Using analytics to deliver higher value!

1. SUBSCRIBER PREFERENCE
MODELING
A unique approach to ARPU maximization
for the satellite television industry

2. Contents
Glossary
Executive Summary
About the Client
Business Situation
Solution Space
Hansa Cequity SPM Framework
Business Benefits
Client Testimonials
About Hansa Cequity
Glossary
DTH (Direct to Home) A term used to refer to digital satellite television
broadcasts intended for direct home reception, received by a satellite
dish and set-top box.
ARPU A measure of revenue generated per user or unit, ARPU
(Average Revenue per Unit) allows companies to analyse revenue
generation and growth at the unit/user level.

3. Subscriber Preference Modeling
Executive Summary
Hansa Cequity enabled a leading direct-to-home digital satellite television provider
increase it's Average Revenue per User with an end-to-end deployment of its
proprietary customer engagement model that saw increased levels of customer
engagement and higher incremental revenues from customised campaign
management strategies.
About the Client
One of the leading digital satellite television providers, the company is a joint venture
between India's leading business house and a global broadcasting giant. This case
study discusses strategies employed by Hansa Cequity to enhance the client's
Average Revenue per User, through customised consumer campaigns and efficient
data management solutions.
Business Situation
Business Space
From its mass emergence in the 1990s to the present, digital satellite television has
grown to become one of the fastest growing consumer electronics products of all
time. Trends for the future indicate innovations in interactive TV services, and cutting
edge 3D viewing experiences. However, the subscription based business model
employed by leading service providers was associated with high costs of acquisition
and retention. It became imperative for service providers to design sales and
marketing strategies adapted to individual consumer types and viewing preferences.
Existing Campaign Assessment
Traditionally, the client relied on mass campaigns across various media to promote
offers, packages, and products. Targeted on the entire customer base without
specific individual customer insights, these were run on a daily, weekly and monthly
basis throughout the year. These campaigns were classified as:
a. Event based campaigns
b. Educational campaigns
c. Campaigns where existing subscribers would be sold higher value packs
d. Campaigns where customers would be targeted just before pack expiry date
1

4. Subscriber Preference Modeling
Campaign Impact
Lower response rate | Customer churn | High campaigning costs | Drain on the
various channel resources
Reasons for churn and low response rate:
a. Confusion and lack of clarity on intended message, owing to differing marketing
messages through various channels
b. Packages offered were not found to be of any relevance to individual needs and
preferences.
c. Low level of satisfaction with the brand.
Solution Space
The Need for a Better Solution
a. Customers needs are evolving according to their tenure of association
with the brand
b. There is a need to leverage customer information (transactional) lying idle in
repositories, for effective segmentation and marketing
c. It is vital to determine the life stage customers belong to, before running the
campaigns,and if the campaign channels are being utilized effectively
d. The one size fits all strategy will not generate the requisite ROI,
or maximize the ARPU
e. The need of the hour is to focus on what customer wants rather than what we
would want to sell them
Solution Conceptualization
The idea was to leverage available customer data and determine patterns to predict
individual viewing preferences. The focus was placed on locating individual behaivour
triggers that would in turn suggest a need, which would enable the client to offer the
best offer/package to fit this person's need.
2

5. Subscriber Preference Modeling
Subscriber Preference Modeling
Framework
Unlike conventional mass campaigning, Hansa Cequity's Subscriber Preference
Model enabled the client to offer each individual user the package/offer most
pertinent to his needs and wants, and thus drive conversions.
About Subscriber Preference Modeling
The Subscriber Preference Model is based on a hosted data mart designed from
analytical data culled from customer transactions over time. This database is used to
create a Propensity Model which gives insights into the probabilities and
predispositions among customers towards buying a particular product / offer /
package. The propensity data for each customer enables the sales executive to make
informed decisions on the product/offer to sell to the person. The feedback from
customer transactions is used to further fine tune the marketing process for the
particular individual, and the demographic.
Salient points:
a. The SPM model enables the client to offer the Right Offer for
the Right Subscriber
b. The framework enables effective segmentation of subscribers
based on their needs
c. Identifies the order of campaign events for each opportunity with subscribers
Analytical Data Mart
Subscribers’ spends
Demographics
DATA MAGNITUDE
Call Centre Records 5 Million Customer Base
Active Services 55Million Transactions
Deactivation Behavior 60Million Call records
Subscriber Self-Care 5 Million Customer Base
Package Characteristics
Approx 250 fields per
subscriber stored as a
ETL snapshot
Campaign feedback also
added back to the data mart
Summary & Roll Ups for enrichment
Marketing Variables
LTD Values & Scores
Master Tables
Subscriber Events
3

6. Subscriber Preference Modeling
Data mart Statistics:
5 Million Customer Base | 55 Million Transactions | 60 Million Call records | Single
view subscriber snapshot of approx 250 fields per subscriber | Continuous updation
of campaign feedback for enrichment
Key Highlights
a. Rich information repository for predicting subscriber behaviour
b. Campaign insights flow back for fine-tuning the campaign
c. Enables quick planning of specific product campaigns by slicing and dicing the
portfolio along various derived variables and parameters
Propensity Model
Salient points:
a. 10 models were built for 10 target products (6 cross-sell and 4 up-sell)
b. Customer base was segmented basis their specific needs
c. Successful identification of factors that significantly influence the decision to
hold a package
d. Utilization of various statistical measures to determine the preferred product
e. Assisted product sequencing for campaigns
f. Determination of top 3 product choices for each subscriber
g. Provided "Quick time-to-market" for various packages and offers
Marketing Design
1
RS
2 3
OFFE
TEST Product Sequence Offers are designed for
each product
Campaign list Email
SMS campaign campaign
sent to Inbound
sent sent
call center
If Subscriber requests
for follow-up
Instant Feedback
mechanism
Unification of
campaigning across Campaign list sent to
Outbound call center
multiple channels by
assigning unique
campaign codes
4

7. Subscriber Preference Modeling
Unique Offerings: Offer hierarchy management | Unique codes for unification across
channels | Channel optimization strategy | Test and learn environment creation
Key Features of SPM Model
a. Effective customer base segmentation based on subscriber behavior
b. Creation of new offers, which included up front packs and discounted packs
c. Effective use of SMS campaigns
d. Designed campaigns to be executed via in-bound channels
e. Creation of monitored dashboards covering a wide range of variables and
parameters, made available to client on daily basis
f. An effective tracking and monitoring functionality to measure performance of
individual models, offers, and product sequencing methodologies
Business Benefits
The Hansa Cequity Advantage
Beginning from conceptualization, creation of the analytical data mart, propensity
model, and the subsequent execution, this project was completed within the
committed time of two months.
Business benefits
a. Triple fold increase in customer response rate
b. Reduced churn by 3%, and won back de-active customers
c. Increased average product holding per subscriber by almost 10%.
d. 230, 000 packages added, including cross-sell and up-sell packs, over a period
of 4 months
e. Pitching and conversion rates at the call centre increased by 10% for in-bound
customer calls.
f. Quarterly incremental revenue of almost $3 million, and a net incremental
revenue of almost $2 million - a 200% lift
g. Reduced redundant campaigns and introduced new
campaigns
5

8. Subscriber Preference Modeling
Client Testimonials
"Substantially Reduced Subscriber Churn"
Thanks to Hansa Cequity's Subscriber preference model. We are now better able
to provide the 'Right Offer' to 'Right Subscriber' and also better engage with them
at each opportunity leading to substantial reduction in subscriber churn. Keep up
the great work!
- Head, Subscriber Marketing
"Improved Campaign Response Rates"
Hansa Cequity's Subscriber preference modeling framework was a key differentiator
in our Marketing efforts. It led to solid business results with our Campaign response
rates improving over 3 times the base rate. Thanks for a fantastic job.
- Chief Marketing Officer
6

9. Subscriber Preference Modeling
About Hansa Cequity
Hansa Cequity is a provider of technology enabled, analytics driven end-to-end
customer and marketing intelligence solutions. Our goal is to help companies build
profitable, empowered customer relationships and engaging experiences by
leveraging customer information with analytics and technology tools. We build and
leverage the power of marketing databases and automation applications using best-
in- class customer marketing processes.
Website http://www.hansacequity.com/
Blog http://blog.hansacequity.com
Contacts Ajay Kelkar
Chief Operating Officer
Ajay.kelkar@cequitysolutions.com
Amit Pote
Business Development
amit.pote@cequitysolutions.com
Address India:
105-106, 1st Floor, Anand Estate,
189-A, Sane Guruji Marg,
Mahalakshmi,
Mumbai - 400 011
Ph: +91 22 43453824
Fax: +91 22 43453840
USA:
626, Grove Evantson,
Chicago, IL 602
7