How can big data help us look differently at our customer base? A presentation by Elan Rosenberg, Business Development Director, Marketing Analytics at cVidya

1. BUSINESS
GROWTH
© 2014 – PROPRIETARY AND CONFIDENTIAL INFORMATION OF CVIDYA
Utilizing big data to
optimize customer value
management strategies
Elan Rosenberg
Business Development Director
Marketing Analytics

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A leading supplier of Revenue Analytics solutions to
communications and digital service providers
Founded: 2001
300 employees in 15 locations worldwide
Deployed at 7 out of the 10 largest operators in the world
150 customers in 64 countries
Processing 2.45 Billion subscribers in deployments globally
Saving over $12 Billion to providers annual revenue
Partnering with world leading vendors
What You Should Know ABOUT US
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How can big data help us look differently at our customer base?
What if you identify that these are
all one family with different kind of data users?
Daughter
Mother
FatherSon
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And what if you knew that they are mainly interested
in Football?
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So, how can this optimize our marketing activities?
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The Williams Family
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Profession
Freelance architect
Hobbies
Fashion, sports (tennis),
news (business, entertainment)
Profession
Marketing professional
in an int’l firm
Main Usage Patterns
Voice, WhatsApp, Skype,
frequent roamer, news apps
Devices
Laptop, tablet, iPhone5s
Devices
Laptop, tablet, Nexus 5
Main Usage Patterns
Voice, internet browsing,
tethering
Hobbies
Sports (football, basketball) and cooking
Debra
George

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University student
Hobbies – music, sports
(rock climbing, scuba diving)
Devices – laptop, tablet, iPhone 4s
Main Usage Patterns – Voice,
Facebook, Skype, WhatsApp
High school student
Hobbies – movies, sports
(dancing, swimming)
Device – Galaxy S2
Main Usage Patterns – Voice,
WhatsApp, Instagram, YouTube
Elementary school student
Hobbies – Reading, sports
(biking, skateboarding)
Device – low-end smartphone
Main Usage Patterns – Voice,
WhatsApp, Facebook, internet
browsing
Mike
16 11
JessicaColin
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Back to the CSP’s reality…

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Tools to support a non-technical marketer with
quick path from ideation to actionable results
Complexity of getting near real-time data
insight supporting informed decisions
Lack of subscriber insight for personalized user
experience
Multiple and disparate data sources
Access, collection, enrichment, analysis
Quick, relevant and cost-effective launch of new
services and propositions
Base Management
Challenges & Needs
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How does the CSP see the Williams family today?
 Debra
− Private account
− Plan: bundle of 3 GBs data,
unlimited nat’l/int’l voice/sms
− Silent roamer (mainly WiFi)
 Colin
− On a student plan in a competitor network
 Mike
– Prepaid SIM
– No visibility on demographics
– Plan: recurrent bundle of 500MBs
data, 500 minutes, 500 SMS
– Occasionally exceeds data allowance
 George
– SOHO account
– Plan: bundle of 5 GB data,
unlimited nat’l voice/sms
– Never exceeds data allowance
?
 Jessica
− On the same account as Debra
− Plan: bundle of 1 GB data,
unlimited nat’l voice/sms
− Regularly exceeds data
allowance
?
Debra
Jessica
George
Mike
Colin

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Top-up stimulation offers
 Mobile data dongle
 Cloud storage
 Standard roaming package
 Extra SIM for a tablet
 Bridge data bundle
 Data bundle upsell
…and what can it offer them?
?
Debra
Jessica
George
Mike

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Utilizing big data
analytics
Data Available
Customer attributes, XDRs, DPI,
device, location, data bundle
utilization, point of sale, invoice, top-
ups, etc…
Insights
Correlations, relationships, patterns,
habits
 Correlations – social circles, families,
SMBs
 Patterns of use – profile enrichment
 Interests
 Gender and age groups
 Influencers (new offers, retention)
 Needs and communication habits as
individuals and as a group/segment
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What can big data analytics reveal about the Williams family?
?
?
Family Circles

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What can big data analytics reveal about the Williams family?
?
Age Group
(8-13)

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What can big data analytics reveal about the Williams family?
?
Gender

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What can big data analytics reveal about the Williams family?
Interests
Family Circles
?
?
Age
Gender
Devices

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What can big data analytics reveal about the Williams family?
?
?
Now what can we offer them?
 Shared, multi-device, data family plan
 Acquisition campaign – add another family member
 Migration of prepaid to post-paid
 Special data roaming rates
 Device upgrade supporting LTE *
 Promotions on a special occasion to a sports event
 1 month free offer for a Mobile HDTV sports pack
* “Apple to be the
most desired brand among
American teenagers”
(Piper Jaffray’s 25th
bi-annual teen survey)

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Let’s zoom out to a full customer base family analysis
Tethering and multi-device usage
Correlation between # data users
and family ARPU/Usage
Families data usage characteristics
Family size distribution
Influencers

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cVidya Enrich – Your Guided Path to Actionable Insights
 Self-service environment for Telecom
marketers
 Pre-modeled customer data analytics with
use cases focusing on different business
objectives
 Identifies potential target micro-segments
for different marketing activities
 Impact analysis of potential offers on
targeted segments
 Combines advanced analytical models,
based on machine learning sophisticated
algorithms
Greater visibility of meaningful data

21. THANK YOU!
www.cvidya.com
Elan Rosenberg
Marketing Analytics Business Development Director
Email: elan.rosenberg@cvidya.com
Mobile: +972.54.561.5661