leveraging big data for bigger revenue
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1 Copyright © 2013 Comviva Technologies Limited. All rights reserved.
Leveraging Big Data for Bigger Revenues Deploy a data-driven marketing approach to improve
service consumption
Africacom, October 2013
2
Agenda
Declining wallet share
The power of Analytics
Use Cases
Case studies
Challenges being faced
The operator continues to be relevant
Monetizing existing services
Need for a new market-place
Bring to bear power of Recommenders
3
Growth has subdued
97 104
84 90
73
0
20
40
60
80
100
120
2011 2012 2013E 2014E 2015E
Net
ad
dit
ion
s (
mn
))
Africa net mobile connection additions
1.91
1.96 2.00
2.03 2.05
1.80
1.85
1.90
1.95
2.00
2.05
2.10
2011 2012 2103E 2014E 2015E
SIM
per
su
bscri
ber
Africa SIM per subscriber
Markets maturing, customers spreading spend across multiple networks
6% Y-o-Y decline in mobile connection growth, 4% Y-o-Y decline in ARPU
7.6 7.0 6.8 6.6 6.5
-8%
-3% -2% -2%
-10%
-8%
-6%
-4%
-2%
0%
0
2
4
6
8
2011 2012 2013E 2014E 2015E
AR
PU
gro
wth
AR
PU
(U
S$)
Africa mobile ARPU
ARPU (US$) ARPU growth
53.9 57.9 63.8 68.0 70.9
8%
10%
7%
4%
0%
2%
4%
6%
8%
10%
12%
0
20
40
60
80
2011 2012 2013E 2014E 2015E
Rev
en
ue g
row
th
Rev
en
ue (
US
$ m
n)
Africa mobile revenue
Revenue (US$ mn) Revenue growth
4
Growth
Growth can no longer come from acquisition
Can growth come from higher consumption?
Are we leaving money on the table?
5
Operator continues to be relevant
Trust built on relationship - people depend on you
You own the subscriber – biggest asset
Serious brand value – established over years
Source: Wireless Intelligence, WHO, World Bank, ITU
Added confidence – Regulatory oversight
6
Many CSPs are adopting a high digital
services strategy
• App Stores & Rich
Media
• IP Communication
• Scope for emerging
market operators to
focus on:
• Health
• Education
• Government
• Finance
• Content/media
• Agriculture
• Law
MTN partners with DStv for
mobile TV streaming
Orange partners with
Deezer for music streaming
mEducation
mHealth
mFinance
Econet acquires TN bank to
extend banking services
7
Challenge is in monetizing existing services
Making out-reach relevant and
contextual is a challenge
Dormancy is a challenge
Service discovery is a
challenge
8
Reducing dead weight loss
Match right product to
right customer
Implement 3rd degree
price discrimination
Demographic
segmentation to
identify price
conscious users
Send promotions at
right time
Price
inelastic
Price
elastic
Superior
Customer
Experience
Personalization and Recommendation
Bring together Buyer and Seller
9
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:
Location & network
environment
When a person would
take an action:
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
10
From old order to new
Reebok 2013 ad
Mass market engagement Personalized engagement
11
Wonderful thing called the recommender
systems
35 % percent sales
generated from
recommendations
75% of the content consumed
comes from the
recommendation engine
Source: businessinsider.com
12
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:
The paradigm
Customer value personalization across channels
Email Social Mobile Web display
67% 44% 40% 36%
67% says it is important for emails to be personalized, followed by
social media (44%), SMS (40%) and web display ads (36%)
In progress
13
Customer data is an unused growth asset
Transaction data
Customer
data
Unstructured data
Location data
Demographic data
CRM data
Data inputs Uses of data
Customers‘ trail of information, coming from many channels, provide rich insights into their
specific needs and preferences
Drive customer
engagement
Generate reports for
business planning
Deliver smarter services
that generate new
revenue streams
Enhance customer
experience
Enhance service quality
by better network
capacity planning
14
Rules of buyer-seller engagement have altered
“Segment of one” marketing
Mass marketing
Batch & blast Customer-triggered
Aligned to campaign calendar Aligned to customer lifecycle
One-way communication Dialogue/interactive
Business & channel silos Integrated & informed
Manual/semi-automated Fully automated
Periodic Real-time/ near-real time
16
Deepen engagement over the lifecycle of the
customer
Use c
ases
Pricing:
Recomm
endation:
Bundled pricing plans
Location based pricing
Acquire Grow Retain Winback
Incentives for the first
top-up
Discount on VAS trials
Real-time
Offers
Personalized real time
offers
Next best offers
Data/VAS/mMoney
promotions
Location based offers
Churn
control:
Churn propensity scoring
Customer experience
optimization
Winback campaigns
Service/content
recommendations
Loyalty programs
Tenure based
personalized rewards
Rewards &
incentives:
17
Map engagement to customer transactional
behavior
0
5
10
15
20
25
30
Balance drops below US$5,
subscriber uses mainly
SMS lately
High balance, subscriber
just topped-up his account
Though customer’s balance
is in credit, he has stopped
using the services
Zero balance for an abnormal
period. Subscriber has not
responded to a top-up Incentive
Balance
Time
Spend offer
Pay «Avg spend +US$2»,
Get X MB data
Top-up stretch
Top-up «Max top-up
amount», Get Y on-net mnts
Activate
This week your calls are
%50 discounted
Recover top-up
Top Up «Avg top-up amount»,
Get 2Y on-net mnts
18
Scie
nti
fic a
lgo
rith
ms
Improve share of telecom spend among
multiple SIM users
Inactivity patterns
Silent period during a day
Device type (multi-SIM)
Service usage pattern
Variance in recharge
pattern
Analyze customer data patterns to
identify multi-SIM customers
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
Cu
sto
mer
data
Send personalized campaigns
to multi-SIM users
Discount on
calls during
day-time:
Recharge with ‘8-
to-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
19
Optimize service experience with next best
offers
Next best data offers:
Priority 1: Video pack $20
Priority 2: Video pack $25
Priority 3: VAS pack $ 30
Calls the customer care
executive to complain about
poor video browsing experience
The customer care executive offers $20
video pack to customer that provides
higher browsing capacity and speed
$20 video pack offer:
Enjoy 3GB of access to video websites and 200 MB
of free access to other website at 21 Mbps
Intensive data user - video
constitutes 90% of data consumption
Current data pack: 2GB data, 7.2
Mbps download speed for US$15
Frustrated with high buffering time
and poor video quality
Based on customer ‘s data usage
pattern, the agent recommend s an
appropriate data pack
Customer
subscribes to the
$20 video pack
20
Proactively anticipate churn events
Last recharge date
Last call/SMS/ data usage
Age on network
Service usage trends
Device type (multi-SIM)
Class of service
Churn
prediction
Churn indicators
Customer care interactions
Location
Social network data
• 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
High propensity
to churn
CS: Churn score
21
Recommendation
engine
543211
Young adult
College student
Baby boomer
Local businessman
My Songs
You light up my life
(Debby Boon)….
Symphony No.9
(Ludwig van
Beethoven)….
Dials RBT
portal
Dials RBT
portal
Recommends
popular hip-hop
and rock songs
Recommends
popular tracks from
the Seventies
First-time
users
Improved service discovery with personalized
recommendations
RBT portal
543211
My Songs
Back in Black
(AC,DC)….
Bartender(T
Pain)….
Drops of Jupiter
(Train)…
Generates relevant playlist based on:
Customer’s demographic profile
Wisdom of crowds
Customer’s unique preferences and transactional patterns
Recommendation
engine
543211
Customer is an R&B music
fan. Purchased 2 Whitney
Houston tones in the last 6
months
In last 4 visits to ‘the RBT portal’
storefront customer selected
hip-hop music
543211
RBT portal
Recommended
songs:
I will always love you
(Whitney Huston)….
When you believe
(Mariah Carey)….
Love is all we need
(Celine Dion)
Recommended
songs:
Lose yourself
(Eminem)….
In da club (50 cents)….
99-problems (Jay-
Z)…..
Dials RBT
portal
Dials RBT
portal
Recommends
R&B songs
Recommends
hip-hop & pop
songs
Frequent users
Generates relevant playlist based on:
Customer’s demographic profile
Wisdom of crowds
Customers music preferences and transactional
patterns
,
22
Case Studies
23
Reactivate revenues from inactive users
Operator
Challenges
Solution
Winback detects presence of inactive customer on the network
Sends a campaign to the customer in real-time improving reach
and ensuring higher conversion
Results
Leading Nigerian operator with 40 million connections
Predominant prepaid multi-SIM market: Each customer
owns 2.4 SIMs
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
Achieved campaign reach rate of 49.5% and campaign
response rate of 15%
ROI recovered within a month
Generated revenue of US$ 17.7 million for the operator in 6
month
After the winback launch, operator market share grew by
2.14% from Dec’12 to Mar’13
Revenue generated from
Winback base
Operator’s market share
Winback Launch
24
Indian operator registers 167% increase
in tone sales
Challenge Problem of plenty
850,000
audio
clips
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
Solutions
Result Increased in sales between
sales Nov’12 & Jul’13
167
%
268
%
MyLikes
tone sales MyLikes
revenues
A tone is sold after every
198 calls
with MyLikes
535 calls
without MyLikes
Monetization of long tail
Decline in share of
top 20 bestsellers
Pre
MyLikes
Post
MyLikes
48
% 43
%
26
Challenges & Tools
27
Revenue
planning
Automated
customer profiling
& segmentation
Campaign design
& definition
Campaign
execution &
fulfillment
Campaign
measurement &
reporting
Revenue Plus
-- A unique CVM solution that drives revenue growth by enabling revenue planning,
customer engagement & retention management
Mahindra Comviva’s Revenue Plus
28
“Average is for marketers who don’t have
enough information to be accurate ”
--Seth Godin
In conclusion
29
Please Visit us at Booth Number C08
30
Disclaimer Copyright © 2013: Comviva Technologies Ltd, Registered Office at A-26, Info City, Sector 34, Gurgaon-122001, Haryana, India.
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Copyright © 2013 Comviva Technologies Limited. All rights reserved.
Thank you Visit us at www.mahindracomviva.com
33
Leading Indian operator: Maximizing music sales
with personalized recommendations
Operator
Challenges
Solution
MyLikes simplifies service discovery by recommending relevant
tunes to customers based on their music preferences,
transactional & demographic profile and wisdoms of crowd
Integrates with multiple channels - IVR, virtual number and
inbound dialing - can be extended to Web and Search
Results
Problem of plenty: Expansive catalogue of 850,000 audio clips
Complex service discovery: Multiple short codes, lengthy
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%
Between November 2012 and March 2013:
100% increase in tone sales on MyLikes compared to 0% on
channels not integrated with MyLikes
126% increase in MyLikes service revenues
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
0.7
1.1 1.3
1.1
1.4
0.2 0.2 0.4
0.1 0.2
Nov'12 Dec'12 Jan'13 Feb'13 Mar'13
mill
ion
Comparative trend in tone sales
Tone sales generated via MyLikes
Tone sales generated via channels notintegrated with MyLikes
55.3 75.8 81.9 89.9
124.9
Nov'12 Dec'12 Jan'13 Feb'13 Mar'13
US
$ ‘00
0
MyLikes revenue
Revenue generated via MyLikes
34
Customer-side engagement