Strategyand Benefiting From Big Data a New Approach for the Telecom Industry

8/16/2019 Strategyand Benefiting From Big Data a New Approach for the Telecom Industry

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A new approachfor the telecomindustry

Beneting from big data


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Contacts About the authors

Beirut

Bahjat El-Darwiche [email protected]

Dubai

David Tusa [email protected]

Düsseldorf

Roman Friedrich [email protected]

Frankfurt/Dubai

Olaf Acker

[email protected]

Madrid

José Arias Partner+34-91-411-5121 [email protected]

Milan

Luigi Pugliese [email protected]

Moscow

Steffen Leistner [email protected]

Mumbai

Jai Sinha Partner+91-22-6128-1102 [email protected]

New York

Christopher Vollmer

[email protected]

Paris

Pierre Péladeau [email protected]

Riyadh

Hilal Halaoui [email protected]

São Paulo

Ivan de SouzaSenior [email protected]

Sydney

Steven Hall [email protected]

Tokyo

Toshiya Imai

[email protected]

This report was originally published by Booz & Company in 2013.

Olaf Acker is a partner with Strategy& basedin Frankfurt and Dubai.He focuses on businesstechnology strategy andtransformation programsfor global companies inthe telecommunications,media, and high-techindustries.

Adrian Blockus wasformerly a seniorassociate withBooz & Company.

Florian Pötscher is asenior associate withStrategy& based in Vienna. He assists clientsin consumer-orientedindustries, such astelecom, high tech, andmedia, in developing newbusiness and enhancingcustomer service.

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Executive summary

How much can companies in the telecommunications industrybenet from “big data”? That’s a critical question. Every operator issearching for new ways to increase revenues and prots during a timeof stagnant growth in the industry, but few have demonstrated thecapabilities needed to make the most of this new technology.

That’s why operators seeking to make initial inroads with big data areadvised to avoid the usual top-down approach, which sets up a businessproblem to be solved and then seeks out the data that might solve it.This method does have benets, but it is unlikely to lead to anyserendipitous and surprising results — and it is difficult to executeuntil 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 anditeratively, without the overbearing oversight from which so manybusiness development projects suffer. If it’s done right, what emergescan form the basis for more efficient operations and more effectivemarketing. At its best, this bottom-up method can give operators amore complete, transparent view of customers, enabling new andmore protable ways of capturing and retaining them.


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Opportunity awaits

The virtues of big data have been touted in hundreds of articles andreports during the past few years. Yet the benets have proven elusive for alot of companies. Indeed, some analysts already see a considerable level ofdisillusionment regarding big data — an umbrella term encompassing thenew methods and technologies for collecting, managing, and analyzing inreal time the vast increase in both structured and unstructured data —

because too many efforts to implement the technology have not lived upto the high expectations triggered by the hype.

This is particularly true in the telecom sector. Most operators conductanalytics programs that enable them to use their internal data toboost the efficiency of their networks, segment customers, and driveprotability with some success. But the potential of big data poses adifferent challenge: how to combine much larger amounts of informationto increase revenues and prots 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 companiesin every industry — is to take a top-down approach by focusing onspecic business problems that big data might solve, and then gatheringthe data needed to solve them. But the challenge in this strategy istwofold: First, the business problem often exceeds the capacity of theavailable data to solve it, and second, the process of gathering the rightdata 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 thebusiness problems the data might help solve, with the help of anyadditional structured or unstructured data that might be needed ( see Exhibit 1, next page ). We believe the best way to get started with thisapproach is through pilot programs. Keeping initial expectationsreasonable, a dedicated team gathers all available data, analyzes it toallow new and unexpected opportunities to reveal themselves, and thentests the efficacy of the results in solving one or more real businessproblems. This tactic offers telecom operators and others a concrete


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starting point, a more realistic assessment of the benets of big data,and a better understanding of what is actually needed to achieve thosebenets in the long term ( see Exhibit 2 ).

Exhibit 1What is big data?

Source: Strategy& analysis

Exhibit 2Two approaches to big data


Structured data Unstructured data

Real Time

Batch

Velocity

Variety & volume

Dataware-house

Analyticalfactory

Big data

In-memory analytics Twitter

Facebook

Google+

Clicks

Customer proles

Call center

POSdata

WeatherPayments

Locations

Sensor data

Text messages

Hadoop/ MapReduce

Text documents

Online forums Video

SharePoint

EnvironmentalFinancials

HR records

Transaction history

Shipments

Top-downapproach

Bottom-upapproach

Big-dataopportunity

Internal & external data

Business issue Most big-data projects begin by dening a businessproblem to be solved, then trying to determine what datamight solve it.

These projects are run like traditional business intelligenceprograms, frequently achieving only incremental benets.

The bottom-up approach begins with the available internaland external data, and allows out-of-the-box opportunitiesto emerge.

Big-data pilots demand speed, agility, and constantiteration if they are to achieve really new and surprisingopportunities.


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The promise ofbig data for telecom

Big data promises to promote growth and increase efficiency andprotability across the entire telecom value chain. Exhibit 3, next page,shows the benets of big data over the opportunities available throughtraditional 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 exibly and protably modify subscribercalling plans immediately

• Tailoring marketing campaigns to individual customers usinglocation-based and social networking technologies

• Using insights into customer behavior and usage to develop newproducts and services

Big data can even open up new sources of revenue, such as sellinginsights about customers to third parties.


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Exhibit 3Big data offers benets across the entire telecom value chain


Big dataReal time/

unstructured

Traditional

datawarehouseBatch/structured

V e

l o c

i t y

V a r

i e t y

Marketing & salesService access

& integrationNetwork infrastructure

managementEnhancing traditional

value chain

Real-time deeppacket inspectionto optimize trafcrouting and steernetwork quality ofservice

Cellular networkperformancemeasurement

Data trafcmeasurement forprovisioning

Real-time call datarecord analysis toidentify fraudimmediately

Proactive behavior-based and planchanges

Total customerusage performancemodeling andmeasurement

Event-basedmarketingcampaigns that usegeolocation andsocial media,allowingdifferentiatedresponses

Cross- and up-sell

targeting (newproduct, upgrade,feature, service)

Sale of (anonymous)customer insightsbased on usagedata to shops,media agencies,etc.

New product/ service innovationbased on real-time

usage patterns

Backward-orientedanalysis of networktrafc to optimize

average networkquality, deployment,and coverage

Fraud detectionbased on historicalpayment data

Reactive rate-plananalysis based onhistorical data

Customersegmentation onhistorical,

aggregated dataStatic campaigns,agnostic of customerinteraction

No application


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From the bottom up

The essence of the bottom-up approach lies in gathering together all thedata available to the operator, both internal and external; applyingsoftware tools to process, analyze, and make sense of it; and thendetermining what can be done with the results. The key is to allow thedata to “speak for itself,” bringing out not just the obvious correlationsand 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 itdoesn’t quit. Many types of data are potentially available to operators —though it is unlikely that operators will have all these sources at thisstage — and certain sets of data might be combined to open up newbusiness opportunities in areas such as campaign marketing and fraudprevention ( see Exhibit 4, next page ).

• Enhanced recommendation engine: Operators could generate moreaccurate and personalized offer recommendations for existingindividual subscribers by combining internal structured data, such ashow and where each subscriber uses his or her phone, with externalunstructured or semi-structured data from social media platforms(for example, Facebook and Twitter). This information on customerpreferences and behavior could enable the recommendation engineto match price plans and offer attractive add-ons, such as sportsadd-ons for fans and free audiobook offers for commuters. As aresult, operators could lower the costs of retaining existingsubscribers and identify cross- and up-selling opportunities toimprove 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 signicantly increase the detection of fraudulentactivity such as looping or call forwarding on hacked PBXs (privatebranch exchanges), or fraud involving the swapping of SIM cards,and improve the overall accuracy and efficiency of their efforts torecognize patterns of fraudulent behavior.

Data has noagenda. It’sincorruptible,it has no boss,

it doesn’t wantto be promoted,and it doesn’tquit.


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Exhibit 4Potential data availability and usage


Network events

Call records (on andoff network)

Number of text andmultimedia messages

Volume of data trafc

Location-specic data

User handset data

Technical fault data

BillingCustomer careMarketing & salesProduct

developmentInfrastructure build

Internal dat a

E xternal data

Product catalog

Product life-cycle data

Product and platformcosts

Innovation road map

Product usage

Critical products

Product deliverymanagement

Customer devices

Option preferences

Sales channel data

ARPU classication

Response rate ofmarketing campaigns

Segmentation data

Usage patterns

Subsidy levels

Order data

Contract data

Fault handling data

Problem type

Resolution timeand rates

Repeated faults

Call center logs

Termination reasons

Call durationrecords

Tariff data

Usage history

Customer accountdata

EJLWireless

IDC

Burton GroupECTA BroadbandScorecards

Informa WBIS

Diffraction Analysis

FTTH Council Europe

Gartner

451 Research

Research ServicesTBR

Digital World

iSuppli

ComScore Data Mine

European InformationTechnology Outlook

Arab Advisors Group

eMarketer newsletters,consolidated data

Acxiom marketing data

TDG research reports

Ovum

Forrester

Nielsen metric

GfK

TNS Infratest

ITU countrycase studies

Social media data(e.g., Twitter,Facebook)

European Commission:eCommunicationsexternal studies

BuddeComm

TelegeoGraphyGlobalComms

Merrill Lynch WirelessMatrix

Credit Karma

Experian

TaricaITU country &regulator Proles

ITU WorldTelecommunication/ ICT IndicatorsDatabase

TeleGeographyyearbook

OECDCommunications

Outlook

Bold - Enhanced recommendation engine

Italic - Improved fraud management


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Piloting big data

The eventual goal of big data is to combine and correlate everyinformation source to generate a holistic, transparent, end-to-end viewof all the interactions every individual customer or household has withthe operator. But to really leverage big data, operators must radicallymodify how they gather, verify, learn from, and make use of theinformation at their disposal. That means completely rethinking the

purpose of the traditional corporate pilot program, long dependent onuncovering incremental opportunities by setting rigid, predeterminedgoals and hoping to attain them through laborious and time-consumingstage-gate and approval processes.

Instead, operators must learn from companies such as Google andFacebook, where data is king and virtually every product decision owsfrom what the available data says about customers and how it can beused. The big-data pilot program should be made up of teams of peoplefrom all over the company — including network operations, IT, productdevelopment, marketing, nance, and perhaps even customers — whocan bring their particular expertise to analyzing the data in new anddifferent ways. They must know what it means to “play around” withthe data, testing various combinations and correlations to see what works and what doesn’t.

This process must be agile, iterative, and quick. Piloting teams needto conduct numerous tests on the data, learn from their mistakes andfalse starts, and move to the next test. They must avoid the overlystructured 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 processof development, allowing the ttest and most valuable results to emergequickly.

Learn fromcompanies suchas Google and Facebook, wheredata is king and virtually every product decision ows from whatthe availabledata says aboutcustomers andhow it can beused.


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Conclusion

Big data offers telecom operators a real opportunity to gain a muchmore complete picture of their operations and their customers, and tofurther their innovation efforts. The industry as a whole spends far lesson R&D than any other technology-oriented industry as a percentage ofsales, and its efforts to change its ways have not yet proven broadlysuccessful. Big data demands of every industry a very different and

unconventional approach to business development. The operators thatcan incorporate new agile strategies into their organizational DNAfastest will gain a real competitive advantage over their slower rivals.


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This report was originally published by Booz & Company in 2013.

Strategyand Benefiting From Big Data a New Approach for the Telecom Industry

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