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All rights reserved.

2. Leveraging Big Data for Bigger Revenues
Deploy a data-driven marketing approach to improve
service consumption
Africacom, October 2013
Copyright © 2013 Comviva Technologies Limited. All rights reserved.
1

3. Agenda
Declining wallet share
Challenges being faced
The operator continues to be relevant
Monetizing existing services
The power of Analytics
Need for a new market-place
Bring to bear power of Recommenders
Use Cases
Case studies
2

4. Growth has subdued
6% Y-o-Y decline in mobile connection growth, 4% Y-o-Y decline in ARPU
120
100
97
Africa SIM per subscriber
104
90
84
73
80
60
40
20
0
2011
2012
2013E
2014E
2.10
SIM per subscriber
Net additions (mn))
Africa net mobile connection additions
2.03
2.05
2.05
2.00
2.00
1.96
1.95
1.91
1.90
1.85
1.80
2015E
2011
2012
2103E
2014E
2015E
Markets maturing, customers spreading spend across multiple networks
Africa mobile ARPU
6.8
6.6
6.5
0%
-2%
6
-3%
4
-2%
-2%
-4%
-6%
2
-8%
0
2011
-8%
2012
-10%
2013E 2014E 2015E
ARPU (US$)
ARPU growth
80
60
53.9
57.9
63.8
68.0
70.9
10%
40
8%
7%
20
4%
0
2011
2012
12%
10%
8%
6%
4%
2%
0%
Revenue growth
7.0
Revenue (US$ mn)
7.6
ARPU growth
ARPU (US$)
8
Africa mobile revenue
2013E 2014E 2015E
Revenue (US$ mn)
Revenue growth
3

5. Growth
 Growth can no longer come from acquisition
 Can growth come from higher consumption?
 Are we leaving money on the table?
4

6. Operator continues to be relevant
Serious brand value – established over years
Trust built on relationship - people depend on you
You own the subscriber – biggest asset
Added confidence – Regulatory oversight
Source: Wireless Intelligence, WHO, World Bank, ITU
5

7. Many CSPs are adopting a high digital
services strategy
• App Stores & Rich
Media
Orange partners with
Deezer for music streaming
• IP Communication
• Scope for emerging
market operators to
focus on:
mHealth
• Health
• Education
Econet acquires TN bank to
extend banking services
• Government
• Finance
• Content/media
mEducation
• Agriculture
• Law
MTN partners with DStv for
mobile TV streaming
mFinance
6

8. Challenge is in monetizing existing services
Service discovery is a
challenge
Dormancy is a challenge
Making out-reach relevant and
contextual is a challenge
7

9. Reducing dead weight loss
Bring together Buyer and Seller
Match right product to
right customer
Implement 3rd degree
price discrimination
Send promotions at
right time
Price
inelastic
Price
elastic
Demographic
segmentation to
identify price
conscious users
Superior
Customer
Experience
Personalization and Recommendation
8

10. Customer-side engagement is the key
Who the customer is:
Demographic information, life stage, transactional
patterns, device type, social group
Where the customer is
present:
When a person would
take an action:
Location & network
environment
Real-time information,
customers’ intent and action
at a specific time and place
•
Operators have large volumes of untapped data

Power of analytics to understand and bring context to engagement

Potentially treat each subscriber as unique
9

11. From old order to new
Reebok 2013 ad
Mass market engagement
Personalized engagement
10

12. Source: businessinsider.com
Wonderful thing called the recommender
systems
35 % percent sales
generated from
recommendations
75% of the content consumed
comes from the
recommendation engine
11

13. The paradigm
In progress
 Analytics to micro-segment (even N=1)_ based on behavior and profiles
 Cross-product into matching products with micro-segments
 Reach via more than one touch point:
Customer value personalization across channels
Email
Social
Mobile
67%
44%
40%
Web display
36%
67% says it is important for emails to be personalized, followed by
social media (44%), SMS (40%) and web display ads (36%)
12

14. Customer data is an unused growth asset
Data inputs
Uses of data
Transaction data
Drive customer
engagement
Demographic data
Enhance customer
experience
Location data
Customer
data
Deliver smarter services
that generate new
revenue streams
CRM data
Enhance service quality
by better network
capacity planning
Unstructured data
Generate reports for
business planning
 Customers‘ trail of information, coming from many channels, provide rich insights into their
specific needs and preferences
13

15. Rules of buyer-seller engagement have altered
Mass marketing
Batch & blast
Aligned to campaign calendar
Customer-triggered
Aligned to customer lifecycle
One-way communication
Dialogue/interactive
Business & channel silos
Integrated & informed
Manual/semi-automated
Periodic
Fully automated
Real-time/ near-real time
“Segment of one” marketing
14

16. Deepen engagement over the lifecycle of the
customer
Acquire
Use cases
Pricing:
Real-time
Offers
 Incentives for the first
top-up
 Discount on VAS trials
Grow
Rewards &
incentives:
Winback
 Bundled pricing plans
 Location based pricing
 Personalized real time
offers
 Next best offers
 Data/VAS/mMoney
promotions
 Location based offers
 Churn propensity scoring  Winback campaigns
 Customer experience
optimization
Churn
control:
Recomm
endation:
Retain
 Service/content
recommendations
 Loyalty programs
 Tenure based
personalized rewards
16

17. Map engagement to customer transactional
behavior
Balance 30
25
Spend offer
Pay «Avg spend +US$2»,
Get X MB data
High balance, subscriber
just topped-up his account
20
Activate
This week your calls are
%50 discounted
15
10
Top-up stretch
Top-up «Max top-up
amount», Get Y on-net mnts
5
0
Balance drops below US$5,
subscriber uses mainly
SMS lately
Though customer’s balance
Recover top-up
is in credit, he has stopped
Top Up «Avg top-up amount»,
Get 2Y on-net mnts using the services
Zero balance for an abnormal
period. Subscriber has not
responded to a top-up Incentive
Time
17

18. Improve share of telecom spend among
multiple SIM users
Analyze customer data patterns to
identify multi-SIM customers
Send personalized campaigns
to multi-SIM users
Silent period during a day
Device type (multi-SIM)
Service usage pattern
Variance in recharge
pattern
Scientific algorithms
Customer data
Inactivity patterns
Multi-SIM
customer 1:
Active during
night from 8pm
to 12am
Multi-SIM
customer 2:
Uses data
service only
Multi-SIM
customer 3:
Makes on-net
calls only
Discount on
calls during
day-time:
Recharge with ‘8to-8 day’ pack
and get 50%
discount on all
calls from 8:00
am to 8:00 pm
Voice and data
bundle:
Recharge with
‘More data’ pack
and get 1GB data
usage and 50 free
voice minutes for
a month
Discount on
off-net calls:
Recharge with
‘off-net call’ pack
and make off-net
calls at price of
on-net calls
18

19. Optimize service experience with next best
offers
 Intensive data user - video
constitutes 90% of data consumption
 Current data pack: 2GB data, 7.2
Mbps download speed for US$15
 Based on customer ‘s data usage
pattern, the agent recommend s an
appropriate data pack
 Frustrated with high buffering time
and poor video quality
Calls the customer care
executive to complain about
poor video browsing experience
Next best data offers:
Priority 1: Video pack $20
Priority 2: Video pack $25
Priority 3: VAS pack $ 30
The customer care executive offers $20
video pack to customer that provides
higher browsing capacity and speed
Customer
subscribes to the
$20 video pack
$20 video pack offer:
Enjoy 3GB of access to video websites and 200 MB
of free access to other website at 21 Mbps
19

20. Proactively anticipate churn events
• Flag churn indicators
• Accord appropriate weights
• Calculate churn score for each customer
• Based on churn score identify
customers with high propensity
to churn
• Preemptively send personalized
campaigns to high-risk
customers to contain churn
Churn indicators
Last recharge date
Last call/SMS/ data usage
Age on network
Service usage trends
Device type (multi-SIM)
Class of service
Churn
prediction
High propensity
to churn
Customer care interactions
Location
Social network data
CS: Churn score
20

21. Improved service discovery with personalized
recommendations
Generates relevant playlist based on:
Generates relevant playlist based on:
 Customer’s demographic profile
 Customer’s demographic profile
 Wisdom of crowds
 Wisdom of crowds
 Customers music preferences and transactional
 Customer’s unique preferences and transactional patterns
patterns
First-time
Frequent users
users
Recommended
My Songs
songs: Black
Back in
I will always love you
(AC,DC)….
(Whitney Huston)….
Bartender(T
When you believe
Pain)….
(Mariah Carey)….
Drops of Jupiter
Love is all we need
(Train)…
(Celine Dion)
543211
Customer is an R&B music
fan. Purchased 2 Whitney
 Young adult
Houston tones in the last 6
 College student
months
Recommendation
engine
Recommended
My Songs
songs: up my life
You light
Lose yourself
(Debby Boon)….
(Eminem)….
Symphony No.9
In da club (50 cents)….
(Ludwig van(Jay99-problems
Beethoven)….
Z)…..
543211
In last 4 visits to ‘the RBT portal’
 Baby boomer
storefront customer selected
 Local businessman
hip-hop music
Recommends
Dials RBT
popular hip-hop
Recommends
Dials
portal RBT
and rock songs
R&B songs
portal
Recommends
Dials RBT
Recommends
Dials RBT pop
hip-hop &
portal
popular tracks from
portal
songs
the Seventies
RBT portal
RBT portal
,
21

22. Case Studies
22

23. Reactivate revenues from inactive users
 Leading Nigerian operator with 40 million connections
Operator
Challenges
 Predominant prepaid multi-SIM market: Each customer
owns 2.4 SIMs
Revenue generated from
Winback base
 20% inactive base: US$ 581 mn is the approx. annual
opportunity loss from inactive users
 Inefficient marketing: Existing push-based blanket SMS
and OBD promotions failed to address inactive users
 Winback detects presence of inactive customer on the network
Solution
 Sends a campaign to the customer in real-time improving reach
and ensuring higher conversion
Operator’s market share
 Achieved campaign reach rate of 49.5% and campaign
response rate of 15%
 ROI recovered within a month
Results
 Generated revenue of US$ 17.7 million for the operator in 6
month
Winback Launch
 After the winback launch, operator market share grew by
2.14% from Dec’12 to Mar’13
23

24. Indian operator registers 167% increase
in tone sales
Challenge
Problem of plenty
850,000
audio
clips
Solutions
Result
Complex service discovery
Unable to monetize long tail
Top 20 songs
generate 48%
sale
Lengthy menus
Multiple short codes
MyLikes recommends relevant tunes to customers based on their music preferences,
transactional & demographic profile and wisdoms of crowd
Increased in sales between
sales Nov’12 & Jul’13
167
%
MyLikes
tone sales
A tone is sold after every
Decline in share of
top 20 bestsellers
48
%
268
%
MyLikes
revenues
Monetization of long tail
535 calls
without MyLikes
198 calls
with MyLikes
Pre
MyLikes
43
%
Post
MyLikes
24

25. Challenges & Tools
26

26. Mahindra Comviva’s Revenue Plus
-- A unique CVM solution that drives revenue growth by enabling revenue planning,
customer engagement & retention management
Campaign
measurement &
reporting
Revenue
planning
Revenue Plus
Automated
customer profiling
& segmentation
Campaign
execution &
fulfillment
Campaign design
& definition
27

27. In conclusion
“Average is for marketers who don’t have
enough information to be accurate ”
--Seth Godin
28

28. Please Visit us at Booth Number C08
29

29. Thank you
Visit us at www.mahindracomviva.com
Disclaimer
Copyright © 2013: Comviva Technologies Ltd, Registered Office at A-26, Info City, Sector 34, Gurgaon-122001, Haryana, India.
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The Company logo is a trademark of the Company. Other products, names, logos mentioned in this document , if any , may be trademarks of their
respective owners.
Copyright © 2013 Comviva Technologies Limited. All rights reserved.
30

30. Leading Indian operator: Maximizing music sales
with personalized recommendations
 Problem of plenty: Expansive catalogue of 850,000 audio clips
menus and high IVR browsing charges negatively impacted
repeat sales
 “Me-too” marketing: Predominant use of “batch and blast”
marketing techniques, resulted in low conversion rates of 0.2%
1.1
million
Operator
Challenges
Comparative trend in tone sales
 Complex service discovery: Multiple short codes, lengthy
1.3
1.4
1.1
0.7
0.2
0.2
0.4
0.2
0.1
Nov'12 Dec'12 Jan'13 Feb'13 Mar'13
Solution
Tone sales generated via MyLikes
 MyLikes simplifies service discovery by recommending relevant
tunes to customers based on their music preferences,
transactional & demographic profile and wisdoms of crowd
Tone sales generated via channels not
integrated with MyLikes
 Integrates with multiple channels - IVR, virtual number and
inbound dialing - can be extended to Web and Search
 100% increase in tone sales on MyLikes compared to 0% on
channels not integrated with MyLikes
 126% increase in MyLikes service revenues
Results
 379% higher customer conversion on MyLikes as compared to
channels not integrated with MyLikes
 A tone is sold every 184 calls on MyLikes as compared to 535
calls on channels not integrated with MyLikes
US$ ‘000
Between November 2012 and March 2013:
MyLikes revenue
124.9
75.8
81.9
89.9
55.3
Nov'12 Dec'12 Jan'13 Feb'13 Mar'13
Revenue generated via MyLikes
33

31. Customer-side engagement
34