15-10-2016, 12:34 PM
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Executive summary
How much can companies in the telecommunications industry
benefit from “big data”? That’s a critical question. Every operator is
searching for new ways to increase revenues and profits during a time
of stagnant growth in the industry, but few have demonstrated the
capabilities needed to make the most of this new technology.
That’s why operators seeking to make initial inroads with big data are
advised to avoid the usual top-down approach, which sets up a business
problem to be solved and then seeks out the data that might solve it.
This method does have benefits, but it is unlikely to lead to any
serendipitous and surprising results — and it is difficult to execute
until a company has demonstrated mastery in its use of data.
Instead, operators should begin with the data itself, experimenting
with what they have on hand to see what kinds of connections and
correlations it reveals. This process must be carried out quickly and
iteratively, without the overbearing oversight from which so many
business development projects suffer. If it’s done right, what emerges
can form the basis for more efficient operations and more effective
marketing. At its best, this bottom-up method can give operators a
more complete, transparent view of customers, enabling new and
more profitable ways of capturing and retaining them.
Opportunity awaits
The virtues of big data have been touted in hundreds of articles and
reports during the past few years. Yet the benefits have proven elusive for a
lot of companies. Indeed, some analysts already see a considerable level of
disillusionment regarding big data — an umbrella term encompassing the
new methods and technologies for collecting, managing, and analyzing in
real time the vast increase in both structured and unstructured data —
because too many efforts to implement the technology have not lived up
to the high expectations triggered by the hype.
This is particularly true in the telecom sector. Most operators conduct
analytics programs that enable them to use their internal data to
boost the efficiency of their networks, segment customers, and drive
profitability with some success. But the potential of big data poses a
different challenge: how to combine much larger amounts of information
to increase revenues and profits across the entire telecom value chain,
from network operations to product development to marketing, sales,
and customer service — and even to monetize the data itself.
The typical advice offered to telecom operators — indeed, to companies
in every industry — is to take a top-down approach by focusing on
specific business problems that big data might solve, and then gathering
the data needed to solve them. But the challenge in this strategy is
twofold: First, the business problem often exceeds the capacity of the
available data to solve it, and second, the process of gathering the right
data to help solve the problem is poorly understood by many companies.
To circumvent this problem, companies should begin with the inverse
approach, viewing the opportunity from the bottom up. In this scenario,
you examine the data currently available, and only then determine the
business problems the data might help solve, with the help of any
additional structured or unstructured data that might be needed (see
Exhibit 1, next page). We believe the best way to get started with this
approach is through pilot programs. Keeping initial expectations
reasonable, a dedicated team gathers all available data, analyzes it to
allow new and unexpected opportunities to reveal themselves, and then
tests the efficacy of the results in solving one or more real business
problems. This tactic offers telecom operators and others a concrete
The promise of
big data for telecom
Big data promises to promote growth and increase efficiency and
profitability across the entire telecom value chain. Exhibit 3, next page,
shows the benefits of big data over the opportunities available through
traditional data warehousing technologies. They include:
• Optimizing routing and quality of service by analyzing network
traffic in real time
• Analyzing call data records in real time to identify fraudulent
behavior immediately
• Allowing call center reps to flexibly and profitably modify subscriber
calling plans immediately
• Tailoring marketing campaigns to individual customers using
location-based and social networking technologies
• Using insights into customer behavior and usage to develop new
products and services
Big data can even open up new sources of revenue, such as selling
insights about customers to third parties.
From the bottom up
The essence of the bottom-up approach lies in gathering together all the
data available to the operator, both internal and external; applying
software tools to process, analyze, and make sense of it; and then
determining what can be done with the results. The key is to allow the
data to “speak for itself,” bringing out not just the obvious correlations
and connections, but the unexpected ones as well. Data has no agenda.
It’s incorruptible, it has no boss, it doesn’t want to be promoted, and it
doesn’t quit. Many types of data are potentially available to operators —
though it is unlikely that operators will have all these sources at this
stage — and certain sets of data might be combined to open up new
business opportunities in areas such as campaign marketing and fraud
prevention (see Exhibit 4, next page).
• Enhanced recommendation engine: Operators could generate more
accurate and personalized offer recommendations for existing
individual subscribers by combining internal structured data, such as
how and where each subscriber uses his or her phone, with external
unstructured or semi-structured data from social media platforms
(for example, Facebook and Twitter). This information on customer
preferences and behavior could enable the recommendation engine
to match price plans and offer attractive add-ons, such as sports
add-ons for fans and free audiobook offers for commuters. As a
result, operators could lower the costs of retaining existing
subscribers and identify cross- and up-selling opportunities to
improve average revenue per user and reduce churn.
• Improved fraud management: By correlating internal location, usage,
and account data with external sources such as credit reports,
operators could significantly increase the detection of fraudulent
activity such as looping or call forwarding on hacked PBXs (private
branch exchanges), or fraud involving the swapping of SIM cards,
and improve the overall accuracy and efficiency of their efforts to
recognize patterns of fraudulent behavior.
Piloting big data
The eventual goal of big data is to combine and correlate every
information source to generate a holistic, transparent, end-to-end view
of all the interactions every individual customer or household has with
the operator. But to really leverage big data, operators must radically
modify how they gather, verify, learn from, and make use of the
information at their disposal. That means completely rethinking the
purpose of the traditional corporate pilot program, long dependent on
uncovering incremental opportunities by setting rigid, predetermined
goals and hoping to attain them through laborious and time-consuming
stage-gate and approval processes.
Instead, operators must learn from companies such as Google and
Facebook, where data is king and virtually every product decision flows
from what the available data says about customers and how it can be
used. The big-data pilot program should be made up of teams of people
from all over the company — including network operations, IT, product
development, marketing, finance, and perhaps even customers — who
can bring their particular expertise to analyzing the data in new and
different ways. They must know what it means to “play around” with
the data, testing various combinations and correlations to see what
works and what doesn’t.
This process must be agile, iterative, and quick. Piloting teams need
to conduct numerous tests on the data, learn from their mistakes and
false starts, and move to the next test. They must avoid the overly
structured mind-set that can drag pilot programs out for months and
years, carefully vetting incremental improvements at every level of the
corporate hierarchy. And they must speed up the evolutionary process
of development, allowing the fittest and most valuable results to emerge
quickly.
Executive summary
How much can companies in the telecommunications industry
benefit from “big data”? That’s a critical question. Every operator is
searching for new ways to increase revenues and profits during a time
of stagnant growth in the industry, but few have demonstrated the
capabilities needed to make the most of this new technology.
That’s why operators seeking to make initial inroads with big data are
advised to avoid the usual top-down approach, which sets up a business
problem to be solved and then seeks out the data that might solve it.
This method does have benefits, but it is unlikely to lead to any
serendipitous and surprising results — and it is difficult to execute
until a company has demonstrated mastery in its use of data.
Instead, operators should begin with the data itself, experimenting
with what they have on hand to see what kinds of connections and
correlations it reveals. This process must be carried out quickly and
iteratively, without the overbearing oversight from which so many
business development projects suffer. If it’s done right, what emerges
can form the basis for more efficient operations and more effective
marketing. At its best, this bottom-up method can give operators a
more complete, transparent view of customers, enabling new and
more profitable ways of capturing and retaining them.
Opportunity awaits
The virtues of big data have been touted in hundreds of articles and
reports during the past few years. Yet the benefits have proven elusive for a
lot of companies. Indeed, some analysts already see a considerable level of
disillusionment regarding big data — an umbrella term encompassing the
new methods and technologies for collecting, managing, and analyzing in
real time the vast increase in both structured and unstructured data —
because too many efforts to implement the technology have not lived up
to the high expectations triggered by the hype.
This is particularly true in the telecom sector. Most operators conduct
analytics programs that enable them to use their internal data to
boost the efficiency of their networks, segment customers, and drive
profitability with some success. But the potential of big data poses a
different challenge: how to combine much larger amounts of information
to increase revenues and profits across the entire telecom value chain,
from network operations to product development to marketing, sales,
and customer service — and even to monetize the data itself.
The typical advice offered to telecom operators — indeed, to companies
in every industry — is to take a top-down approach by focusing on
specific business problems that big data might solve, and then gathering
the data needed to solve them. But the challenge in this strategy is
twofold: First, the business problem often exceeds the capacity of the
available data to solve it, and second, the process of gathering the right
data to help solve the problem is poorly understood by many companies.
To circumvent this problem, companies should begin with the inverse
approach, viewing the opportunity from the bottom up. In this scenario,
you examine the data currently available, and only then determine the
business problems the data might help solve, with the help of any
additional structured or unstructured data that might be needed (see
Exhibit 1, next page). We believe the best way to get started with this
approach is through pilot programs. Keeping initial expectations
reasonable, a dedicated team gathers all available data, analyzes it to
allow new and unexpected opportunities to reveal themselves, and then
tests the efficacy of the results in solving one or more real business
problems. This tactic offers telecom operators and others a concrete
The promise of
big data for telecom
Big data promises to promote growth and increase efficiency and
profitability across the entire telecom value chain. Exhibit 3, next page,
shows the benefits of big data over the opportunities available through
traditional data warehousing technologies. They include:
• Optimizing routing and quality of service by analyzing network
traffic in real time
• Analyzing call data records in real time to identify fraudulent
behavior immediately
• Allowing call center reps to flexibly and profitably modify subscriber
calling plans immediately
• Tailoring marketing campaigns to individual customers using
location-based and social networking technologies
• Using insights into customer behavior and usage to develop new
products and services
Big data can even open up new sources of revenue, such as selling
insights about customers to third parties.
From the bottom up
The essence of the bottom-up approach lies in gathering together all the
data available to the operator, both internal and external; applying
software tools to process, analyze, and make sense of it; and then
determining what can be done with the results. The key is to allow the
data to “speak for itself,” bringing out not just the obvious correlations
and connections, but the unexpected ones as well. Data has no agenda.
It’s incorruptible, it has no boss, it doesn’t want to be promoted, and it
doesn’t quit. Many types of data are potentially available to operators —
though it is unlikely that operators will have all these sources at this
stage — and certain sets of data might be combined to open up new
business opportunities in areas such as campaign marketing and fraud
prevention (see Exhibit 4, next page).
• Enhanced recommendation engine: Operators could generate more
accurate and personalized offer recommendations for existing
individual subscribers by combining internal structured data, such as
how and where each subscriber uses his or her phone, with external
unstructured or semi-structured data from social media platforms
(for example, Facebook and Twitter). This information on customer
preferences and behavior could enable the recommendation engine
to match price plans and offer attractive add-ons, such as sports
add-ons for fans and free audiobook offers for commuters. As a
result, operators could lower the costs of retaining existing
subscribers and identify cross- and up-selling opportunities to
improve average revenue per user and reduce churn.
• Improved fraud management: By correlating internal location, usage,
and account data with external sources such as credit reports,
operators could significantly increase the detection of fraudulent
activity such as looping or call forwarding on hacked PBXs (private
branch exchanges), or fraud involving the swapping of SIM cards,
and improve the overall accuracy and efficiency of their efforts to
recognize patterns of fraudulent behavior.
Piloting big data
The eventual goal of big data is to combine and correlate every
information source to generate a holistic, transparent, end-to-end view
of all the interactions every individual customer or household has with
the operator. But to really leverage big data, operators must radically
modify how they gather, verify, learn from, and make use of the
information at their disposal. That means completely rethinking the
purpose of the traditional corporate pilot program, long dependent on
uncovering incremental opportunities by setting rigid, predetermined
goals and hoping to attain them through laborious and time-consuming
stage-gate and approval processes.
Instead, operators must learn from companies such as Google and
Facebook, where data is king and virtually every product decision flows
from what the available data says about customers and how it can be
used. The big-data pilot program should be made up of teams of people
from all over the company — including network operations, IT, product
development, marketing, finance, and perhaps even customers — who
can bring their particular expertise to analyzing the data in new and
different ways. They must know what it means to “play around” with
the data, testing various combinations and correlations to see what
works and what doesn’t.
This process must be agile, iterative, and quick. Piloting teams need
to conduct numerous tests on the data, learn from their mistakes and
false starts, and move to the next test. They must avoid the overly
structured mind-set that can drag pilot programs out for months and
years, carefully vetting incremental improvements at every level of the
corporate hierarchy. And they must speed up the evolutionary process
of development, allowing the fittest and most valuable results to emerge
quickly.