metrics gained from
a number of customer experience KPIs; all
customers are ranked based on their overall
customer experience.
The Catalyst demonstrated an innovative
approach to gathering and adjusting the
mechanism for measuring customer experience,
so that it provided valuable information for
product managers. The metric used both soft
and technical quality measures, along with a
proprietary weighting scheme for determining
their values. Importantly, feedback from
customers was used to adjust the weighting
scheme, which meant that the customer
experience metric improved over time, providing
a better and better reflection of various
customer groups’ experiences.
The Catalyst included a collection of data
from many customer touch points including
BSS, OSS, and the network equipment itself.
The team augmented this ‘raw’ data by
calculating aggregated customer experience
metrics, including the Customer Experience
Index (the weighted sum of technical and soft
measurements on an individual customer basis),
customer lifetime value (the revenue expected
from this customer), customers’ propensity to
churn, and others.
This means:
nThe CSP maximizes the benefits of customer
experience investments to achieve goals
involving revenue, costs, and customer
nDepartments responsible for decision-making
and operational monitoring are aligned in a
systematic way through KPIs that represent
key decision assumptions;
nThe CSP manages complexity by visualizing
the interactions between tangible and
intangible factors such as revenues, brand,
investments, and decision outcomes by
simulating existing business parameters and
nThe CSP uses a systematic approach to agile
strategic and operational management, where
the need to reconsider a decision is triggered
by changes in operational KPIs;
nFeedback from customers and information
gathered during a product launch is used to
continually improve the product management
process. The Catalyst showed how a
Customer Experience Intelligence measure
can be improved in this way. Furthermore,
the decision model created a structure within
which new data (both in the form of external
values as well as functional relationships)
could be gathered during operations.
nBrand is an intangible asset that is difficult
to measure and manage, but is
systematically incorporated into the decisionmaking process. Brand here is representative
of a class of intangibles (which include
morale, attitude, acceptance, and net
promotion as additional examples) that can
be managed in the way shown within the
CEM Control Center.
The metric was constantly refined better
to reflect customers’ actual experience
In an increasingly competitive environment,
service providers are seeking new points of
differentiation. There is also strong evidence
to suggest that an investment in customer
experience can provide significant benefits,
even if the initial costs are higher. This is
because customer experience improvements
impact the CSP’s brand, which changes the
demand for the product and increases customer
However, in today’s economic climate,
investment dollars are limited, even those for
improving customer experience. For this reason,
there is an opportunity for service providers
to benefit from more systematic product
By harnessing and analyzing the data already
present in a CPS’s various systems – including
BSS, OSS and the network – the CSP can gain
meaningful insights into its customer base.
These insights can enable a CSP to provide a
more personal customer experience, tailored to
particular customer communities.
Two examples of this are more personalized
product offerings and more personalized
interactions at the various touch points. The
key is to obtain data from the various sources,
including the expertise of product managers,
and to harmonize it into a consistent model. The
CEM Control Center demonstrated that this can
be done.
All together, the CEM Control Center showed
how a CSP can improve strategic and tactical
planning through:
n using improved information about the present;
network that reflects customer experience in a
way that can be improved over time. Ultimately,
these techniques enable service providers to
better manage the inevitable risk, uncertainty,
and complexity involved in every product
launch. This improved risk mitigation enables
CSPs to be more aggressive, gaining a stronger
competitive foothold in today’s rapidly changing
Business Metrics Development Program
The business metrics that have been developed
within the TM Forum’s Business Metrics
Development Program represent areas of
business operation that are important in
assessing business performance, customer
satisfaction and loyalty, and efficiency.
To provide an holistic, business-oriented
benchmarking facility, the business metrics
scaffold is based on a balanced scorecard
approach. To this end, three major domains have
been defined:
nRevenue and margin: providing a view of fiscal performance;
nCustomer experience: providing a view of
the measures that impact the end-customer’s
reaction to the service offering, which also
drives loyalty;
nOperational efficiency: providing a view of cost and expense drivers.
Figure 6-5: Structure of the business metrics – domains
n more intelligent analysis to predict the future;
n changing direction more effectively by
communicating the decision-making rationale
to stakeholders through visual tools.
This allows the operator to identify issues
within a timeframe to take corrective action if
The Catalyst also showed how new
technologies for high performance, high volume
data extraction, storage, and analysis can be
used to reap valuable information from the
Revenue & margin
Under each of these domains, a set of topics
has been defined to drive the development
of specific metrics. The following illustration
presents the topics under each domain and
is followed by an explanation of the different
The Customer Experience Domain covers:
nPreferred access: what are the channels and touch points available to customers, such as actual person, web and store?
nCustomer time spent: amount of time spent
on process or activity that impacts the customer, such as the length of time system could not be used, as opposed to fault repair time;
nUsability: how easy it is to set up, usefulness of documentation, and so on;
nReliability of interaction: this includes
the consistency and accuracy of information
provided by the CSP and also relates to the
credibility of the CSP;
nAvailability of purchased service, including
bearer service and content;
nSecurity: (future work item);
nPricing flexibility: preferred pricing mode available, such as prepaid card, flat rate, by usage (future work item).
Figure 6-6: Structure of the business metrics – topics
1. Preferred access
2. Customer time spent
3. Usability
4. Accuracy
5. Contact availability
6. Security
7. Pricing flexibility
1. Margin/revenue
2. OpEx/CapEx
3. OpEx/Revenue
Revenue & margin
1. Unit cost
2. Time
3. Rework
4. Simplicity
5. Process flexibility
& automation
6. Utilization
is to ensure those customers enjoy an optimal
experience when dealing with the provider to
retain their custom.
Software suppliers and systems intergrators
are also seeing this as a major growth area.
They are mobilizing efforts to provide systems
that can provide effective and timely reports on
customer experience and tie them to systems
that provide alarms, remedies and focused
responses, which are geared to provide the
ultimate customer experience.
TM Forum, through its programs and
extensive base of CSP and supplier members,
will continue to provide relevant information
and guidance on the latest developments via its
Online Collaboration Communities, programs,
research and publications.
The GB935 Business Benchmarking Metrics
Scaffold11 provides a more detailed overview
of TM Forum benchmarking activities around
custmer experience. Additional information
concerning each metric and its corresponding
benchmarking data is available by contacting
the Business Metrics Development team at
[email protected] and/or subscribing to
its reports and services.
There is a strong movement by CSPs to
use sophisticated analytics, increasingly in
real-time, to provide current and relevant
information about their customers’ experience
benchmarking. It is a sign of a CSP’s maturity
that after the ‘subscriber grab’ slows down,
they start to focus on how to determine which
are the most viable customers. The next step
“TM Forum, through its programs and
extensive base of CSP and supplier
members, will continue to provide
relevant information and guidance on
the latest developments via its Online
Collaboration Communities, programs,
research and publications.”
Customer insights:
Building relationships that stick
Developing more insight into customers is the key to keeping
them happy and building loyalty. Nokia Siemens Networks can
provide end-to-end solutions that reveal what service providers’
customers really want, and can even predict what they’re going
to do next.
Keeping track of what makes subscribers
happy – or not – is crucial for any
successful communications service
provider (CSP). According to a study by
Bain & Company, a five percent increase
in customer retention can boost a CSP’s
profitability by 75 percent. It’s a lesson
that CSPs around the world are taking to
heart, and they’re going to great lengths
to build “stickier” relationships with their
Any strategy for delivering a better
customer experience is underpinned by
understanding what customers want. It
relies on pulling together data to build
a coherent picture of each subscriber.
Traditional customer surveys and feedback
are helpful as far as they go, but real
customer insight is about much more than
that. It involves breaking down the barriers
within the CSP’s organization to bring
together real-time data about charging,
subscriptions, devices, service usage,
online behavior and how customers
perceive the experience. These disparate
snapshots come together to form a profile
or “digital identity” for each customer.
CSPs can then use this insight to identify
priorities for creating real value, perhaps
through more innovative services, more
focused marketing, or by providing
improved customer care.
Achieving these aims depends on
having the systems in place to act on
these insights automatically in realtime or near real-time across the CSP´s
organization and processes. It’s this ability
to take targeted action at the right time
that ultimately boosts the business.
Building a customer-centric network
Nokia Siemens Networks has the endto-end capability to ensure that CSPs
can address any aspect of the customer
experience, from device management and
identity management to churn prediction,
from mobile broadband strategy and
quality optimization to automated
customer care.
This capability helps CSPs to create
a “customer-centric network” by
collating and analyzing real-time data
and information from devices, networks
and IT systems. It includes inputs about
subjective perceptions, as well as data
related to services, subscriptions, devices,
charging, billing and CRM systems. This
will provide a unified view of individual
customer needs and enable CSPs to
take timely action to link their customer
insights to business and operational
processes, using automated solutions
to boost speed and efficiency wherever
Nokia Siemens Networks is unique in
delivering a real-time response to what’s
happening in the network and supporting
systems. Our solutions go far beyond
simply consolidating subscriber data and
aggregating and warehousing a CSP’s
customer data. This is incredibly important
in a market where CSPs must tailor their
offers specifically to each customer.
The most obvious examples are timeand location-specific offers. People are
much more responsive to offers if they’re
timely and relevant to their situation at
a given moment. For example, if a CSP
offers music fans the chance to buy a
bundle of MMS messages as they enter
Customer care automation solution solves more than
50% of technical problems in a few seconds
From subscriber data to customer value.
From customer value to business results.
time actions to
boost business
Services &
& sales
planning &
Operation &
Business intelligence applications
where the
real value is
Reports, dashboards, analysis & query, segmentation, profiling
Real-time & historical data collection, consolidation and exposure
One view of
a customer
Basic analytics including metadata
Orange Switzerland needed to improve
customer satisfaction by reducing
complaint handling time, while
decreasing the amount of complaints
escalated to customer care technical
Nokia Siemens Networks provided Orange
with Customer Care Automation solution.
Real-time & long term data storage and consolidation
Billing, charging, subscription, service usage, device, CRM,...
Customer data
coming from
many sources
Spread out
a concert venue, they’re likely to take up
the offer because they’ll want to take
pictures of the concert and send them to
their friends. It’s not possible to achieve
this if it takes 72 hours for a report from
a static database query to reach the CSP
system that sends the promotion to the
With instant feedback, a CSP can
run many such highly targeted microcampaigns. Nokia Siemens Networks
worked with one CSP to help it generate
an extra €25 million a year by increasing
its campaigns from four every three
months to 15 per week.
As the communications industry
evolves to offer a wider range of
innovative and life-enhancing services,
those CSPs that put a solid customercentric network in place will be bestplaced to maintain a competitive edge by
offering subscribers relevant and tailored
services and deals.
Many businesses discover that
achieving that ideal means changing
their existing culture. For example, one
European CSP found that data was held
throughout its organization in more than
200 legacy systems, leaving it unable
to track even the most basic network
activities. Nokia Siemens Networks
Smart device support calls last an average
of 45 minutes – 3x longer than calls to
customer care about feature phones1 and
these will be 43% of devices in 20132.
“We can solve technical problems
during the first call in 50% of the cases.
Response is available on average in
20 seconds.”
– European CSP
SMS complaints escalated to customer
care technical support decreased by 30%,
and to IT operations decreased by 60%.
1 Source:, April 2010
2 Informa
delivered a solution that combined
consulting services and technologies to
pull that information together and improve
every area of the CSP’s operation.
The CSP can now spot network
problems 20 times faster and resolve
them in one third of the time, leading
to savings of €1.4 million in 2010. In
marketing, fewer provisioning problems
led to better service uptake and boosted
revenue by €5 million in 2010. Increased
network availability is helping operations
to secure revenue of €500,000, which
might otherwise be lost.
Automating customer care
Care has always been an important
contact point between CSPs and
subscribers, with the quality of care
services having a huge influence on
how providers are perceived. The rise
of smart devices is making it even more
of an issue, however. Smart phones are
expected to account for 43 percent of
mobile devices by 2013, and it has been
estimated that smart device support calls
last an average of 45 minutes, which is
three times longer than calls to customer
care about feature phones (Source:, April 2010).
Additionally, the top smart device
issues at call centers relate to email
configuration, lost phones and Internet
settings. While an automated care system
may not be able to do much about nontechnical issues such as lost devices,
other than wiping and locking the device,
it can speed up the handling of technical
complaints and requests, which account
for around 15 to 20 percent of the total.
These specific issues are also the most
costly to resolve for the care organization.
For example, more effective service
provisioning should enable customers
to set up and access services without
consulting their CSP’s care team. There
will always be some issues that can’t be
sorted out by users and in these cases it’s
great to have friendly and helpful support
staff. However, that’s not as important
as giving those helpline personnel the
tools to solve problems quickly and
effectively. The aim is to solve as many
queries as possible during the initial call
and to minimize the number of cases that
need to be passed up the line to technical
support staff. Nokia Siemens Networks
solutions link operational and business
support processes directly with real-time
insights to generate real-time automated
actions that resolve problems fast.
Our customer care automation solution
Customer insights:
Building relationships that stick
Winners in the business service innovations category
of the 2010 Global Telecoms Business Innovation Awards!
CSPs have a unique opportunity
Service Provider
Insight, Identity
& Privacy
Telco 2.0
Internet Service
Trusted Identity
Trusted Privacy
Trusted Insight
Connectivity and network control, individual relationships, real-time monitoring and charging
enable CSPs to take the responsibility for protecting customers’ personal information.
offers genuine, one-click problem
resolution. The system works “behind the
scenes” to correlate technical data from
across the CSP’s systems and deliver a
firm diagnosis and solution to the problem
via a simple, one-screen interface.
For one European CSP, customer care
automation has helped to increase its
first call fix ratio by finding the problem
in 50 percent of cases, on average within
20 seconds. Furthermore, the ticket
handling time by customer care technical
support has decreased by 50 percent.
The number of complaints passed on
to technical support also dropped by 30
percent, and the complaints escalated up
to IT operations dropped by 60 percent.
Five steps to better mobile broadband
Another big area where customer
insights deliver all-round benefits is
mobile broadband. Two years ago,
when fewer people were using
mobile broadband, most of those in
mature markets weren’t worried about
network quality. Fast forward a few
months and the rapid uptake of mobile
broadband has created a bulk of users
who have expressed higher levels of
dissatisfaction with the service they
get from the network. (Source: Nokia
Siemens Networks Acquisition and
“Service differentiation allows us to attract more subscribers
while reducing churn. With the Identity Management project,
we are sure of ushering in a new level of end-user experience.
This is a big win for us and encourages us to offer many more
innovative platforms in future.”
– Diego Scalise, Value added service manager & senior
architect, Movistar Argentina
Movistar Argentina and Nokia Siemens
Networks were recognized for using the
Identity Management solution to link
subscribers’ multiple online identities
and multiple Web sites such as Flickr or
Facebook with their mobile phone
avoiding separate sign-on procedures.
This simplifies the end-user experience
whilst obviating identity theft and allows
Telefónica to provide a range of
personalized services.
Retention Study 2010).
Nokia Siemens Networks offers
end-to-end quality of service (QoS)
differentiation to help CSPs target highly
segmented groups of users with the
satisfying products and offers that meet
their specific needs. The simplest way to
visualize our approach is as a continuous
cycle of improvement in five steps.
Step one is about mapping how users
consume mobile broadband. What are
their favorite applications? What size
are the files they download and upload
and what volume does that add up
to over the month? When do they go
online and which devices do they use to
gain access? All this information should
be easily accessible to the product
managers designing services for the CSP.
For example, one European CSP found
that just 5 percent of its customers were
generating between 80 and 90 percent of
network traffic, leading to congestion and
dissatisfaction among high-value users.
It’s a problem that CSPs ignore at their
peril. The latest acquisition and retention
study from Nokia Siemens Networks
found that average churn in mature
markets may be stable, but churn is on
the rise among smart phone users and
other high-value users, so CSPs must
find ways of satisfying them better. The
answer is to identify the different user
segments and make differentiated offers
to each group.
That leads us to step two, in which
product managers – often with the help
of Nokia Siemens Networks consultants
– devise differentiated offers based
on QoS, volume thresholds, price or
bundling with devices. The key is to
identify the different user groups and
target them with different packages. For
example, business users will typically
be looking for high QoS but won’t be
worried about price, while teenagers
are looking to achieve access on a
tight budget. Between these “quality
sensitive” and “price sensitive”
extremes are groups such as the “price
elastic”, who are the most likely to
increase their service usage in response
to attractive off-peak offers, and
“influential” users, who it’s important
to keep on-board since they influence
their peers by being very active on social
networking sites.
Step three is about CSPs delivering on
their promises by implementing the right
policy controls in their network servers.
Enforcement is step four, and requires
the right tools to check that users aren’t
exceeding their volume thresholds, for
The final step is monitoring the
customer experience. Are they enjoying
the levels of service that they’re paying
for to the full, or might they perceive
that they’re getting poor value because
they’re not using all the applications
they’re entitled to or falling well short of
their download thresholds, for instance?
The Nokia Siemens Networks portfolio
encompasses the whole circle.
Predicting and preventing churn
CSPs have been moving away from
looking only at historical data and
towards using real-time information to
deliver real-time benefits. Some have
been going even further, however, with
companies including Vodafone and
Singtel using data to accurately predict
customer behavior. It’s an approach
that’s proving especially useful in
spotting those subscribers who are
most likely to churn. The technique is
called social analytics.
Nokia Siemens Networks uses
market-leading social analytics
products to combine usage behavior,
demographics and social networking
information to predict churn and the
likely responsiveness to offers. One
European CSP used this approach
to boost the accuracy of its churn
predictions by 70 percent within its top
10 percent of customers.
Social analytics also enables CSPs to
predict how well people will respond
to different offers and make sure that
customers are offered only the most
relevant promotions, thus giving them
a better experience by being less
intrusive. Of course, social analytics
algorithms are only effective if they
have access to good underlying data,
so they rely on subscribers giving their
consent for CSPs to use their personal
information in this way.
A question of trust
CSPs aren’t the only organizations that
can make a strong business case for
finding out more about their subscribers.
Web 2.0 companies such as Google,
Flickr and Facebook all track customer
behavior, while organizations as diverse
as airlines to banks could benefit from
getting to know their customers a little
better. On the other hand, customers
are aware that privacy can be an issue
and they want to retain control of their
Still, research shows that the greater
the perceived benefit of sharing, the
higher the proportion of people who
are willing to share (Source: Nokia
Siemens Networks Privacy Study 2009,
Psychonomics). Better still, the same
survey shows that consumers trust
CSPs to take care of their data. Only
banks score more highly. No one else,
including ISPs, insurance companies and
even governments, are trusted to the
same degree.
This puts CSPs in a strong position
to become a trusted partner for their
customers, gaining permission to use
their data to improve the customer
experience and service offering while
identifying new business models and
revenue streams. CSPs can act as data
brokers between customers and Web
2.0 providers, sharing information such
as location, presence, reachability and
device capabilities with third parties
in a controlled way. They can provide
users with an online identity that
enables single sign-on for the Web,
freeing people from the growing list of
passwords and security questions.
For example, Nokia Siemens
Networks and Movistar Argentina,
a subsidiary of the Telefónica group,
recently won a Global Telecoms
Business (GTB) award for Business
Services Innovation. The award
recognizes an Identity Management
(IDM) solution that links Movistar
subscribers’ multiple online identities
and multiple websites, such as Flickr or
Facebook, with their mobile phone. This
means they no longer need to sign on
separately for each service.
Customer experience transformation
Nokia Siemens Networks’ unique
combination of capabilities can maximize
opportunities for CSPs. That’s the
secret behind more than 120 customer
insight-based service improvement
projects that we have already delivered
successfully worldwide.
We know what data is available and
how to deliver it to the right place fast in
order to derive the maximum business
value. We recognize that customer data
is a valuable yet sensitive asset. Our
know-how and experience can help
protect it. We help CSPs develop the
right strategies and plans and follow
them through to a successful launch and
We are uniquely able to leverage
subscriber data and make it available in
real-time. This promotes agile decisionmaking and enhances business and
operational processes.
Our solutions turn data into insights
and enable CSPs to act on those
insights. Our end-to-end approach also
comes with a clear commitment to
security and privacy at every stage.
Through Nokia and our own acquisition
and retention studies, as well as external
research, our unrivalled understanding
of the markets can help CSPs develop
segmentation strategies and implement
them within their operations.
We help CSPs transform their
businesses to take a genuinely
customer-centric approach.
Customer Lifecycle Provides a Wealth of Insight
Aggregate, Analyze and Act to Optimize the Customer Experience
Exploit Information Sources
The competitive intensity of the
Telecommunications Industry is
increasing rapidly as the growth
rates in new subscribers slow down
in many markets around the world.
The markets for many connectivity
services are approaching saturation
and new competitors are entering
telecommunications markets with lower
cost services and alternative business
models. In this market Communications
Service Providers are looking for new
means to increase revenue and profit
by retaining their existing subscribers,
selling additional products and services
to their current subscribers and finding
new ways to attract subscribers away
from competitors.
Communications Services Providers
have found they can use the information
generated across their enterprise as
a source of competitive advantage.
Information generated from network
events, billing records, CRM systems,
web traffic, product management
databases and other systems can be
exploited to improve the effectiveness
of business processes across the
enterprise and provide unique insight
into new opportunities. CSPs of all
sizes can increase revenues, profits
and customer satisfaction by managing
information assets more effectively,
analyzing information in real time, and
employing historical and predictive
analysis to optimize processes
throughout the customer lifecycle.
Aggregate, Analyze, Act
In this brief we outline a strategy
for optimizing customer experience
management via a logical
implementation of capabilities that
focus on: aggregation of all relevant
data/information that support the
customer lifecycle; application of a
rich set of analytical tools (including
real-time) that enable LOB to quickly
and accurately assess the current
state of market, products, network
services, devices, customers, etc; and
automation to translate analysis into
action. The capabilities described are
enabled by IBM’s Service Provider
Delivery Environment, which is a widelydeployed framework that allows a
provider to accelerate new services and
business models, achieve operational
and network efficiencies, and
differentiate the customer experience.
Critical Role of Data in Support of
Analysis and Action
TMForum’s 2009 CEM report stresses
that “data is the backbone of customer
experience processes.” One of the
greatest challenges is managing
the increasing volumes of data that
are critical in providing a uniquely
differentiated customer experience.
Key sources of data include: customer
information, with the goal of providing
an accurate, complete and consistent
view across all lines of business and
channels; an enterprise product
catalogue that efficiently manages
the detail, policies, business rules and
complexity of a provider’s offerings
across all channels and enables rapid
changes to product information;
the network, with an ever greater
volume of XDRs and operational data,
provides, perhaps, the richest source of
information with the greatest potential
to positively impact the customer
experience, assuming the provider has
the capability to capture, process and
analyze the massive volumes of network
data either in-flight or within a database
or data warehouse.
However, to gain the most from these
data, providers need to implement an
information management strategy that
encompasses data quality, consistency,
latency, comprehensiveness,
governance and lifecycle management
to insure that LOB activities and all
customer interactions promote loyalty
and profitability. Automatic aggregation
of multiple data sources and formats
presents a challenge; excessive time
taken in rationalizing and normalizing
this data into one consistent format
prevents timely business decisions and
Information Management Strategy
for “Network Intelligence”
IBM’s InfoSphere products and
Telecommunications Data Model solve
these issues for a European mobile
broadband provider. The provider’s
“network intelligence” approach
aggregates data from multiple sources
– network, CRM and billing, to enable
near-real-time monitoring of network
performance and customer experience.
The single source of network
performance and customer behavior
also allows the provider to segment
subscribers based upon calling/usage
patterns to gain insight into customer
experience and service requirements
with the goal of continually optimizing
network performance.
Every customer interaction is a
potential source of detailed customer,
product and network information to
be captured, processed and analyzed
to provide deeper market insight and
further improvement of customer
experience. However, these massive
data volumes create data storage
challenges. Alternatively, providers may
want to consider a data archiving and
retention strategy to reduce storage
hardware costs.
Managing Data Growth
To cost-effectively accommodate
petabytes of data growth associated
with customer and network data, a
North American triple play provider
adopted an enterprise archiving strategy
to curtail additional storage node costs.
IBM Optim allows the provider to
identify and archive volumes of historical
data to more cost-effective media, while
still allowing the provider to quickly
access or reference that data when
necessary. Savings in storage hardware
more than justified the investment in
IBM’s archiving solution.
Analytics Maximizes the Value of
Customer, Product and Network Data
By successfully addressing data
governance and lifecycle management
a provider establishes the foundation
for accurate and meaningful analysis
of customer, product and network
data. To exploit the latent potential of
these data, a provider needs analytic
capabilities which can be applied to
myriad data sources (both structured
and unstructured) in both real-time and
traditional fashion to deliver actionable
insight to optimize all aspects of the
customer experience.
Accurately Targeted Campaigns
IBM’s InfoSphere Streams, SolidDB and
Cognos Now! allow an Asian mobile
provider to efficiently determine which
offers are most attractive to particular
customer segments. The process begins
by creating as many as 700 promotions
to offer to sample groups of subscribers.
Real-time analysis and reporting enables
the provider to determine which
promotions are most relevant. These
promotions are then offered to a larger
audience while continually analyzing
the results of the offers. Iterative
refinement and analysis quickly result
in a set of relevant offers specifically
tailored to different market segments.
The real-time analysis, reporting and
automation of this process increase the
success rate of marketing campaigns
while simultaneously lowering campaign
Analytical capabilities should permit
lines of business to quickly assess
the current state of affairs relative to
their specific responsibilities – sales,
product profitability, churn, network
performance, campaign results,
customer service, spam detection
and elimination, and fraud, etc. – and
apply this insight to further improve
performance in these areas.
Analytics Prevents Fraud
IBM Identity Insight enables a European
provider to detect fraudulent account
activation attempts by automatically
analyzing customer information –
phone number, credit card information,
postal address, IP address or other
distinguishing attributes – across
disparate data sources. The information
is scored using sophisticated algorithms
that calculate provider’s risk based on
product cost along with data from usage
monitoring and payment collections
to provide a comprehensive view of
customer behavior compared against
historical information to reveal potential
fraud. One quantifiable benefit of
employing IBM Identity Insight is that
the provider avoided the financial losses
associated with provisioning iPhones to
fraudulent individuals.
To improve all areas of the customer
experience in the context of customer
lifecycle, analytical capabilities
should ideally encompass statistics,
modeling, data mining, predictive and
prescriptive capabilities. With these a
provider can develop more accurate
market segments, model buying
behaviors or purchase propensities,
launch carefully targeted promotions,
determine appropriate next best action
scenarios, assess network performance
in real time, facilitate capacity planning
for network build-out, develop
predictive models for churn mitigation
and customer lifetime value, and
continually evaluate the efficiency and
effectiveness of customer interactions.
Early Assessment of Customer
Experience Mitigates Churn
A European provider employs IBM
SPSS to assess and analyze customer
experience throughout the customer
lifecycle. The provider discovered
that unsatisfactory events in early to
midterm lifecycle have the greatest
effect on churn. Implementing a survey
program targeted at customers who
had been with the company for about
seven months identified more than 100
indicators predictive of customer churn.
The provider can now identify at-risk
customers with a 78 percent degree of
accuracy. By proactively engaging at-risk
customers the provider has reduced
churn rates from an average of 19
percent down to 2 percent.
Previously ignored sources of
customer insight contained in email,
CSR logs, blogs, IVR and social media
can also be exploited. “Unstructured”,
textual and contextual information
contained in these sources provides
insight into market trends, competitive
activities, customer sentiment,
product/service issues and can provide
guidance for additional cross-sell and
up-sell opportunities. In combination
with customer, product and network
data maintained in a data warehouse,
unstructured information provides
Customer Lifecycle Provides a Wealth of Insight
Aggregate, Analyze and Act to Optimize the Customer Experience
a significantly more detailed view
of the customer, enabling product
development, sales, marketing and
customer service to more accurately
develop, deliver and support relevant
Voice of the Customer Via
Unstructured Content
An Asian provider employs IBM’s
Content Analytics to glean insight
from CSR call logs, email and inquiries
received from customers. Analysis of
unstructured content enables product
management, marketing, finance, sales
and service management to gain a more
detailed understanding of the “voice
of the customer.” This has allowed the
provider to offer an optimum set of
services for mobile phone customers,
create a more compelling loyalty
program, establish more favorable offers
in model and service upgrades for loyal
customers and create a FAQ data base
which facilitates faster call resolution
and increases the usefulness of the selfservice web site.
Critical to the efficient analysis of the
massive volumes of provider-managed
data is the ability to continually capture,
process and analyze data with minimum
latency and translate this real-time
insight into business opportunities. Realtime insight can help providers establish
profitable contracts with retailers and
issue context-sensitive promotions to
their subscribers. Real-time context can
be derived either based on locations or
browsing patterns on smart phones.
Technologically progressive service
providers have been using real-time
analytics for a variety of purposes –
such as location based promotions to
maintain high utilization of assets during
off-peak hours, real-time promotions
to group leaders or social network
leaders as well as subscribers prone to
churn – and identification and pro-active
notification regarding network problems.
Network Analysis Reveals Millions
in Lost Revenue
A provider desiring to obtain a more
granular understanding of customers’
wireless experience in order to reduce
churn employed IBM’s Tivoli Netcool
Customer Experience Management
to conduct a proof of concept
using real-time data from 6 million
subscribers. The analysis revealed
that an astonishing 400,000 wireless
customers in one 24-hour period could
not access the wireless data network.
They could not download a ringtone, visit a website, or send a short
message – an estimated $4.8 to $7.2M
of lost revenue for the provider. The
operator also discovered that many of
these customers were denied network
access, not because of network
problems, but because they had not
purchased the right to use data services
in their service plan.
Acting on Analysis
Every customer interaction or service
usage creates data that can be
captured and analyzed to continually
refine segmentation models, customer
profiles, buying behaviors, purchase
propensities, network performance
and business processes that enhance
the customer experience. A wellimplemented information management
strategy combined with a powerful
set of analytical capabilities enables a
provider to exploit the inherent value
of data to continually improve the
customer experience. Throughout
the customer lifecycle – targeting and
marketing, acquisition, service usage
and support, key lines of business play
a critical role in planning, managing and
supporting the customer experience.
Each line of business has unique
opportunities to translate analytical
insight into actions to impact customer
experience. In many instances, analytical
models and results can automatically
be incorporated into customer lifecycle
processes to enhance customer
Reduce churn
Improve customer
Ensure successful
launch of new services
and user devices
visibility into
behavior and
service usage
operational and
investment costs
un-tapped revenue
among existing
customer base
Provide operations insight
into customer experience
Protect and increase
roaming revenue
Service Provider Benefits from Analytics–Driven Customer Experience Management
Targeting and Marketing
Product Management
•Assess product acceptance and
profitability via accurate and current
sales reports that enable granular
analysis, and augment product
strategy accordingly.
•Gauge market sentiment by analyzing
web and social media to identify
product gaps or previously
unrecognized market opportunities.
• Develop products and services more
closely aligned with market segments
though analysis of CSR logs and
customer correspondence.
•Apply predictive analytics to
determine buying propensities and
optimize product features or service
• Improve data quality and integration to
facilitate accurate capture of customer
and product information to facilitate
efficient interaction throughout the
customer lifecycle.
•Apply customer profiles and purchase
propensity models in the order
process to recommend appropriate
cross-sell/up-sell offers.
•Employ business process
management to accelerate order,
fulfillment and provisioning, using
business activity monitoring to assess
the on-going quality and efficiency of
the process.
•Analyze customer sentiment via
surveys, CSR logs, blogs, social
media and email to more closely
align products/services with market
segments, thereby lowering marketing
and customer acquisition costs.
• Develop customer lifetime value
models to guide marketing decisions
and customer interactions throughout
• Reduce campaign costs and achieve
better campaign results via more
accurate customer profiling and offer
• Monitor campaign results in realtime to improve accuracy of customer
profiles and increase offer acceptance.
• Develop predictive churn models that
guide offers and customer interaction,
and are enhanced via continual
analysis of customer, product and
network data.
•Extend data quality and process
efficiency established in the order
process to the fulfillment, provision
and billing processes to significantly
reduce order fall out and accelerate
activation time.
Service Support
Customer Service
•Employ CLV models for customer
segments and continually refine
models via aggregation and analysis
of customer transactions; use
CLV models to prioritize and guide
customer service.
• Develop predictive models to advise
next best action based upon context
of offer, appropriate channel and
purchase propensity. Continually
refine predictive models on basis of
NBA results.
•Analyze CSR logs and email to reveal
frequently occurring questions that
could be answered more efficiently via
self-service or improved CSR scripts.
• Monitor network performance down
to the device level in real-time. In the
event of fault automatically notify
customer via appropriate channel and
offer compensation designed to retain
profitable customers.
•Continually assess network
performance to identify areas that
may require infrastructure upgrades to
accommodate increased demand for
• Use historical operational data to
develop predictive maintenance
models to extend asset life and
minimize maintenance costs.
Summary and Recommendations
The true value of analytics cannot be
fully achieved without an enterprise
strategy that provides accurate,
consistent and current information to
enable line of business insight, and a
service provider delivery environment
to facilitate translation of analytics
into action throughout the customer
lifecycle. Aggregation of relevant data/
information, application of rich set of
analytical capabilities for structured
and unstructured information, as well
as real-time analytics to effectively
process massive volumes of network
data can enable providers to achieve
a significantly better understanding
of market, customers, products and
services. This understanding, when
acted upon and incorporated into key
processes of the customer lifecycle,
has significant potential to optimize
the customer experience while
simultaneously improving enterprise
Using Analytically Driven Insight
for Competitive Advantage
Around the world Communications Service Providers (CSPs) confront the same challenge. They
collect, process, and store enormous quantities of unique and varied customer data – data that
could give them much deeper insight into the total customer experience. Indeed, many industry
insiders and observers believe that customer data, not the network, is a CSP’s most important
asset – the crown jewel. However, most CSP’s undervalue customer data, and therefore are
not fully leveraging it to their advantage. As the global leader in Business Analytics, SAS is
helping over 200 CSPs get more value out of their data to improve all aspects of the customer
The Paradox facing the
Communications Service Providers
Customers enjoy more choices today
than they could have been imagined
just a few years ago. They delight in
personalizing devices and services to
suit their unique needs and preferences.
Both consumers and business
customers now demand greater control
and flexibility. To satisfy this demand,
CSPs now offer an expanded product
catalog accessible through self-service
portals. However, offering more options
inevitably leads to a more complex
operating environment that makes it
more difficult and costly to ensure a
high quality experience.
But customers also complain that
the number of available options leaves
them confused about the technology,
services, and the value they receive for
their money. This is the paradox faced
by CSPs today. To thrive in today’s
market you must deliver a high quality
experience, but increasing the number
of services and options makes it harder
to ensure that all customers have that
quality experience.
Business Analytics offers a proven
antidote to this paradox and many
successful network operators are
leveraging it today for competitive
advantage. Success in today’s highly
competitive marketplace is a function
of the quality of customer data and
how quickly and efficiently a company
can leverage that data to drive better
decision making. The competitive edge
goes to the CSP who invests in deeper
customer insight and then mindfully
choreographs customer interactions
tailored to each individual.
Proving the Value of Business
CSPs began using analytical software
decades ago. SAS has worked with
network operators around the globe to
complement OSS and BSS functions
by performing such tasks as network
capacity planning, demand forecasting,
customer segmentation, network
and service optimization, profitability
analysis, marketing optimization, and
many other functions. For most of
that time, analytics was the domain of
statisticians and data modelers. That
changed about a decade ago when high
churn rates threatened the survival of
many wireless operators.
Across the industry, churn rates
are now about half what they were
ten years ago, when many operators
routinely posted monthly churn rates
above 3% for their post-paid customers.
Too often, the cost to acquire a
customer was not recovered before the
customer terminated the relationship.
As markets became saturated leaving
fewer new customers to acquire, senior
executives became alarmed at the high
churn rates.
Analytical models identify the
customers most likely to leave.
Retention activities are then proactively
directed at those customers. CSPs
became better at identifying the drivers
of churn so they could prioritize and
execute corrective actions, while
remaining with their budgets. More
impressively, the operators who relied
on analytic insight were able to reduce
churn while maintaining or even raising
ARPU, becoming far more profitable
than their less analytically inclined
competitors who resorted to price cuts
and giveaways.
For many operators, this highly-visible
validation of the benefits of analytics on
business performance convinced many
senior executives that strategic use of
analytics has a much higher return on
investment than other options.
Innovations that Increase Customer
Profitability As the value of business analytics
became clearer, network operators
became highly innovative and varied in
their use of analytics. Many operators
now consider their analytic applications
as key intellectual property and a source
of competitive differentiation.
Customer segmentation at leading
CSPs is now a highly sophisticated
and essential business function
that considers how customers use
communications services. For many
years CSP s only segmented customers
as either business or residential. Later,
simplistic demographic segmentation
variables such as age, income,
geography, and gender were adopted.
More advanced approaches in use today
include behavior based variables that
tell a CSP how the customer is using
services, the cost to serve, and the
lifetime value of the customer. The
result is that a CSP can better classify
customers, have more profitable
engagements, and improve the ROI on
CSPs can also greatly enhance their
segmentation models by inferring
and leveraging the influence that an
individual has over other customers.
Analytics can give marketers the
tools and know-how to create more
cost-effective marketing campaigns,
reduce customer attrition by attracting
influencers, and provide more relevant
content in their marketing messages.
Users can quickly visualize social
networks between their customers
that were previously unknown to
uncover leaders, followers, and other
members within social communities.
By incorporating such role-based
variables, a CSP can enhance existing
segmentation models, and discover
how and when to target influencers.
In addition, marketers are able to
understand how products and ideas
diffuse through entire networks, thus
allowing tests of new campaigns which
would optimize the spread of new
products or services to their customers.
Customers frequently express
difficulty in understanding the various
price plans and options offered by their
service provider. Furthermore, many
customers believe that a different plan
would have resulted in a lower bill. Price
plan dissatisfaction is a leading reason
for customer churn. Business analytics
offers an efficient and effective solution
by calculating the optimal offer for each
customer in advance of a customer
interaction via a highly efficient method.
Simulating an individual customer’s bill
under any number of price plans can
give operators a precise, analyticallydriven, prioritized list of offers that
balance the customer’s desire to
reduce cost with the operator’s need to
maximize profits.
Where this is leading is to a
breakdown of the traditional
mass marketing model – and the
establishment of a marketing model
that’s customer-centered and
personalized. As noted by independent
research firm, Forrester Research,
Inc., “Only 13% of consumers say
that the ads they see are relevant to
their wants and needs, and even fewer
find direct mail and e-mail marketing
relevant. Consumers have had enough
of marketing, and more than threequarters say they want companies to
let them decide how a company can
communicate with them.”
In other words, consumers want to be
in the driver’s seat. They also expect a
consistent experience with their service
providers across channels. And, they
want a dialogue with the companies
– one that clearly demonstrates
employees take into account what the
business already knows about them.
To do this, a CSP needs to leverage the
customer data explosion for competitive
advantage. Rather than just using the
same syndicated data available to
competitors, a CSP can create unique
analytical insight about customers
and prospects based on how they use
communications services, what they
are buying, their location, how they
use websites and mobile applications,
their social media interactions and
more. They can then choreograph their
interactions based on this insight.
The Need for an Analytics
Architecture and the Role of the
As valuable as analytics have proven
to be, one might expect all CSPs to be
advanced user of business analytics, but
this is rarely the case. Most business
and IT executives of major CSPs admit
that they are not making the best use
Using Analytically Driven Insight
for Competitive Advantage
of all the data they collect and store.
The challenge, according to the CIO of
one of the largest US communications
service providers, is that multiple
customer data warehouses, silos of
information, and competing strategies
across service lines and divisions
prevent delivery of a more strategic,
holistic approach to customer
CSPs need a standardized approach
to data modeling for business
analytics just as they do for OSS and
BSS applications. The TM Forum’s
Information Framework (SID) offers the
industry’s best business architecture
for analytical applications. SAS has
invested significant R&D resources in an
analytic architecture that is aligned with
the SID. As the Forum increases the
focus on analytics, SAS’s customers will
be assured of easier and more effective
integration between systems.
When used to its full potential, business
analytics removes complexity from
decision making at all levels of the
organization. Speed, precision, and
efficiency of decision-making will
determine if a CSP can deliver the
quality of experience that customers
expect. To meet this challenge, the
most competitive CSPs in the world
are migrating analytics from the back
office up to the C-suite and out to every
decision point in the organization.
SAS’ proven software, services and best
practices offer communications industry
specific solutions, data management,
customer analytics, forecasting and
optimization to improve the customer
experience, business performance and
• Superior data management. SAS lets
you extract data from nearly any
source and transform it, as well as
integrate data from third parties and
across business and service lines for a
holistic customer view.
•A communications-specific customer
data model optimized for analytics
and aligned with the TM Forum’s
Information Framework (SID). An
optional communications data model
addresses segmentation, cross-sell/
up-sell, and churn.
• Powerful analytics. Data and text
mining and detailed segmentation/
profiling (churn analysis, market
basket analysis, customer profitability,
response modeling, next-best activity
modeling, etc.) help you understand
and predict customer behavior.
• Social influence analysis. Identify
social communities and measure
social influence based on relationships
between customers using rolebased variables to enhance existing
segmentation models and discover
how best to target influencers.
•Critical early-warning alerts. Only SAS
lets you establish triggers that send
early warning alerts automatically
when a key customer’s behavior
is about to change – so you can
intervene early enough to make a
•Cost and profitability analysis.
Calculate cost and profitability of
activities tied to campaigns as well
as customer, channel and product
• Patented optimization. Our patented
algorithm is more precise and
flexible can be applied to many
business activities, such as marketing
campaigns, resource planning and
allocations. Multiple weighted
objectives can be built in the model
for optimal results.
Only SAS provides an evolutionary
growth path that lets you address your
most critical business issues first, then
add new functionality over time as your
needs change.
Learn more at
Analytics Defined
The term analytics is trendy. As the
term becomes more widely used its
meaning is sometimes obscured.
Analytics cannot be asked as a binary
question, as in – “does a product
support analytics?” We have to think
of analytic applications as a spectrum of
offerings with different capabilities for
different tasks.
Plotting the spectrum of analytic
applications on a graph, with
Competitive Advantage along the Y axis
and Degree of Intelligence along the
X axis, we can see that as the degree
of intelligence increases, so does the
competitive advantage. We can also
divide these analytic applications into
those looking only at the past (green
spheres) and those that predict future
outcomes (blue spheres).
At a very low level, are standard
reports that answer the question, “What
happened?” Financial reports generated
on a regular basis are a good example.
They answer questions such as “How
many new customers signed-up last
quarter?” or “What was our churn rate
and ARPU?” or “Which devices are
selling best?”
Ad hoc reports are for special
situations such as asking “What were
the results of a one-time promotion?” or
“How did that code fix impact network
Query drilldown capability such as
OLAP enables deeper discovery, for
example, if handset returns are on the
rise, an analyst can look at a geographic
breakdown in search of a pattern.
Alerts are very useful in bringing
attention to a problem area. A good
example could be changing a dashboard
indicator from green to yellow and finally
red as a problem is developing.
All four of the above applications look
only at what already happened. These
applications are essential to keep the
business operating, but reveal nothing
about what’s likely to happen in the
future. That is farther up the spectrum.
Statistical analysis can answer the
questions, “Why is this happening?”
and “What factors most contribute
to network degradation?” Data and
text mining can be used to identify
correlations which may illuminate an
early indication of developing trends.
Forecasting helps CSPs more
accurately plan for changing conditions
by address the question “What if
these trends continue?” More accurate
forecasts can offer a clear picture of
things like future bandwidth demands,
customer churn rates, or the minimum
number of handsets to have in stock.
Predictive modeling can help answer,
“What will happen next?” For example,
when offering a new service predictive
modeling can identify which customers
are most likely to respond. Or it can
identify the customers most likely to
Finally we have optimization, which
tells “How do we do things better?” or
“How do we best align our resources to
achieve our objectives?”
The applications at the top of the
spectrum deliver the most value. As
CSPs face intense competition in a
rapidly changing marketplace, they need
analytics that span the full spectrum.
“The one who sends the (data service) bill,
owns the (data service) problem”
by Sean Timiney, Director, Mobile Solutions, Compuware and former Manager, Mobile Data Services, Sprint
Data, data everywhere:
The wireless industry passed an
interesting “crossover” milestone
late last year, but with surprisingly
little fanfare from press and analysts:
Ericsson reported that for the first time
ever, in December 2009, worldwide
mobile data traffic exceeded mobile
voice data traffic. figure 1.
More recently, in their “US Wireless
Data Market Q2 2010 Update” (1),
Chetan Sharma Consulting predicted
that on the current trajectory, the
next major “crossover” – data ARPU
exceeding voice ARPU – would occur in
the US market in Q2 2013. figure 2.
Let’s put these dry statistics into
hard business terms: Within the next
36 months, your mobile data customer
will be more important to your business
than your mobile voice customer. This
change is inevitable and irreversible.
For a wireless operator to be
successful in this new era, they have to
recognize that this is a highly disruptive
change that cannot be addressed
by fine-tuning existing management
tools and processes. Instead of being
Figure 1
organizations driven primarily by one
type of customer needs (reliable pointto-point voice communication within a
proprietary network), wireless operators
will be driven by an entirely new set of
customer needs (ubiquitous, seamless
access to applications and content
of their choosing across an open,
worldwide network – the Internet).
(their own network); non telco vendors
are heavily influencing user behavior
and expectations with a myriad of
innovative devices and applications;
and there is a whole new generation of
users that are impatient, unencumbered
with antiquated notions such as brand
loyalty, and who expect things to just
Using yesterday’s solutions to solve
today’s and tomorrow’s problems?
One of the key processes that will
distinguish the major players from
the “also-rans” in the new, datadriven operator world is the way that
they manage the user experience of
their service to ensure a profitable
and loyal customer relationship. The
traditional solutions used for decades
by fixed and mobile operators to
manage voice services simply don’t
work when it comes to managing
mobile data services. There are a
number of reasons for this: Unlike
voice services, mobile data services
are not confined to end points within
the mobile operator’s “walled garden”
“Within the next 36 months, your
mobile data customer will be
more important to your business
than your mobile voice customer.
This change is inevitable and
Figure 2
The major problem that’s being
exposed inside almost every operator is
that there’s a deep disconnect between
what the operator thinks is happening
on the data network, and what the end
user is actually experiencing:
- On the one hand, the operator’s
visibility is confined to one part of the
service delivery chain (the network),
and with limited or no visibility into
other parts of the delivery chain
(device, application, etc). figure 3. In the
absence of any other data, when “all
lights are green” on the network, the
assumption can only be that “we think
all is well with our users.”
- On the other hand, the end user is
continuously experiencing the entire
service delivery chain (including any
problems), and perceives “the mobile
data service” as a single entity provided
by the operator. So, rightly or wrongly,
end users will hold the operator
accountable for any problems without
consideration for who in the service
delivery chain is actually to blame.
In other words, operators are in the
awkward position of not having full
visibility into the service delivery chain
that their customers are using, while
still being expected to assure that the
customer has a good experience.
Disruptive problems demand
innovative solutions:
At the heart of the problem facing
operators as they transition from a
voice-centric to a data-centric business
is the fact that there is a rapidly
widening gap between the operators’
view of their mobile data service quality
and the actual end-user experience of
the service.
The only way to narrow (and
ultimately eliminate) this gap is for
the operator to adopt an innovative
Service Performance Management
approach: manage mobile data services
by managing the entire service delivery
chain, not just the elements that are
under the operator’s direct control.
figure 4. The approach goes way
beyond just implementing the latest
and greatest “visibility management”
software tools; it also includes aligning
departments within the organization,
and even managing the customer
“End users will hold the operator
accountable for any problems
without consideration for who
in the service delivery chain is
actually to blame.”
Figure 3
experience before they actually become
a customer. Although this approach may
be new inside the operator’s business,
it is actually a tried-and-tested approach
that has been successfully employed
in a large number of enterprises to
manage the delivery of their IT services
for internal users and customers. In
fact, the problems faced by these
enterprises are remarkably similar to
the problems that operators now face in
delivering data services to their users.
Figure 4
“The one who sends the (data service) bill,
owns the (data service) problem”
The Service Performance Management approach defines 4 key principles:
i)Unite at the customer: The traditional silo-focused departments within the operator must unite at the customer. This
means that each department must communicate problems across departmental boundaries, and always in terms of what the
problems mean to the customer. For instance, if Engineering detects that Cell Tower 123A is operating at reduced throughput
because of an electronics failure, they must communicate this to other departments as “Customers in postal code 34017 may
be experiencing slow performance for the next 2 hours.” This enables customer-facing departments to respond quickly and
accurately to customer calls, to post information to a customer performance website, or even to proactively notify affected
users in that area via SMS.
ii) Create a customer experience ecosystem: True end-user-focused service management requires a unified ecosystem of
people, responsibilities, processes and tools inside the operator to find and fix problems quickly. Without this, operators are
unwittingly putting their customers at the forefront of problem detection, since all too often, the first time the operator knows
of a problem is when the customer calls to complain. The customer experience eco-system brings together disparate parts of
the organization by providing one view of the truth aligned to a clear line of responsibility, which enables them to identify and
solve the right problem with the right people, as opposed to debating if the problem actually exists or who owns it.
iii) Develop an “outside-in” view of your business: Although it is frequently overlooked, the “outside-in” view of the
customer experience is an important part of building a competitive offer and a profitable, long-term relationship with
customers. It starts with real-time monitoring of the performance of the operator’s own website from outside the organization
(the customer’s point-of-view), and so includes content from a number of partner sites. If any of the partner sites are
performing badly, it will look to the customer (or prospective customer) as though the operator’s website is slow. Even more
important, “outside-in” performance monitoring should include monitoring of the performance of customers’ most important
applications and services (e.g. Facebook, YouTube, Google, the operator’s website, etc.) over both the operator’s network and
their competitors’ networks. Using the “collective intelligence” gained from “outside-in” monitoring, very valuable customer
data can be obtained. For instance, is the operator providing a better, or worse, experience with device A on Facebook
than a competitor using device B? It also allows the operator to determine what devices are most popular on the network,
irrespective of who actually sold the device (e.g. including roaming-in users and unlocked devices).
iv) Develop a real-time, end-user experience view of data service performance: The vast majority of the mobile data traffic
handled by the operator is to/from services and websites which reside outside the operator’s own network, somewhere on
the Internet – the operator is simply part of an extended service delivery chain. A real-time, end-user experience view of data
service performance allows the operator to gain visibility into the performance of the entire service delivery chain: the device,
application, network and content that make up the mobile data service for the user. With this visibility, the operator can now
view these service delivery segments from the perspective of the end user. With real-time and continuous monitoring of the
service delivery chain, the operator gains the ability to proactively identify and resolve service experience issues before they
become impediments to individual user perception of the service.
Compuware puts it all together:
In response to requests from many
operators around the world, Compuware
developed the vision of “Proactive
Customer Awareness” (PCA). This
incorporates the Service Performance
Management approach described above,
plus it uses a phased implementation
methodology which defines three major
milestones as shown. figure 5
This is the approach that carriers
around the world are adopting as their
strategic direction and deployment
methodology to achieve their business
goals. Compuware’s PCA vision also
recognizes that operators have a
significant investment in existing tools
and processes, so it offers the ability to
Figure 5
leverage those whenever possible.
Compuware’s PCA solution builds
a new foundation for managing and
growing high-performance customer
data experience. It combines our
market leadership in enterprise end-user
experience management (the Vantage
product line) with our market leadership
in “outside-in” web performance
management (Gomez) to deliver a truly
unique, end-to-end solution to manage the
data service experience.
As mobile operators become increasingly
dependent on data services for revenue
and margin, they are faced with the fact
that their existing network management
tools and operational processes are unable
to meet the needs and expectations of
this new era. There is only one solution:
Re-focus the organization to unite around
the customer, not just the network. This
requires working with a vendor that has
the operational experience in a number
of areas, such as end-user experience
management, website performance
management and service delivery
Compuware has extensive,
demonstrable experience in all three
areas: It pioneered end-user experience
management of data applications in the
enterprise; it has been helping businesses
manage the delivery of mission-critical
services for over three decades; and
Gomez, our web performance division,
provides the world’s most comprehensive
testing network, covering every logical
and physical layer of the web application
delivery chain. Compuware has expanded
this industry-proven approach to managing
mobile data services, and is now helping
global mobile operators achieve success in
managing profitable mobile data services.
Compuware’s solution is more than just
adding more tools. It is an approach to
help mobilize the departments involved in
service delivery, leverage existing tools,
improve processes, and use innovative
tools that help deliver maximum user
experience on mobile data services to
ensure a loyal, profitable relationship with
your customers.
Report “US Mobile Data Market Update Q2 2010”
Closing down the Customer Experience
Gap and Safeguarding against Churn
Arantech have taken a radically different
approach to the BSS and OSS market
compared to existing vendors, by building
a system that puts the customer at the
heart of its architecture.
Today’s operators must embrace
customer advocacy service models if
they are to gain new users, drive up
ARPU and increase customer lifetime
value. Such service models, which are
geared to helping customers achieve their
objectives, require operators to ‘co-create’
value along with their customers through
effective business support systems (BSS).
Customer experience management
(CEM) is an increasingly important
element of the OSS function of many
telcos, but the potential that CEM
systems offer in terms of the BSS
function is still to be realised. Finding
ways for BSS to effectively use realtime, point-of-use CEM data offers
significant benefits for operators to map
performance objectives, identify next best
action (NBA) strategies and build accurate
churn propensity models.
The following article considers the
issues faced as operators migrate to
customer advocacy models, look at the
requirements for a customer advocacyorientated BSS, identify how CEM data
can help to address those requirements
and discuss ways that this data can be
effectively mined by the BSS function.
Safeguarding themselves against
churn and ARPU decline has become
a key objective for the mobile operator.
However, in attempting to identify
churn rates in different sections of the
subscriber base, the operator is faced
with some difficult problems – they must
deal with a lot of information coming from
disparate elements of the network, and
they do not have an understanding or
vision of how to interpret this information.
Reference, Figure 1.1 Stop Churn.
CEM delivers a unique, new dataset
that provides an operator deeper insight
Figure 1.1 Stop Churn
into its entire customer experience
and behavior, especially the real-time
experience of data and other services.
In putting together a coherent strategy
to overcome potential churn problems,
Arantech suggests that the operator asks
the following questions;
What information is required?
If we believe what the analysts are
telling us, we are seeing a churn factor or
industry standard of 30-35% churn. Given
this, the operator needs to understand
what the level of churn is on their
network, and confirm whether they are
within the industry norms.
The operator must out the rate of
churn, identify higher or lower rates in
specific groups, and discover what are
the influences on these groups and what
can they do to reduce or strengthen
this influence. They will also need to
understand what return is being sought
from the marketing activities that they
deploy to affect the rates of churn.
What are we looking for?
The operator should start to look at the
subscribers who are at a higher risk of
churning from pressures such as bill
shock or other “bad experiences” like
dropped call rates. Subscribers coming
to the end of their contracts are also in
this high-risk category. The operator must
analyze the customer base to find these
high-risk subscribers.
We would advise that they start to
look beyond typical market research to
segment the customer base. Operators
cannot afford to keep using traditional
demographic or attitudinal segmentations
to make decisions about differential
treatment of existing customers. It is not
enough, for example, for an organization
to target the “youth segment,” because
this group encompasses customers
with a wide range of usage and spend
patterns, and its members may not be
accurately identifiable in the first place.
Instead, the operator could create
“micro-segments” that provide a closer
view of customer types and vary by value
driver. These micro-segments can be
developed incrementally using subscriber
information. This is data that very likely
already exists on various databases
throughout the organization such as
Customer Experience Indicators (CEIs), or
Customer Data Records. (CDRs).
Getting this data into a single data mart is
the first step; from there, it can be groomed,
updated, and redeployed to all the key areas
of customer contact, including call centers,
retail outlets, and the operator’s Web site.
As more data is gathered and updated, its
usefulness and value grows.
If the operator can do this, they may
be able to quantify the lifetime value of
each subscriber and start to do things like
“de-averaging” customer segments and
offers. This is where they are not making
offers to an entire segment, which may
contain millions of subscribers, but instead
make focused, targeted offers to more
precise groups, thereby achieving a higher
yield from these activities.
The operator also needs to understand
propensity & pressure to churn. This can
be viewed in the form of a social network.
A typical user will be at the center of
a social network that includes their
family and colleagues. If they have a bad
experience, this will have an influence on
the network by increasing the propensity
for churn. The factors for this influence
will vary according to the strength of the
connections in the network.
The operator needs to understand that,
despite having identified the groups of
subscribers and key subscribers that they
want to focus on, they will have a very
limited time in which they can reverse the
effects of a bad experience. This window
is a key factor in managing a churn
reduction program. Understanding this
window and the duration that they have to
respond for each group of subscribers will
be an important consideration.
Subscriber Profitability
Once the customer base has been
segmented and the churn pressures are
understood, the operator needs to evaluate
the results and focus on a group that
they feel will produce a meaningful return
or yield from the preventative measures.
We would advise the operator to look
at some of the following factors to build a
set of thresholds or levels that will define
when the subscriber becomes profitable.
1.Age Analysis – This is contract
information that can be extracted from
a third party. This is also called the
FICO score or credit score. The data
can be imported from agencies such
as Experian, Equifax or TransUnion.
Injecting the FICO scores use third
party analysis to look at the risk factors,
but this will have to be referenced by
something like a social security number.
2.Billing History – Payment history
means a number of different things. It
means more than just a history of the
subscriber paying on time, although
that is important. If they pay 30 days
late or 60 days late, that is recorded and
can build the propensity analysis. Even
a single late payment can dramatically
lower this rating.
3.ARPU Analysis – This can be split into
two elements, the tariffing element
and the cost element. The tariffing is
the “package” the subscriber is on and
this can vary according to the needs
of the market. Data tariffs associated
with a handset such as the iPhone
can vary significantly with that of a
basic package. The cost elements
can be associated with the region the
subscriber is based in and can include
costs of an urban area such as backhaul,
spectrum, etc. as compared to a rural
cost basis.
4.Contract Duration - The average age
of an account with the operator is also a
factor in propensity rating. For example,
subscribers with a long history of good
use and prompt payment with a low
cost of retention allows operators to
rate accordingly.
The operator now has built a rating
engine for his subscribers and they
can now assess the desirability of the
subscriber that has the propensity to
churn from a financial perspective. This
will enable the operator to focus on the
retention of the more desirable subscribers.
Customer Satisfaction Index
The operator is now in a position to build
a model that will enable him to take a
filtered group of subscribers and focus
on their satisfaction with the operator as
a service provider.
We would advise the operator to build
a Customer Satisfaction Index (CSI) to
monitor these filtered subscribers. There
are ranges of inputs or KPIs that form
the CSI. We have suggested some of the
KPIs and measures that form the CSI they can include the following;
1.Commercial Data – This can be a
summary of the profitability data
as described above or it can be the
rating that is currently used by the
care agents in the customer care data
associated with the subscriber.
2.Network Experience for Voice
& Data – Using a CSI to identify the
network issues affecting subscribers
can drive key metrics such as network
attach success rates from 70% to
over 90% (based on existing Arantech
3.Service Usage for Voice & Data – It
has become more and more difficult
for an operator to measure exactly
how individual services are being
used by subscribers, and how each
of these services are contributing to
overall revenues. A CEM solution such
as Arantech’s can provide key usage
metrics for us in an overall CSI.
4.Customer Complaints – If the
subscriber has called the operator to
make a complaint or highlight a problem
when linked to the experience or service,
this can present the operator with a
very real picture of the problem for rapid
resolution and/or monitoring. We have
seen this used to great effect in the
management of VIPs and for large events
such as sporting fixtures.
Closing down the Customer Experience
Gap and Safeguarding against Churn
5.Survey Responses – If we add survey
responses to this index we get an
understanding of the average levels for
the CSI. An example of this would be in
the use of an outbound survey on the
levels of survey from a new handset.
The results can be fed into the CSI for
each subscriber surveyed and this can
be aggregated to see if the desired or
expected levels are being achieved.
The CSI is very effective in
understanding where the satisfaction
level is for the selected subscribers and
where the potential issues are and what
may have in an influence on this across
the network or handset.
Decision Engine
The final element of this process is the
decision engine. Now that the operator
has decided on and is monitoring this
group of subscribers he next needs to
decide how he is going to respond to the
issues that are highlighted in the CSI.
1.Intervention Strategy – The positive
effect of a care agent calling a
subscriber to tell them of a problem
with the network or their specific
service is very powerful and can
have a very strong impact on the
pressures and influences to churn. The
intervention strategy can be some form
of credit or gift that is in line with the
decision of the marketing function. The
reality of this situation is now that the
operator can monitor the effects of the
intervention strategy in “real-time” or
something very close to it.
2.Closed Loop Capability – The ability
to automatically identify, process
and respond to customer and
network events in a meaningful way
in real-time is critical in identifying
negative experience scenarios
and to proactively address user
experience issues as they arise. For
example, Arantech’s OpenPlatform
ProAction™ solution has had proven
success in greatly reducing customer
care calls and ensuring that revenuegenerating services continue to be
available and usable.
Now that the operator has all the
elements in place, they will be in a
position to greatly increase their ability in
predicting churn in their network.
Arantech have also seen that if
the operator can maintain customer
satisfaction among a group of profitable
subscribers, this will have a positive
impact on free cash flows as the operator
will generate more cash from operations
and use their infrastructure much more
efficiently, thereby generating higher free
cash flows.
The difference between what a
customer actually experiences and
what can be currently measured by the
operator using existing tools is called
the ‘customer experience gap’. See
Figure 1.2 The Customer Experience
Gap. This gap is measurable and present
in most telecommunications networks
and is growing rapidly in the area of data
services. Identifying and closing this gap
will be a key factor in the future success
of any mobile and fixed line operator. The
ability to identify the poor experiences
of its users, and act on the outcomes
automatically is key and will result in
Figure 1.2 The Customer Experience Gap
higher levels of customer retention,
advocacy and greater revenues, leading to
market share gains over rival operators.
CEM systems are designed specifically
to identify and close this experience gap
as well as gain deeper customer insight
into experience and take actions on
customer events. Using CEM systems,
operators will be able to understand how
subscribers interact with their services in
real-time and be able to respond to issues
that cannot easily be uncovered by OSS
KPIs or existing data stored across the
business (e.g. CDRs).
Most customer insight, experience
and behaviour knowledge is currently
gained through data mining exercises
against existing data sources already
stored within the BSS environment. This
exercise entails sifting through enormous
quantities of data in an attempt to rebuild
the customers’ experiences over time
(typically CDR and usage data is used
from data warehousing solutions). Very
little, if any, OSS network transactional
data is stored and available for this
purpose, as this data is usually kept
within the monitoring tools to enable
session and protocol level analysis to
help root cause and problem diagnosis of
services and the network. This means the
customer usage experience is missing
from most customer insight analysis in
BSS. Trying to make such large volumes
of raw transaction OSS data available
into Business Intelligence tools would be
extremely difficult for three key reasons
(a) the cost of storage for such data
volumes would be restrictive (b) the data
would be uncorrelated with customer id
and other useful index types (e.g. handset
device and service node), requiring huge
post processing queries and the need to
correlate with other data sources and (c)
the knowledge required to build sensible
data queries of such low level data would
make using the solution impossible for
anybody other than a protocol expert.)
The uniqueness of a transactional based
CEM solution is that it can make this
experience data available and combine
it with BSS datasets. The system builds
in real-time a set of new KPIs from the
raw protocol flows across many business
and operational touchpoints, creating a
unique customer centric data set that
reflects actual experience and is customer
specific. This process both links important
index types to service KPIs at aggregation
time and also removes the need to store
unwanted and irrelevant data which is
dropped after the CEIs are built, retaining
only those errored events and experience
counts that define a customer’s experience
at that time. Each customer therefore has
an experience record in near real-time in
the database, such data can be configured
in real-time by the operator into experience
threads against flexible or a dynamic group
of individuals and reported against using
standard Business Intelligence software,
dashboarding and reporting techniques
to provide deep customer insight. This
data can also be made available to other
standard BI tools or applications through
solutions (such as Arantech’s touchpoint
OpenPlatformTM), which includes a
Datamart and a rules based action engine.
The Arantech Approach
Arantech was one of the first companies
that pioneered the use of CEM as
a technology within the telecom
industry, and has successfully delivered
its flagship CEM solution called
touchpointTM into some of the world’s
largest mobile operator groups. Through
its touchpointTM product offering and
OpenPlatform™ interfaces it has
been able to take raw event data from
the network and model the underlying
protocols as useful customer KPIs
allowing this to be cross referenced with
customer relationship and demographic
information held within the operator’s
BSS stack. This gives a unique
perspective of the subscriber lifecycle
that helps the operator achieve higher
levels of customer satisfaction and the
ability to proactively reduce churn.
Founded in 1999, Arantech is
the premier provider of Customer
Experience Management (CEM)
systems to communications service
providers worldwide.
Arantech’s CEM solution radically
transforms not only the way that
operators service their customers but
also the way their internal organisations
respond to customer needs. This has
made touchpoint™ both a catalyst for
cultural change and a transformational
force for its customers in terms of
revenue and operational practice, or
network issues. touchpoint™ provides
a simple proactive approach to first-line
customer management and selling,
supporting any network, service or
device type, through its touchpoint™
360° desktop portal. It also enables
tight integration via APIs to other BSS
and OSS systems like customer care,
service management, and performance
management, providing these systems
with a high level of customer centric
Arantech’s CEM solutions provide
mobile operators with a unique
customer insight, and enable them to
take proactive management action on
real-time experience events.
touchpoint OpenPlatform™ recently
launched by Arantech opens up access
to the touchpoint™ data through open
access protocols such as web services
and SOA, giving access to CEM data
to a broader range of stakeholders
and third party application vendors.
This enables operators and third party
application vendors, to drive-and-derive
the benefits and value of CEM data
throughout their organisation.
Arantech is the customer experience
management specialist within the
wireless industry, and is actively
broadening its reach into other channels
of digital service delivery, such as fixedline broadband and mobile convergence.
See Figure 1.3 Converged CEM View.
For more information, visit
Figure 1.3 Converged CEM View
Managing Customer Experience in a Hyper-Connected World:
The growing role of Business Intelligence
Every touch point between customers
and their service providers or partners
contributes to some form of customer
perception, leading to satisfaction or
frustration, loyalty or churn and ultimately
to the profitability of a service provider.
As defined by TM Forum in their 2009
Insights research report, customer
experience can be viewed as the result
of the sum of observations, perceptions,
thoughts and feelings arising from
interactions and relationships between
customers and their service provider(s).
Therefore seeking to retain and up-sell
customers, introducing a loyalty program,
attracting new subscribers, focusing
on lifetime value are all examples of
customer oriented initiatives that will
create an experience, and a trace in
both the customer’s mind and the
Communications Service Provider’s (CSP)
information systems.
It is widely accepted that Business
Intelligence has a fundamental role to
play in helping CSPs manage customer
experience by locating and extracting
these traces, this customer data, across
their entire organization, cleansing
and consolidating it, analyzing it and
finally making it available to the “point
of opportunity” to support the right
customer impacting decision. Though the
structure of many CSPs (including “silo”
line-of-business and regulatory walls )
often makes it difficult for them to get a
complete and consistent representation
of all customer experiences, most
service providers have already started
their journey to incorporate analytics in
their Customer Experience Management
strategy and have achieved various levels
of success.
Where the most timid CSPs still
struggle with the accuracy or the
quality of their customer data, the most
progressive ones can already point to
tangible results, such as improvements
in first call resolution rate or customer
retention, thanks to the effective use
of Business Intelligence in their daily
interactions with customers.
As CSPs continue to incorporate
analytics in their customer experience
management strategy, a number of new
trends have recently emerged that will
offer them new opportunities to augment
the role that Business Intelligence can
play in their organizations.
• The era of ‘Big Data’
As the proliferation of connected
devices per individual continues in
mature markets, and the Machineto-Machine (M2M) communications
space is set to explode in the coming
decade, the pace and volume of
customer centric data accumulation
will only accelerate for CSPs. IDC
estimates that more than 1200 billion
gigabytes of information will be created
in 2010 and that this ‘digital universe’
is likely to double every 18 months.
As data sources and volume grow,
CSPs will be forced to sift through
an ever greater amounts of data,
both structured and unstructured,
to maintain successful customer
by Stephan Gatien
• Let me do it myself
Broadband pervasiveness and new
breeds of customers such as the
digital natives who have always known
a world with an Internet connection
will continue to fuel a ‘do it yourself’
attitude and to create a growing
appetite for self-service. The customer
experience for this segment will
increasingly be dictated by his ability
to conduct business on his own terms
and to be able to find the information
required at his fingertips. Business
Intelligence can be an integral part
of this push towards self-service and
become an experience enhancer by
providing access to personalized data
such as usage behavior, consumption
history and billing details.
• A more engaged customer
The emergence of social networking
and instant communications
capabilities means that the experience
of a single customer (whether good
or average, excellent or terrible) will
increasingly shape and potentially
impact the behavior of many others.
Though some of this content may
be available in internal or external
customer satisfaction surveys, it is
more likely that the vast majority
of these comments will be located
in blogs, forums or social network
sites such as Facebook and Twitter.
Being able to locate and transform
unstructured data or text and turn
it into valuable data for analysis
will therefore become even more
• A real time world
The digital experience has already
significantly compressed the time
span of many activities in the industry.
Ordering a video on demand or
purchasing a mobile app takes only
a few seconds. Instant access is the
new form of instant gratification for a
new breed of subscribers and partners.
This tempo will increasingly define
the expectations of customers and
dictate the tone of all facets of the
relationship with subscribers from offer
management to dispute resolution.
These trends will impact both the
behaviours and expectations of customers
as well as the complexity of a CSP’s data
environment. Here are some areas that
service providers should consider:
• Focus on Enterprise Information Management
With virtually every aspect of customer
experience hinging upon the accuracy,
consistency and accessibility of data
scattered in an increasing number
of sources, the need to establish a
corporate wide trusted data foundation
has never been so critical. Therefore,
deploying an information management
solution that will provide extraction,
cleansing, enrichment, consolidation
and integration of data across the
enterprise, coupled with powerful
master and metadata management
capabilities is, more than ever,
required for CSPs. This will help
establish a trusted environment to
make better fact based decisions when
interacting with customers.
• Learn and Predict
Determining the propensity of a
subscriber to churn or his propensity
to be up-sold based on attributes such
as number of devices owned, size of
household, postal code or monthly
spend can enable a much more
granular and personal conversation
with a subscriber or prospect. With
Predictive analytics, the availability
of highly sophisticated modeling and
analytic engines, allowing CSPs to
leverage their complex historical data
to establish correlations, uncover
patterns and identify trends needs to
be fully leveraged by Service Providers.
• Leverage In-memory Analytics
What once represented a promising
technology is now a reality. With
the rapid decline of memory prices,
the need to store pre-calculated
data (in the form of OLAP cubes or
aggregated tables) is being eliminated.
For CSPs, this opens the door to the
possibility to browse through very
large data sets (up to billions of data
records) in seconds and effectively
deliver valuable insights during each
interaction with a customer, whether it
is around identifying the optimal offer
or better understanding the profitability
of an individual customer.
• Consider BI On-Demand
The BI space is clearly impacted by
the SaaS revolution. BI On-Demand
will increasingly offer a very costeffective way for CSPs to further
leverage Business Intelligence during
interactions with customers. CSPs
should explore how BI On Demand
solutions may help them spread the
usage of analytics at each ‘point of
opportunity’ with customers and, in
doing so, further empower a broader
range of customer-facing roles in their
organizations. In addition, BI OnDemand has the potential to become
a very cost effective pillar of any CSP’s
self service strategy by enabling a
simple deployment model of access to
personalized data such as usage and
consumption patterns or billing history.
• Analyze in Real Time
As highlighted earlier, the digital
economy will increasingly run in realtime. The business implications are
obvious for CSPs. A greater level of
agility will be needed to create more
differentiation from the competition,
particularly as they interact with
customers. Gaining this extra edge will
be virtually impossible without realtime analytics. Thanks to the advent of
technologies such as Complex Event
Processing (CEP), opportunities exist
for CSPs to operate at real-time speed
and leverage real-time insight to make
new decisions or react quickly to any
changes in their business.
Business Intelligence has a critical role
to play for Communications Service
Providers to help them manage
optimally each customer experience.
The emergence of a hyper-connected
world with a proliferation of devices per
individual and the probable explosion of
M2M communications will intensify the
need to remain customer relevant in a
growing ocean of data. Whether it is to
analyze structured or unstructured data,
on premise or on demand, in real-time or
not, CSPs should select a comprehensive
platform provider that can support them
across the different dimensions they
will require to broaden the usage of BI
in their organizations in order to enable
better customer facing or customer
impacting business decisions.
Real-Time Intelligence,
Real-Time Value
1.0 Introduction
The global telecommunications market
has become increasingly competitive
over the past half decade. Markets are
nearing (or have passed) 100 per cent
penetration and new entrants – including
‘over the top’ content providers – have
undermined communications service
providers’ (CSPs) profitability and
revenue growth. CSPs recognise that
the customer data they hold is a unique
asset which they need to exploit better,
but they lack the means to do so fully.
Many CSPs have made significant
investments in Business Intelligence
(BI) and while they have had some
successes, BI has not enabled them to
achieve the business agility they need.
CSPs need to adopt a new approach
– one which gives them strategic
advantage by tracking and responding to
changing trends in customer behaviour –
in real-time.
In this report we will explain how data
within business support systems (BSS)
can be exploited to enhance business
performance. We refer to this as Realtime Actionable Intelligence. This area of
the analytics spectrum is rapidly growing
in popularity: Gartner believes that
“during 2010, real-time decisioning will
be the most adopted category of analytic
2.0 What is real-time actionable
2.1 The elements of real-time
actionable intelligence
Let us look at this in three parts:
REAL-TIME. The BI model used by most
CSPs today uses historical customer data
held in a data warehouse. While this
approach holds some value for longerterm and predictive modelling, it cannot
help the business respond to short-term
To see how customers are behaving
and launch offers which appeal, a blend
of historical analysis and real-time
customer insight is needed.
Such real-time insight needn’t be a
huge technical challenge, as BSS tools
generally include real-time capabilities.
INTELLIGENCE. The trends yielded
must facilitate some kind of business
improvement. For example, by-thesecond insight into metrics like a
customer’s spend from the start of the
week, or creditworthiness based on
top-up behaviour, speak volumes about
customers’ needs at that moment in
ACTIONABLE. The ability to act is the
final link in the chain. Here, CSPs are
Figure 1: Telecoms revenue growth trends 2004-2013
often hamstrung by internal processes:
delays of several weeks between data
capture and action are commonplace.
Yet if successful retailers can manage to
fine-tune promotions day by day to get
the most profit out of each segment,
there is no reason why CSPs cannot
do the same. CSPs should know on
a daily basis who their most valuable
customers are.
2.2 Real-time actionable intelligence
in action
A common problem with pre-paid
customers is the decline in top-up and
recharge revenue towards the end of
each month. A CSP equipped with realtime actionable intelligence could:
1.Target prepaid customers who have a
balance of less than €10 with a free
gift (say, a download) if they top-up in
the next hour
2.Offer those who don’t accept an
immediate 10% bonus on their credit
3.Analyse information who accepted
the promotions immediately for overall
Another issue concerns ‘capped’
broadband access. Customers at their
usage limit are far more likely to churn if
they receive an offer from a competitor,
but CSPs can be proactive in making
offers of their own, and applying them
immediately, e.g.:
1.Buy a one-off 1Gbyte of usage for half price
2.Upgrade to an unlimited plan with the 1st month free.
3.0 How will my business benefit?
3.1 Long-term value from real-time
The value of real-time actionable
intelligence isn’t just restricted to the
CSP’s own services. CSPs have a great
opportunity to market these data to third
benefit from real-time intelligence, for
example, monitoring employee behaviour
to tackle fraud or curb spend overruns.
Wholesale users may want a realsMicrosoft_PerspectivesYearbookAd_2010_Final.indd
1 time view of usage so that decisions on
3.2 A consolidated view
dynamic pricing and routing can be made
One of the big reasons why BI
implementations fail is the sheer diversity right away.
of data sources that must feed into the
4.0 Where does real-time actionable
system. The right real-time actionable
intelligence sit in the BSS stack?
intelligence solution can provide a single
The logical home for real-time actionable
consolidation layer that encompasses
intelligence is the rating engine. Here,
every potential data source. This
the tools can tap into all data from all subapproach has been operationally proven
systems, manipulating it to serve a range
to produce significant cost savings.
of purposes, as figure 2 demonstrates.
The ideal rating engine should have the
3.3 Better promotions
following characteristics:
A recent report from Nokia Siemens
Networks for instance found that 33% of
•Event agnostic – i.e. capable of
churn could be attributed to competitors
working across all network and content
making the right offer to other providers’
service types
customers. The ability to respond rapidly
• Streamlined rating and billing – for
to customer behaviour means CSPs can
wholesale transactions between
pre-empt the competition.
partners. This can vastly simplify
More importantly, the customer gets
business processes and reduce costs
a more personalised experience – a real
•Able to trigger real-time events on
advantage in today’s marketplace.
a per-customer basis – for example
a top-up offer is only sent to prepaid
3.4 Wholesale and enterprise
customers whose accounts meet
specific criteria
The need to know what is going on right
now does not just apply to retail services. •Easy integration with existing BI and Campaign Management tools.
Enterprise customers in particular could
parties – such as content providers,
brands or media planners – with the CSP
providing the delivery network.
Figure 2: Real-Time Actionable Intelligence and the BSS Stack
(source: Convergys)
4.1 Deploying real-time actionable
Convergys’ Smart Suite of BSS
applications takes an overlay approach
that allows the CSP to achieve a realtime view without replacing existing BI
Following a staged approach,
CSPs can tightly link implementation
milestones to business outcomes, as
figure 3 explains.
5.0 Conclusion
The communications industry is buying
heavily into BI which, though useful in
parts, doesn’t provide all the capabilities
CSPs need to remain competitive.
Convergys asserts that simplicity
must come first. Real-time actionable
intelligence provides a basis for
improving data quality and business
performance. Most importantly, in
combination with a marketing rules
engine it provides a valuable tool to
implement actionable promotions
that maximise segment and category
Convergys Smart Suite provides the
right information at the right time so
CSPs can win customers and boost
profits, all the time.
Figure 3: Stages in real-time actionable intelligence
(source: Convergys)
Enabling a positive customer
experience through the ROC
The value of Customer Experience
Management (CEM) is in understanding
everything there is to know about a
customer, and more importantly treating
them in a unique way. Introspecting
“What would one do differently, if one
knew each customer personally?”
To achieve this goal one must
consider the entire life cycle of a
customer before personalizing touch
points and continuously improving the
It is important to understand there
is no one size fits all answer. CEM
targets the emotional response of
customers and as such is unique to
each customer just as each customer
is unique. Calculating a customer’s
potential satisfaction requires careful
planning along with a regimented
approach to customer experience
management. This calls for an extensive
application of analytics to pull data
from disparate sources in order to
deliver a comprehensive CEM solution.
Something that Subex’s Revenue
Operations Center (ROC) is adept at.
The ROC provides the ability to
correlate cross domain information and
extract additional value from near realtime operational data. Using the ROC,
service providers can
• Use Fuzzy Logic to correlate across domains with data challenges
•Analyze and report on information correlated from many domains
• Define analytic classifiers to group customers based on behavior
• Predict future events to devise more cost effective ways of managing pending events
• Set thresholds on cross domain data combinations (eg. Margin)
• Initiate workflow based on KPIs placed at any level above
With its powerful analytics, easy sliceand-dice features, advanced reporting
per contact of $6.00, the call center cost
burden for a single product exceeds
$20 million annually.
Using customer and service data
already collected as part of an advanced
revenue assurance practice, and
combining it with customer behavior
and contact history, the ROC develops
propensity models to predict when a
customer exhibits behavior signaling
a problem. The propensity model can
determine how likely a customer is to call
the call center, and also the time frame in
which the customer is likely to contact.
Not only this, the ROC can also
capabilities with superior management
and operational dashboards, and
efficient case management tools, the
ROC lends itself ideally to CEM.
ROC applications that enable a
positive customer experience
Propensity Analysis
Propensity to Call
Consider a Communications Service
Provider (CSP) that is experiencing a
high volume of expensive inbound calls
to its call center. Using an average cost
Trending and Forecasting
Subscriber growth
Subscriber revenue
Subscriber costs
(content, device)
Subscriber margin
Advanced Analytics
Slice and dice
By geography
By subscriber type
By product/package
What-if (promotions, package changes ,
pricing changes)
Content overpayment associated with fraud,
bad debt, seasonal suspends, promotions
Per Subscriber
Per Subscriber
Content Costs
3rd party
OSS/BSS products,
Mediation Systems
Cost Management
Revenue Assurance
3rd Party Invoice
Partner Settlement
Asset Discovery
Per Subscriber
Risk / Bad Debt
Fraud Management
BI etc.
Credit Risk Management
Route Optimization
Figure 1: Subex ROC Solution
Proactively ensure high margin
customers are content with service
Propensity Analysis
User: Customer Service
Immediately determine how to best
manage upset customers
Positive Customer
Margin Management
User: Business Executives with P&L responsibilities, Economic Sponsors
Ensure customers are getting the
right bundles based on actual
usage; while maximizing margin
New Product Introduction Analysis
User: Marketing, Product Management
Determine whether new products
will be successful and improve
overall customer experience
Figure 2: ROC applications that enable a positive customer experience
enable service providers to act on this
information. By segmenting the pending
contacts and prioritizing them based on
the time frame in which the customer
is predicted to contact and distributing
the investigation and corrective action
across offline resources, the CSP can now
proactively initiate an outbound contact
with a more favorable cost structure,
informing the subscriber an issue has
been noticed and is being investigated.
Offline resources are used to make
corrective action, sometimes in mass,
if a single root cause is the core issue
driver. A final outbound and cost
effective communication to the customer
informs them of resolution. Proactive
communication and action has been found
to be a key driver in customer satisfaction.
Propensity to Churn
All CSPs view the prevention of churn as
an opportunity to avoid lost revenue. For
quite some time using a ‘save desk’ to
quickly evaluate the termination request
and offer the customer incentives to
stay was viewed as a ‘one size fits all’
approach and resulted in CSP’s limited
retention budget being used on low value
‘deal hunters’. Customers requesting
termination were being treated based
on length of stay and product mix only.
However, our experience shows that it is
much more difficult to retain a customer
once the decision has been made to
terminate services. Proactive and
qualified outreach is proven to be more
effective in retaining customers.
Using advanced analytics techniques
to analyze data collected from customer
touch points and quality of service
metrics, the ROC detects patterns that
predict churn. But predicting churn
alone is not a comprehensive solution.
“IF” one should treat and “HOW” one
should treat the customer still remain
unanswered questions.
Combining propensity to churn with
key metrics such as Customer Lifetime
Value (CLV), the ROC enables informed
decisions regarding “WHERE” the
CSP should concentrate proactive
retention efforts. To do this, the ROC
uses classifiers and the developed
knowledge base to categorize customers
based on service utilization behavior,
and recommends the most effective
treatment options based on the next best
activity; and in certain scenarios, even
automating the outreach to the customer.
Margin Management
Imagine a service provider offering IP
based television service to its customers,
experiencing a decline in margin, despite
a steady growth in its customer base.
‘Broadcast’ studio contract terms vary
widely due to variables such as channels,
market area, tiers, bands, promotional
periods, etc. Relying on an outdated
traditional telecom billing platform, the
service provider performs all rating on
aggregated data, completely obscuring
detailed analysis on the relationship
between cost and revenue. An aggregate
profit margin calculation is indicative at
best, but in reality it only contributes to
the confusion.
The ROC corrects this problem by
calculating costs at the most granular level
possible. Next, it combines actual costs
with the bundled revenue amounts at a
subscriber level from the customer billing
system using near real-time operational
data. In addition, it also provides all major
cost and revenue impacting dimensions to
the finance user for further slice-and-dice
analysis. The ROC also provides visibility
of the impact and results of broadly
negotiated contract terms all the way
down to the individual subscriber level,
and calculates the first phase of Customer
Lifetime Value (CLV). In addition, the ROC
also provides all major cost and revenue
impacting dimensions calculating margin
by market, product, content provider, etc.
New Product Introduction Analysis
Consider a CSP that lacks reliable
information on the delivery and financial
performance of newly launched products.
This lack of information creates a situation
where the service provider is ‘shooting
from the hip’, investing in marketing and
/ or sales training in attempts to address
observed symptoms such as service
delivery delays or poor uptake. In any
new product launch it is important to track
progress against targets and overall health
of the product.
This entails collecting data across OSS/
BSS systems, along with the application
of advanced analytics to construct an endto-end picture of customer interaction, in a
bid to make informed business decisions.
The ROC collects real-time quote-tocash data, compares these metrics to
target service level agreements, and
trends these KPIs over time to provide
the service provider complete visibility
into the performance of service delivery
functions and quickly isolate problem
areas requiring attention.
It also tracks financial performance
metrics such as ARPU, AMPU,
acquisition costs, fixed and variable costs
that contribute to real-time monitoring
of product performance and also provide
the necessary elements to track progress
against financial targets. Employing the
ROC’s “What if” modeling capability,
the service provider can now project
the impact of additional investments
focused towards marketing or training,
and how those investments would affect
profitability in the short, medium and
long term.
In summary, personalizing customer
experience requires customer data from
multiple sources, and there’s no dearth
of it in any service provider environment.
What is required is advanced analysis of
that data and the intelligence to make it
actionable - most effectively powered by
Subex Revenue Operations Center (ROC).
Monetizing Service Assurance Monitoring Data
Leverage data already being collected for service assurance to achieve strategic power decision-making
Service assurance monitoring has long
been considered an operational task.
When a failure occurs, service providers
must quickly isolate and resolve the
issue. ROI is typically measured in
terms of improvements in operational
efficiency and the amount of revenue
saved from faster detection and repair.
Forward-thinking organizations
are revising their view. Why not use
this wealth of information to better
understand customers and achieve
strategic power decision making? Why
not find new ways to increase revenue,
reduce churn, or exploit trends? Instead
of simply reacting to issues, why not
proactively prevent bottlenecks and
measure the quality of service before it
impacts the customer?
How do you turn mountains of
monitoring data into the precise
information necessary for improving
decision support? The answer is Service
Assurance Analytics (SAA).
analysis of the multiple protocols for the
network as a whole.
The Power of Service Assurance
SAA is a multi-dimensional approach
to analyzing the rich data set obtained
from network monitoring systems.
It not only helps Operations assure
a great customer experience, but it
also provides the company as a whole
with strategic information for decision
To achieve strategic decision making,
service providers need a robust, unified
service assurance analytics architecture
capable of providing an in-depth, 360°
view of the network as a whole.
Unfortunately, many organizations
have isolated solutions in different
departments which only monitor one
piece of the network, application or
service. It is understandable. Each
department has its own concerns and,
until recently, no one solution was
robust enough to provide in-depth
Understanding Customer Behavior
In today’s complex world, service
providers cannot simply view customer
behavior in terms of minutes called.
They need a true understanding of the
extent to which new services launched
are being utilized. They need to know
how new third party applications
impact network usage. Only an in-depth
understanding of network usage, by
customer, will illuminate options for
strategically maximizing revenue and
controlling costs.
A comprehensive SAA solution will
enable service providers to analyze
customer behavior, comprehend the
user experience, track usage, spot
patterns and assess access to different
application servers. Additionally, it will
track the impact of recommended
changes such as price reductions, new
marketing campaigns, or network reconfigurations.
A true service assurance analytics
• Spans traditional and next generation technologies in a single architecture
•Tracks all KPIs across all dimensions (customer, geography, service, protocol, etc…)
• Offers powerful slice and dice capabilities to drill into any network element
• Meets operational requirements for department-level service assurance
• Fully correlates all data points to reveal important customer, area and usage trends
Reaping the Benefits of Service
Assurance Analytics
SAA solutions offer many ways to
help service providers compete more
effectively in today’s evolving markets.
More importantly, service providers
will be able to predict at what point
service growth will affect service quality
and demand subsequent infrastructure
Using Quality to Strengthen Brand Value
Every service provider could use another
competitive differentiator. As we have
seen in many high-profile advertising
campaigns, service quality can definitely
be a valuable one. However, you must
have measureable metrics and the
ability to interpret and display them. For
example, a service provider who can
prove continued excellence in service
quality with a weekly, monthly, quarterly
and yearly track record has a better
foundation for winning business and
increasing revenue.
Additionally, a service provider could
use SAA and the resulting information
as important content on a customer
portal. Providing access to service
quality information can be a direct
benefit to end customers to help
them optimize and troubleshoot their
own network infrastructure. It could
be a value-add to generate revenue
or offered as a pro-bono capability
to enhance brand value and boost
customer satisfaction.
Churn Reduction
It’s a well established fact that acquiring
a new customer is far more expensive
than retaining an existing one. The first
challenge with any effective retention
strategy is to understand why your
customers leave. The two main reasons
usually cited are poor service quality or a
competitor offering the same service at
a lower cost.
SAA is an invaluable tool for
identifying customers with the highest
likelihood of switching carriers due to
poor service quality. With SAA predictive
modeling, a rule-based engine can be
set to look for customers experiencing
multiple quality issues.
Additionally, customers who report
issues to customer care are more likely
to switch if the issue does not get
resolved. Customers identified through
SAA as high risk for churn can be
proactively contacted by customer care
with reassurance that their problems are
being fixed.
Of course, SAA will also alert the
operations staff to the service issue and
the network elements causing it.
SLA Verification
Service verification is an important
element of business today.
Interoperability testing is needed before
working with interconnecting networks
and large customers are increasingly
requiring SLAs. With SAA, it is possible
to set baselines for SLA metrics and
very quickly determine if there were any
service turn-up problems.
Using SAA, a service provider can
routinely verify KPIs to answer two
essential financial questions:
•Are obligations to customers being met (not incurring penalties)?
•Are interconnected partners meeting their obligations (ensuring revenue or identifying potential areas of cost)?
Furthermore, accurate SLA verification
means a service provider can avoid
playing the “blame” game when it comes
to potential network problems. Having
clear, definitive and shareable information
on network and service behavior enables
network peers to collaborate in problem
resolution, making them true partners
instead of simply an interconnection point
or worse, an enemy.
Today’s SAA solutions not only provide
operations with the data it needs to
ensure high service quality, but also
offer key insight into the company as a
whole. By investing in an SAA solution
that tracks the right information and
provides powerful tools to sort through
it, a service provider can easily turn
mountains of monitoring data into
decision-making intelligence. These
are a few examples of how to increase
revenue, reduce customer churn and
exploit trends. Once in your hands, you
will find endless ways to monetize your
SAA solution.
Assuring a high quality customer
experience in converged IP networks
CEM is here, what’s next?
Customer Experience Analytics
• According to our research, operators lose from 5%
to 10% of revenues due to different problems within
the network, such as configuration, sw/hw, capacity
and user errors.
• There is too much data in too many silos to be analysed manually.
• By deploying latest analytics tools, operators are able to get a holistic view of the customer experience, and isolate and react to problems causing losses.
• Customer Experience Analytics (CEA) represents
a move toward a more imaginative and sophisticated Figure 1: Top-level view into discovered revenue losses.
approach to customer experience management.
From CEM to CEA
Depending on who you ask, you
may get a very different answer
as to what ‘Customer Experience
Management’ (CEM) actually means in
the telecommunications business. Many
times a very quality oriented approach is
taken – it is about what kind of service
you are providing to your customers.
The holy grail to operators is a single
centralised solution that would handle
all possible aspects of CEM in real-time.
Therefore, it is not very surprising that
in many offerings today CEM is often
referred as the central OSS / BSS element
that sits in the middle of operations, and
manages all communication channels,
other processes and controls other OSS/
BSS systems and network elements. The
phrase ”Let the customer be in charge” is
often used.
We also believe that customer comes
first, but realise that having a centralised
CEM system controlling all aspects of
customer experience is simply not a
reachable target. There are too many
critical processes and elements already
in place. Building a new master system
would be too massive a monoblock to
build, integrate and maintain.
Operators and service providers will
benefit from a distributed approach
where elements of CEM functionality are
divided to various OSS / BSS systems
managing the individual silos, and the
consolidated view into the ‘customer
experience’ across different channels is
formed via a central analytics component.
The innovation behind this approach is
that there is no need to replace any of the
existing OSS / BSS systems, while the
central analytics will provide substantial
business benefits, such as new revenues
and increased profitability, by comparing
the information they produce.
The big picture
Let’s take a closer look at the set-up
and concrete benefits of the distributed
approach – Customer Experience
The set-up presented here is from a
live customer installation, and although
it does not contain all customer contact
channels, it gives concrete examples
of the benefits achieved through CEA
approach. The main data sources for this
set-up are:
• Service Assurance tools aim to ensure
and improve the quality of service
and typically contain tools to execute
corrective actions to network. In this
context Service Assurance systems
are used to find details of actual traffic
events from the network.
• Billing systems ensure that all usage
is invoiced effectively. Many systems
support also dynamic tariffing and are
therefore able to change billing logic
in real-time. Revenue Assurance
(RA) systems are an extension of
billing, designed to find missed
revenue opportunities through CDR
reconciliation and business process
assessment. In this example, billing
and RA systems are feeding invoicing
data and discrepancies in billing
process to CEA.
• Customer data that contains
information about customers and their
interaction. In this example, Customer
databases feed customer data, such as
demographics and tariffing information
to CEA.
Figure 2: The interfaces and types of analysed
data of the example set-up.
One important aspect is the integration
process. The demand for real-time
management capability makes the
integration of CEM tools a very
challenging task. However, in our
experience the CEA component does
not have to be real-time. The best
results can be achieved when CEA
component takes care of the logic and
task prioritisation, and the silo-based
CEM systems take care of issues
demanding immediate actions.
Near real-time architecture makes
the integration process much easier
and faster. With appropriate tools and
processes, such integrations can be
implemented in just a few weeks.
The Benefits
There are numerous benefits that result
from Customer Experience Analytics.
Presented here are just a few:
Prioritisation of Business Decisions:
As discussed earlier, service assurance
systems are mostly concerned with
real-time quality management. The
challenge is that there are numerous
simultaneous incidents ongoing in the
network, and the network operations
team does not have the information to
identify which of them has the largest
revenue impact. CEA reponds to the
challenge by combining traffic and
revenue information, and produces
priority lists that sort the most important
locations, services and customers. With
the intelligence, operators are also able
to proactively monitor the experience of
their corporate customers and keep up
with the SLAs.
Additional revenue can be found by
looking at the failed service delivery
attempts and, according to our research,
it can be increased by 2%. This requires
a combination of unsuccessful delivery
attempts and pricing information. CEA
Figure 3: Experience and revenue trends for dynamically detected micro-segment of high-profit customers.
tools are capable of finding this type of
information and feeding it back to the
service assurance tools to be sorted out.
Improved correction accuracy
through Revenue Assurance systems:
According to our studies, over 20%
of failed service access attempts
are caused by problems in billing
authorisation or service activation.
Many of them could be fixed by using
Revenue Assurance systems, which are
tightly integrated with billing process
and contain tools to detect root causes.
CEA contributes to the use case by
identifying such events and prioritizing
them according to business priorities.
Management of convergent offering:
Management systems of different
technologies are easily integrated to
CEA, which makes it an ideal tool for
managing convergent portfolio. For
example, with CEA tool operator’s
business manager and sales &
marketing can see how customers
are consuming the services across
the portfolio and make adjustments in
tariffing to optimise the profitability. In
real customer cases it was also noticed
that the number of information requests
to the accounting team dropped by
95% after the introduction of the CEA
tool, which turns out to be a substantial
annual cost saving.
Business process management and
understanding profitability: Due to its
multi-silo nature, CEA brings visibility
into the whole business process. It
combines service usage with actual
billing and tariff plans, and is therefore
often used to analyse the profitability
of individual customers. The incidents
involving the most profitable customers
can be prioritised in CEA and managed
real-time in silo-based CEM systems.
Putting the Actual Customer Experience
Back into Customer Experience Analytics
The battle for the customer has driven
providers to create an arsenal of customer
analytic capabilities, including predictive
and behavioral modeling, value scoring,
net promoter score (NPS) ratings, and
customer reporting. Each in their own
way tries to understand or predict how
customers experience their services and
make decisions.
The focus on customer experience
management highlights the simple notion
that the customers’ actual experience
is an important factor in how customers
feel about their provider, whether they
may churn, or whether they will upgrade
to new devices and services, or whether
they will go “all-in” and become a Triple or
Quad play customer.
The notion that the actual experience
matters points to a gap in the analytic
arsenal. The current analytic capabilities
are good and useful, but they miss one
important ingredient - the ability to drill
down to each customer’s individual,
actual experience at every measurable
touch point to create an analytic that truly
reflects experience.
Customers “experience” a set of
interactions and outcomes from a highly
complex, end-to-end processes ranging
from marketing to provisioning to billing
and care. Interactions and outcomes
include receiving marketing, receiving
and paying bills, calling to complain or
request help, among others. Each of these
interactions represents experiences that
are positive, neutral, or negative – and
these experiences accumulate to shape
the customers’ views and actions.
From the provider’s perspective,
managing a highly complex and conditional
mega-process, from marketing-to-care, is a
daily grind in which they seek to optimize
and drive accuracy in service delivery, billing,
and customer management operations.
Customers only care that their provider
offers and delivers a set of services that
are there when they want them, bills
them accurately, and resolves any issues
rapidly or with due financial compensation
if those expectations are not met.
When customer expectations are not
met, customer cost and risk increases
through call loading to the call center,
churn, or the reduced ability to upsell or
cross-sell across products. In some cases,
such as bill shock, billing “surprises”
create extraordinary risk – even if the bill is
Customer expectations and experiences
“live” in each individual interaction across
this marketing-to-care process. How do
you know what those experiences are?
Or where negative experiences occur?
Or where risk becomes sufficiently high
that, unless addressed, will impact the
These questions may indicate that a
more process-centric customer experience
analytic is needed. This approach is
useful when it leverages the existing
data and analytic infrastructure; meaning
you do not have to build an entirely new
infrastructure. A process-centric approach
would enable you to analyze the actual
customer experience as they “flow”
across marketing, order management,
provisioning, billing, and care.
To build a process-centric customer
analytic, four inter-related capabilities are
1.Data analytics: The ability to acquire
and correlate the data across the
different systems that underpin the
marketing-to-care process
2.Process Discovery: The ability to
capture and understand the business
rules within and across each sub-process
that govern the overall process and often
determine the experiences and outcomes
that the customer experiences
3.Root cause analytics: The ability to
identify detailed causal issues that you
can systematically detect and address to
minimize risk
4.Business collaboration: The ability
to perform an interactive, collaborative
discovery-to-analytic process among
business SME’s and analysts, avoiding the
need of a waterfall-based requirementsto-development process in an area where
the requirements and underlying risks
may not be well understood.
Data Analytics
At the core of a process-oriented customer
experience analytic is the data needed to
conduct a detailed and robust analytics.
Let’s start with three important realities:
1.Providers govern an immense amount
of complex and fluid data that is stored
and managed in a set of systems and data
warehouses that underpin the order-tocash process
2.Data complexity may be further
complicated by post-merger realities in
which parallel systems address different
customer or market segments
3.“Systems of record”, such as complaint
data, may have substantial integrity issues
that complicate more simple, singlesource analytics.
For a tier 1 Quad play provider, we
acquired and correlated data from more
than 15 systems to fully model the trouble
management process and identify the key
results and root causes that were both
driving up costs and creating untenable
customer risk.
Two approaches are viable: a data
warehouse approach where all relevant
data must be centralized in a warehouse;
or a data federation approach where
the analytic can pull from, correlate, and
synchronize data from the operational
systems, to include smaller warehouses
to store results or other “reduced”
data. This choice can dictate the overall
program’s financial merits as the large
warehouse approach can be costly – and
given the unclear requirements at onset –
can struggle to develop the right queries
to produce highly valuable results. The
federated approach gives providers
the greatest speed and flexibility,
but requires a very robust analytic to
successfully correlate disparate data to
re-create and analyze the customer’s
experience as they travel across
Process Analytics
In order for a process-oriented customer
experience analytic to measure the
accuracy and strength of the marketingto-care process, the analytic needs to
capture and analyze the business rules
that govern the process. This includes:
1.Service delivery rules and workflow that
governs eligibility, timing, and success
of each step of the process from order
management to provisioning to care
2.Billing rules that dictate single service
and cross-discounted rates, including
conditional rules in play if customers are
part of a marketing program
3.Customer rules that govern eligibility,
signal high-value or low-value customers,
or indicate the extent of transactions
(e.g. number of subsequent or one-time
For a tier 1 cable provider, we audit each
order and provide routine, rules-based
auto-correction so that order errors do not
impact activation or cause billing errors.
This is not an easy exercise as these
rules can be hidden or opaque, or only
understood by the business SME.
Process discovery is a critical step to
analyses that are factual and drill down to
individual experience.
Root Cause Analytics
“What” is happening can be revealed
by aggregated reporting on different
customer segments and progress to
customer KPIs, or it can be revealed by
the volume and nature of calls to the call
center and web interactions. “Why” it
is happening and what common causes
exist that enable efficient and productive
improvement is another matter. Without
root cause analytics, the provider can
know something is wrong, but may not
be able to isolate on what to fix – or
as importantly – determine which root
causes are creating the greatest risk and
thus are the most important to attack.
C 2010 Martin Dawes Analytics
Within root cause analytics, pattern
analysis, such as dimensional analyses,
can be used to rapidly understand
outliers that represent areas of acute risk,
then re-create the process to identify the
specific cause.
Business Collaboration
For most providers, a process-oriented
customer experience analytic represents
a new type of analytic – more akin to
revenue assurance from a process
perspective than traditional customer
analytics. With that in mind, providers
need an agile, adaptive methodology in
which the business SMEs interactively
discover, learn, and analyze - adapting
the analyses in real-time as risks are
uncovered, common root causes
identified, and a set of optimized
analytics are produced. This approach
reduces the analytic development time
frame from 12+ months to less than
3 months, ensures an active, learning,
collaborative environment for business
SMEs across business functions, and
produces the greatest ROI potential –
meaning, substantially less time and cost
with greater value.
For a tier 1 Quad play provider, we
identified design defects in the order-toprovision workflow in less than 1 month
and put in place a working control on the
area of greatest risk in 3 months.
Building the Case
Winning the customer battle is
Contact Us
paramount. It is the major conduit to
improving ARPU and lifetime value
performance, minimizing churn, and
reducing the cost of revenue. Analyzing
and making targeted improvements
to your operations that directly impact
the actual customers’ experience is
a business lever that has generally
lagged in the market. This is because
investments - and associated gains
– were prioritized to BI and predictiveoriented analytics. But with some of
those gains already in place, the crucial
next step for providers is to execute a
process-oriented approach that builds
on your existing analytic infrastructure,
can provide a factual and detailed
understanding of the actual customers’
experience – the experience that drives
impressions, decisions, and ultimately,
customer value.
Capitalize on advantages of convergent real-time solution
Reduce revenue leakage to less than 0.005%
Mobile network operators around the world are seeking for systems with
real-time convergent capabilities, going far beyond those of traditional billing
systems. Multi-node real-time rating sets the benchmark for profitable
customer-centric marketing management. Rating, charging and billing for
all services, customer segments and payment methods must be handled
within one unified rating and billing environment to meet operators’ needs.
Managing all activities within the real-time convergent billing system
enables a centralized subscriber view. This new view leads to better
and more personalized service offerings, higher customer retention and
optimized revenue collection.
Operators relying on Orga Systems’
fully convergent charging and billing
platform can strengthen their leading
market position. Offering next
generation services on a unified
platform streamlines operations for
operators’ successful, multi-service
growth strategy. Recently, one of
Europe’s leading communication service
providers has deployed Orga Systems’
real-time charging and billing platform
OPSC® Gold. Fully convergent rating and
charging consolidates the CSP’s billing
infrastructure and enables the first
real convergent family offer. Reducing
revenue leakage to almost “zero” has
unlocked millions of additional revenue.
OPSC® Gold enables operators
to successfully launch new, nextgeneration services with a very
aggressive time to market. This helps
operators to clearly differentiate
from competitors. The launch of
OPSC® Gold enables a market leading
European CSP to capitalize on the
additional advantages of Orga Systems’
convergent real-time technology.
Revenue leakage reduced from about
7% to less than 0.005% in the high
ARPU consumer postpaid segment.
The Need
Being the market leader in a highly
competitive market made an innovative
yet cost-effective business strategy
essential to the European operator
which has deployed OPSC® Gold
recently. New offerings, tailored to the
target customer segments, were in
focus to win new customers. Flexible
service options and an increased
transparency in the area of tariffs and
service consumption were aimed at
targeting a “new” customer experience.
In addition, consolidation within the
rating and billing infrastructure needed
to provide higher efficiency and to bring
down the overall TCO.
In general, performance and
capability limitations as well as the
need to prepare for the next-generation
networks and services force a strategic
decision for the future. In view of
the serious danger of losing revenue
and generating bad user experience,
operators require a short time solution,
securing a long term and convergent
roadmap in addition. The only answer
to this is one single real-time system
for pre- and postpaid customers with a
unified rating and billing environment
for all services to assure future ability
to differentiate from the competition
via tailored tariffs, innovative service
bundles and campaigns.
The Challenge
The targets in this strategically
important European project meant
transforming the legacy billing
infrastructure into a consolidated and
future-proof, real-time architecture.
To be ready for advanced services
in next-generation networks, the
project aimed at enhancement of
customer experience though by-passing
limitations in the existing rating and
billing systems and implementation of
new convergent service options and
bundles as well as services. Minimize
revenue leakage by improving the
rating performance, efficiency and
accuracy for postpaid subscribers and
consolidating the rating and charging
environment for pre- and postpaid
subscribers and cost reduction had to
be achieved.
Implementing a fully convergent
real-time rating and billing platform
within a customer’s existing system
infrastructure effects the most sensible
environment that is in control of
the operators’ revenue stream. This
requires a detailed planned and phased
approach to guarantee uninterrupted
network operation.
The Solution
Matching all requirements for real-time
performance, convergence support
and market expertise makes Orga
Systems and OPSC® Gold the number
one choice. The deployment of OPSC®
Gold enables real-time interaction
with all postpaid subscribers that use
data service bundles. With instant
notifications, a new and compelling
customer experience is delivered to
the subscribers. To focus on new and
profitable customer segments, the first
true convergent offer in the market
addresses the family segment. Using
the concept of shared balances in
OPSC® Gold, free minutes and SMS
bundles are available to any family
member. This innovative offering
successfully increases usage and
attracts new subscribers.
Migrating more than a thousand
postpaid tariffs to this new rating
platform in less than 6 months also
showed immediate results. Wrong
bills, customer refunds and calls to the
customer-care centers were reduced
drastically. In the following months,
revenue loss in the postpaid segment
decreased from about 7% to less
than 0.005%, generating savings of
millions of euros each month. With the
deployment of OPSC® Gold, a positive
return on investment was achieved in
months rather than in years.
The implementation of Orga Systems’
real-time billing system OPSC® Gold
successfully ends up with managing
all subscribers in one single system.
Migrating the whole customer base and
all related tariffs enables operators to
offer more sophisticated hybrid tariffs.
Combining pre- and postpaid payment
methods for new offerings, including
promotions and loyalty campaigns
can boost brands’ attractiveness and
Our Sponsors
Nokia Siemens Networks is a leading
IBM has spent nearly $12 billion in the past
SAS, is the global leader in business
global enabler of telecommunications
three years to deepen our capabilities in
analytics software and services. With nearly
services. With its focus on innovation
the telecommunications industry through
three decades of communications industry
and sustainability, the company provides
a combination of internal technology
experience in over 200 global companies,
a complete portfolio of mobile, fixed and
developments and strategic acquisitions
converged network technology, as well as
such as Micromuse, Vallent, MRO, SolidDB,
SAS helps CSPs to:
nIntegrate the customer view to
professional services including consultancy
Cognos, and SPSS. IBM’s Service Provider
understand the total customer experience.
and systems integration, deployment,
Delivery Environment is a telecommunications
maintenance and managed services. It is
industry framework that can be used as a
models based on customer insights.
one of the largest telecommunications
blueprint to help accelerate creation and
hardware, software and professional
delivery of new services, expand the partner
products, and services.
services companies in the world. Operating
ecosystem, and integrate management
in 150 countries, its headquarters are in
of services with business processes. Our
factor constraints like policies/budgets.
Espoo, Finland.
solutions, selected by over 1,000 providers and
20 of the top 20, include software, hardware,
efficiencies by analyzing QoS, network services and research across OSS, BSS,
and IT performance and service costs.
analytics and optimization, service delivery,
and device/asset management domains.
SAS gives network operators around the world THE POWER TO KNOW®.
more targeted and granular
profitability of customers, campaigns to objectives and customer issues and improve Founded in 1973, Compuware provides
Arantech’s CEM solutions provide mobile
SAP for Telecommunications is a market-
software, experts and best practices to
operators with a unique customer insight, a
leading solution that supports end-to-end
ensure applications work well and deliver
rich experience discovery and enables them
enterprise business processes for wireline,
business value. Compuware optimizes end-
to take proactive management action on
wireless, cable, broadband, satellite, and
to-end application performance for leading
real time experience events. All solutions
other multiservice operators. With 81% of
businesses around the world, including 46
deliver a rapid and strong ROI by identifying
the top 500 telecommunications service
of the top 50 Fortune 500 companies and 12
customer-centric issues (‘the experience
providers as a customer base and with
of the top 20 most-visited U.S. web sites.
gap’) in real time and enable behavioural
proven success stories, SAP provides a
In telecommunications, Compuware
segmentation of a customer base which
compelling solution for your business.
combines end-user experience monitoring
today is not possible through existing
With SAP’s world class business process
and business service management with
Business and Operational Support Systems
platform, you can quickly adapt to market
real-time subscriber intelligence capabilities
(B/OSS). Arantech has 39 customers
demands and embrace new business
to deliver end-to-end visibility into the
including mobile operators from four out of
models in a fast changing convergent
customer data experience. This in-depth
the six largest mobile operator groups in the
visibility and fault isolation allows operators
to deliver superior quality of service to their
For more information, visit
For more information visit
ment World in Nice, France.
Convergys Smart Revenue Solutions
Subex is a leading global provider of
Empirix is the leading provider of service
Convergys has 25 years’ experience
Operations and Business Support Systems
quality assurance solutions for new IP
providing Smart Revenue Solutions to the
(OSS/BSS) that empowers communications
communications. Since 1992, Empirix has
telecoms, cable, satellite, broadband, and
service providers to achieve competitive
led the market in innovation and expertise
utilities markets. With its convergent billing
advantage through Business Optimization
for IP testing and application performance
and customer care solutions, Convergys’
and Service Agility.
management. Its widely acclaimed
future-proof solutions enable clients to
3/20/2010 2:06:29 PM
Subex offers a range of OSS/BSS solutions
Hammer(tm) Test Engine(tm), with patented
offer personalised, innovative services and
and managed services that have been trusted
technology is the acknowledged global
delivery, build customer loyalty, lower costs,
by over 200 service providers through 300+
standard for validating the quality of IP
and grow revenues.
implementations across 70 countries.
Convergys is a global leader in
Its product portfolio powers the Revenue
networks, systems and applications. The
world’s largest service providers depend on
relationship management enabling
Operations Center (ROC), a concept it
Empirix’s solutions to maintain the quality
leading companies in over 70 countries to
pioneered and its solutions enable new
of the user experience for business-critical
deliver exceptional customer experience.
service creation, subscriber-centric fulfillment,
voice, data, video and mobile services. With
Convergys is globally trusted and proven
provisioning automation, revenue assurance,
Empirix, customers can increase revenues,
in the market, reflected by the fact that its
cost management, data integrity management,
reduce customer churn and cut support
top 40 telecoms clients have been with
fraud management and partner settlement.
Convergys for more than 25 years.
To know more about Subex, please visit
For further information, please visit
About Aito Technologies Oy
MDA is a global process analytic company
As the pioneer of GSM billing, Orga Systems
Founded in 2006, Aito Technologies Oy
that enables our customers to maximize
has gained highly qualified expertise in real-
is a developer of an innovative Customer
cash from and optimize business operations;
time charging and billing.
Experience Analytics (CEA) product suite
notably the complex and critical order-
for network operators and digital service
to-cash process. Through our Lavastorm
based solutions for customer billing and
providers. As the brainchild of a group
Analytic Platform and associated Adaptive
administration in mobile telecommunication
of telecom experts, Aito brings a unique
Modeling capability, we deliver powerful
services. It sets important milestones for
approach to the market.
solutions, such as revenue assurance,
the industry regularly to further expand its
fraud management, customer experience
leading position.
Aito software, which effectively combines
Orga Systems focuses on real-time
customer usage, experience and business
analytics, service delivery analytics, trade &
information, simplifies the understanding
settlement analytics, migration assurance,
InCore is currently the fastest data technology
of the customer experience environment.
compliance and risk analytic, and dealer
worldwide with regards to access speed.
It provides key business management
commission analytics. We deliver these
Mobile operators need future-proof billing
stakeholders with the richest end-to-end view
solutions in the communications, media,
systems which offer clear service and cost
of their customers, in an easy-to-use form,
energy, and utility markets, helping our
within minutes. Aito is used to manage the
customers optimize current operations and
experience and behaviour of tens of millions
de-risk the transition to new products and
platform OPSC Gold guarantees their
of customers around the world.
new business models.
profitable future growth.
Orga Systems’ high-performance database,
The fully convergent real-time billing