Praxis Telekommunikation Für Telekommunikationsunternehmen ist es mit Big Data auf Grund der verfügbaren Kundendaten, möglich, diese Beziehungen besser zu monetarisieren. Mobily, Saudi Arabiens Telekommunikationsunternehmen mit rund 20 Millionen Kunden, beauftragte Roland Berger eine “Big Data Monetization Strategy“ zu erarbeiten. Wie das Unternehmen dadurch seine eigene Leistungsfähigkeit steigert und seine Kunden mit passgenauen Serviceleistungen anspricht, das erzählte Andreas Tiefengraber von Roland Berger Strategy Consultants beim Werbeplanung.at Summit SPEZIAL am 3. Dezember 2013 in der Uni Wien.

1. Praxis: Telekommunikation
Andreas Tiefengraber
Roland Berger

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BIG DATA monetization
Focus Telecommunication
December 2013
Andreas Peter Tiefengraber
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DATA PRODUCTION
DATA PRODUCTION and therefore the amount of available data is
EXPLODING – this trend is expected to continue the coming years
Rapid growth of data – Indicators and development
COMMENTS
2m
30 bn
Emails sent every day
Search queries received by
Google every minute
Pieces of content shared on
Facebook every month
450 bn
> 5 bn
1m
Business transactions on
the internet in 2020 every
year
People using mobile phones
worldwide
Transactions handled by
Walmart every hour
DATA SIZE
294 bn
> Global data production
has reached
astonishing levels,
mostly driven by
cheap computing and
increasing online
activity
> Volume of business
data worldwide
expected to double
every 1.2 years
Big data
Information overload
Relevant data
TODAY
SOURCE DC, Big Data Meets Big Data Analytics (SAS), ROLAND BERGER
THE FUTURE
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DATA IS THE NEW OIL
The new paradigm everybody is talking about –
DATA-IS THE NEW OIL
"I suspect that when the history is written two hundred
years from now, a trend will emerge as something very
important that happened in human thinking during the
time when we were alive, and that is that we are
becoming
rational, analytical and data-driven
in a far wider range of activity than we ever have been
before."
independent.co.uk
"Data is the next
intel inside."
Tim O'Reilly (2005)
Source: Roland Berger, Press research
"Data is the new oil."
Ann Winblad (2012)
"Data is becoming the
new raw material in
business" Rollin Ford (2012)
Larry Summers,
Former President of Harvard
and Chief Economic Advisor
to Barack Obama
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DIGITALIZATION
BACK-UP
BIG DATA MONETIZATION becomes a key capability to unleash the
power of data that is generated through DIGITALIZATION
R&D
Procurement
Operations
Marketing
Sales
Information&
Communication
DIGITAL
TRANSFORMATION
CREATES…
DIGITALIZATION
Source: Roland Berger
Data-driven R&D
E-Procurement
Enterprise systems
Digital Marketing
BIG DATA
Big data usually includes data sets with sizes
beyond the ability of commonly used software
tools to manage and process the data with
tolerable time and effort.
BIG DATA TOOLS
Recent Big data approaches comprise hardware,
databases, analytics software and predictive
models to analyze big data effectively to reveal
dependencies and thereby new insights.
E-Commerce
BIG DATA MONETIZATION
Social Media
Leverages insights from predictive models to
identify new revenue and cost savings
opportunities – within the current business or
through new business models.
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BIG DATA CHARACTERIZATION
BIG DATA is characterized by massive data volume, velocity and
variety – too large and complex to process with traditional tools
As DATA grows exponentially…
1m
Pieces of content shared on
Facebook every month
Transactions handled by
Walmart every hour
DATA SIZE
30 bn
Est. No. of business
transactions on the internet
in 2020 p.a.
Relevant data
Source: Roland Berger
1
450 bn
Information overload
TODAY
BIG DATA deals with massive
data…
VOLUME
3
VARIETY
BIG
DATA
2
VELOCITY
THE FUTURE
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SPECIFIC RELEVANCE FOR TELCOS
Possessing enormous amounts of VALUABLE CUSTOMER DATA,
Telecom operators increasingly warm up to the idea of monetizing it
INDUSTRY QUOTES
RECENT DEVELOPMENTS
"We could make a living just out of
analytics"
Dir. Technology & Strategy
> Telefónica launched Dynamic
Insights, a new unit dedicated
to unlocking value from Big Data
"We create more than 5,000
campaigns per day based on Big
Data"
CIO Bharti Airtel
> AT&T (AdWorks) and Sprint
(Pinsight Media+) both
aggregate subscriber data to
improve campaigns
"Whether Vodafone will be the
"Apple" of Big Data? May well be"
CEO Germany
> NTT provides its corporate
customers with various Big Data
solutions with their high volume
processing unit
BIG DATA
Source: Roland Berger, Press research
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BIG DATA MONETIZATION APPROACHES
There is no standard approach for BIG DATA MONETIZATION –
We offer help in four different areas which fit companies' needs
Roland Berger project approaches – Big Data monetization
Any CEO is relatively easy
convinced that Big Data is
important – but it is important to
define the right scope of the
first Big Data monetization
approach!
Source: Roland Berger
1
2
3
4
BIG DATA
AUDIT
Understand the potential of (big) data to
boost top and bottom line impact along the
value chain – different options from a 1
day workshop to a 6-8 weeks audit
BIG DATA
MONETIZATION
STRATEGY
Capture and prioritize the strategic options
to become a big data champion and
work out pre-requisites, business case and
roadmap to do so
CONCEPT DEVELOPMENT FOR BIG DATA
APPLICATIONS
Work out and implement concepts for
concrete big-data applications (e.g. new
1:1 marketing concepts, new products,
new business models)
IMPLEMENTATION &
TRANSFORMATION
Ongoing and hands-on support (could be
success fee based) to implement and
harvest the potential identified and put in
place all pre-requisites
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MOBILY BIG DATA MONETIZATION STRATEGY
Mobily asked Roland Berger to elaborate its Big Data monetization
strategy in order to enhance its business
Mobily Big Data Monetization Strategy
MOBILY STARTING POINT
SELECTED KEY QUESTIONS
Valuable data waiting to be
monetized
> Innovation as its core value;
customers expect Mobily to
be first in new services and
efficiency
> Significant amount of
customer data (e.g. ~20 bn
CDRs per month, geo
locations, email)
> Capable and scalable IT
infrastructure
Source: Mobily; Roland Berger
> How to monetize data?
> How should Mobily enhance its ongoing operations?
> How should Mobily introduce new services?
> How should Mobily diversify?
> What are the requirements in terms of
> IT infrastructure,
> organizational structure,
> operating model and
> regulatory changes?
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MOBILE AS A DRIVER
Big Data combined with mobile usage drives a revolution of new
insights, value creation and customer centricity / benefits
Huge jump in mobile
advertising revenues
Mobile driving
success
Profiling users through
audio and video
fingerprints / tags
Mobile ecosystem
enhancing economic
development
Personalized recommendations based on
past purchases
1 of 10 customer pays
with their mobile at
Starbucks USA
Individual insurances
based on observed
customer behavior
Hugely successful in
bringing music through
the smartphone
Source: Roland Berger
… with Big Data
as key enabler
Incorporating wide data
variety to pinpoint movie
suggestions
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DATA SOURCES
Mobily has the opportunity to utilize the enormous amount of
available data for their own operation and diversification
Data sources available to Mobily – Illustrative
INTERNAL
STRUCTURED
Mediation
Passive network information
Cell map information
Logs
Personal data
Shop information
CDRs
BTS information
Billing
EXTERNAL
Top-ups
Self-developed
apps
Geo-location
information
ATM location data Distributor POS data
Retail customers' information
Source: Roland Berger
Offers
External websites
DPIs
Click-stream information
Complaints
Call centre voice calls
transcriptions
Questionnaires
Articles (newspaper, magazines,
publications, etc)
Census data
Partners information (e.g. banking)
Ad servers
UNSTRUCTURED
Transactions
Social Media (Twitter, Facebook, etc)
Emails
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TELCO BD VALUE CHAIN
In the value chain of Big Data monetization, Telcos can play a range
of roles, from pure datamart to end-to-end service provider
Data
I
DATA
GENERATION
Information
II INFORMATION
PROCESSING
Value
III INFORMATION ENABLED ACTIVITIES
Services
IV THIRD PARTY
SERVICES
KEY
ACTIVITIES
> Acquire customer data > Structure data
> Storage and tagging
> Define data analysis
> Privacy management
algorithms
REQUIREMENTS
> Infrastructure to record > Hardware and software > Aligned organization to > Suite of services
to analyze structured
and store without
make use of insights
catered to 3rd parties
and unstructured data > Potential clients
privacy invasion
> New organization
in real time
benefiting from insights > Partners
POTENTIAL
ROLE OF
TELCOS
> Full aggregator of
complete digital and
physical life data set
(e.g. location, spend,
surfing, email, calls)
Datamart Provider
Source: Roland Berger
> Improve existing
services
> Offer new services
> Set up new entity
(double sided business
model)
> Provider of the
> User and/or provider of > Provide data to new
information processing
relevant insights based entity
platform deployed to
on end-to-end Big Data > Run new entity (in
extract insights from
management
partnership)
data
Big data platform
provider
End-to-end business
service provider
Separate Big Data entity
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STRATEGY – KEY DELIVERABLES
Mobily's Big Data monetization Strategy was built on
8 KEY DELIVERABLES
AS-IS ASSESSMENT
DATA PROTECTION
OPERATING MODEL
IT ARCHITECTURE
ROADMAP
BUSINESS PLAN
PROOF OF CONCEPT
INDUSTRY/USE CASES
Source: Roland Berger
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THE 6 KEY SUCCESS FACTORS
We have identified 6 KEY SUCCESS FACTORS on the path to a
leading big data player – From data capturing to skill development
Key lessons learnt from big data champions
1
CAPTURE &
GENERATE ALL
RELEVANT DATA
… generates a digital
blue-print of our life
2
STORE &
PROCESS ALL
TYPES OF DATA
… combines its own with
external data e.g. weather
3
TRANSLATE
DATA TO
INSIGHTS
…links all relevant data to
predict our needs
4
MAKE INSIGHTS
CONSUMABLE
… increased its value by
1,000% through analytics
5
FOSTER A
DATA-DRIVEN
CULTURE
… expects all employees "to feel like they are capable of using data"
6
ACQUIRE &
DEVELOP
SKILLS
… announced the launch of a global software center and a USD 1 billion
investment to build software and a team of appr. 1000 data scientists
and 9,000 software engineers
Source: Roland Berger
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4 AREAS OF MONETIZATION
There are four distinct areas on HOW TO MONETIZE BIG DATA –
In the short to mid term main value creation will be internally
BIG DATA VALUE CREATION
INTERNAL
> Optimization of current
business model (e.g. faster, more
targeted, more effective and
efficient processes along the entire
value chain)
Revenue:
TOPLINE
> Identify & acquire new customers
> Boost share of wallet
> Boost customer loyalty
> Boost customer recommendations
+ 3-7%
Cost savings:
- X-X%
Internal vs. external:
EXTERNAL
> New business models to
create value out of big data – often
with third parties (e. g. data
brokerage, new data-enabled
products & services)
Source: Roland Berger
BOTTOMLINE
> SPEND less / reduce OPEX
> INVEST smarter / reduce CAPEX
$
6:1
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INTERNAL VALUE CREATION
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ILLUSTRATIVE
We jointly identified and selected 200+ valuable use cases, which
were consolidated in 6 areas of Big Data impact
Use case assessment (selection of use cases)
Calling screen configuration: Customers will be
notified if they can initiate a local or international call
based on their location
Review and monitor
Improve Sales & Distribution through customers' usage
providing real-team performance data
behavior to
Give network priority to VIP customers in proactively avoid
customer churn
areas where the network is congested
before it happens
Assigning favorite numbers: Customers Re-identification of users
who are frequent international callers and using different SIM card,
haven't assigned their IFN
based on their motion and
Time Based Charging: Different charging behavioral profile
to be done at different time of day.
Analyze customer data and design
specific offers and promotions to
enhance up selling and cross selling
Real-time deep packet inspection of
network performance to optimize
traffic routing and steer network
quality of service
Analyze structured data (such as actual subscriber usage) and unstructured or semi-structured data types
(such as log files, click streams and text from e-mails), to provide more accurate and personalized offer
recommendations
Source: Roland Berger
Consolidated areas Big Data impact
1. Reporting accuracy
2. Prediction accuracy
3. Customer profiles
4. Analysis time
5. Real-time action
6. Mobility patterns
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OPERATING MODEL
ILLUSTRATIVE
Best practice examples confirm that Data, Tools & Processes and
R&D are best done centrally while A&M fits better decentrally
Input from best practices
BEST PRACTICE
Centralized
Ensures cohesion
with company-wide
standards
Efficient use of
resources for
development
Operational speed
and local adaptation
and feedback loops
Decentralized
Data, tools and
processes
Source: Roland Berger
Research &
development
Analytics and
management
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PROOF OF CONCEPT
Mobily already analyses social media; in our project we conducted
an ANALYSIS of UNSTRUCTURED DATA to showcase Big Data
Twitter analysis
How is this useful
> Social media analysis
currently already
executed by Mobily
> With Big Data proof of
concept additional
analyses performed in
very short timeframe
> 2 million (16 GB)
Tweets analyzed on
Amazon Web
Services
> Geo-location
dimension, customer
sentiment and social
graphs
> Determine where are
your most vocal
customers
> Gauge the reaction
e.g. on new price
plans, promotions,
product launches, etc.
per area
> Influence your
opinion leaders
> Quickly respond to
negative or positive
communications
Riyadh
Saudi
Global
Social graph
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PROOF OF CONCEPT
During the project we ran PROOF OF CONCEPTS with Mobily's own
data (CDR) e.g. Base station performance linked to customer value
Under utilized
Source: Roland Berger
Congested
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INTERNAL CHALLENGES
Using Big Data requires to CHANGE THE COMPANY in three
domains: Mentalities, Organization and Technology
Internal challenges due to Big Data
A
Mentalities
Mentalities
> Accept that data treatment will improve
human decision making and not replace it
> Accept to change the business processes
to include it
Organization
> DMO1) should be responsible of the
right usage and changes around
INTERNAL
data seen as an asset for the
CHALLENGES
company
B
C
> All Business Line will be potentially
affected: Marketing, product
Organization
Technology
management, HR, IT, Top
executives
Technology
> Smallest changes to operate
> Ensure right management of
Data projects
1) Data Management Office
Source:
Roland Berger
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YOUR CONTACT
Looking forward to hearing from you
ANDREAS
TIEFENGRABER
+43 1 536 02 201
andreas.tiefengraber@rolandberger.com
Principal
InfoCom CEE
Thank you
for your attention!
Source: Roland Berger
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RB CASE REFERENCES
Our BIG DATA REFERENCES – We help leading international
companies to become a big data champion
Case references
BIG DATA
PROJECT TOPICS
> Development of business models based on "Big data"
> Development of consumer-insights from generated
from loyalty card data
> Identification of multi-channel customer behavior
> Growth of market share with new channel-based CRM
system
> Analysis of transactional customer data for service
improvement
> Market potential, business model and technology for
offerings based on telematics data
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PUBLICATIONS
We have demonstrated THOUGHT LEADERSHIP with many highly
recognized publications
Selected publications on Customer Data Monetization
In Data We
Trust
Shopper
Insights
Big Data –
Big Picture
Rediscover
Your
Customer
Cloud
Economy
Geo-analysis
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Strategies
That
Work!
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