Customer Churn for Direct to Home Service Provider

Analytics & Technology Company
Business
Intelligence
Technology
Consulting
Increasing Active Subscriber Base
Analytics
Consulting
DTH Industry
www.valiancesolutions.com
© 2013 Valiance Solutions
DTH Industry: Reducing Customer Churn
Our Understanding
Key Challenges
 DTH market clocked revenue of USD 1.5
billion in 2012 with projected increase to
USD 3.9 billion by 2017 and over USD 5
billion by 2020.
 Total Subscriber base in 2012 stood at 32.4
million with projected increase to 63.8
million by 2017 and 76.6 million by 2020.
 Increasing faces increased competition with
6 players in the market coupled with price
sensitivity of the consumer.
 Accumulated losses of Rs 15000 Crores
till present.
 Heavy tax regime of 35 percent which
includes service tax, entertainment tax,
custom duty and license fees.
 Industry churn rate of 14-15% per
annum.
 Issues in DTH Sector
 High cost of Customer Acquisition
with CPE Subsidy of Rs 2600 per
customer, free subscription period
and zero installation fees.
 Growth in active subscriber base has
remained sluggish.
 Gap between gross & active
subscriber base is widening.
 Industry churn rate of 14-15% per
annum.
 High marketing & staffing expenses
 Changes in regulations with latest
proposed TRAI draft adding further
burden on DTH providers.
Our Response will Include
 Implementing decision engine to increase
active consumer base and proactively
reduce churn.
 Take proactive measures to increase ARPU.
Cost of Customer Acquisition
Per Subscriber
Per CPE Subsidy
Rs 2600
Rs 468
Rs 475**
Rs 81
Free 2 months
Subscription
Rs 400**
Total Cost of Customer
Acquisition
Rs 3475
Free Home Delivery &
Installation
Overall Cost (In Crores)
Rs 72
Rs 621*
* Based on 10 million gross subscriber per annum and18 percent market share for DTH Operator
** Based on ARPU of Rs 200 per month.
*** Based on Installation charges of Rs 350 per subscriber and visit charges of Rs 125
How does Churn Impact DTH Operator?
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Churn very early in customer life cycle amounts to
zero recovery of acquisition costs which stands at
nearly 93 Crores per annum at present churn rate of
15 percent for a DTH Operator.
Refurbishment of CPEs if at all returned by all
subscribes will cost around 135 Crores per annum to
a DTH Operator. Further more CPEs not returned
will result in direct loss.
Inefficient or poorly designed retention campaigns
can further result in high costs incurred in reaching
out to inactive subscriber base through Tele Calling,
Email or SMS campaigns.
Proposed TRAI recommendations in May’13 of
standardizing the cost of CPE and free warranty &
Repair period will further result in loss of over 1000
Crores. With high churn company won’t be able to
recover these costs from consumer.
Proposed Solution


Implementing a Scientific Decision Engine to pro actively identify subscribers who are
likely to stay inactive for continued period.
Design & Implement retention strategy for different consumer segments.
Benefits of Proposed Exercise





Increased Active subscriber base resulting in increased top line.
Increased wallet share per customer over a period of time with increased opportunity
for Cross Sell.
Increased efficiency of Renewal & Marketing Campaigns result in increased top line &
bottom line.
Proportionate increase in recovery of Acquisition costs from potential inactive
subscribers.
Lower cost of CPE refurbishment annually due to lower churn.
Why Us?
 Experience in similar exercise with a prominent DTH player with successful ROI.
 Strong team with experience in successful implementation of analytical solutions across
domains.
 In depth knowledge of advanced statistical tools and modeling techniques to impact visible
business outcome.
 Successfully implemented 50+ predictive models for clients across domains.
 Being a startup we are strongly determined to make a visible impact in functions we
operate.
 We can help you quickly set up decision processes and improve agility and responsiveness.
Case Study: Improving Active Subscriber Base
Objective : Improving Customer Retention



Understand Customer behavior leading to churn.
To identify Subscribers who are likely to churn and move out of the System.
Take proactive measures to keep likely subscribers in the program.
Identify Customers
Likely to Churn
Increased revenue
Base
www.valiancesolutions.com
© 2013 Valiance Solutions
Business Solution – Decision Engine
Quantitative Analysis of Churn


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What are the reasons for Churn?
What are patterns in customer Churn across different products, regions?
How does the Churn rates change by changing factors?
What is the probability of a customer to Churn?
Subscribers Coming up for
renewal
Prediction Engine Identifies Segment Customers based Segment Customers based
Most likely customers to
on likelihood to churn
on likelihood to churn
churn
** Accounts coming for renewal during the week/month will be scored using algorithm and
segmented.
www.valiancesolutions.com
Contact
Customers
© 2013 Valiance Solutions
Business Impact
Month1
Month2
3L
12%
3L
12%
3L
w1
•
•
•
w2
12%
3L
w3
w4
w5
w6
w7
12%
w8
Assuming On an average 3L Subscribers are due to be paid in a period of one week
8% (~24k) of them go into 30 + de-active bucket – Assume 10% of them can be refrained
with a proper communication => 24k * 10% * ARPU = 4.8 L
A random communication is made as on today (say 30%). Assume communication
cost/Sub = Rs. 1=> Total communication cost = 30k*3deciles*4weeks*Rs.1 = 3.6 L
Solution Framework
Build a predictive model to identify subscribers likely to move into “Deactivation
30+” for suitable communication
D1
D2
D3
D4
D5
Framework – Active Base
•
•
“Deactivation 30+” Model: Predicts top 3 deciles who are highly likely to go into
30+ deactivation state.
Develop a retention strategy in form of Offers to be rolled, Communication
strategy based on outcome of Predictive Model.
Two Step Approach
•
•
Step1: Build Churn Prediction model on a representative sample data
Step2: Deployment on the overall base.
Solution Framework (Contd.)
Identify Trigger Events
Identify events which influence churn/activation – Estimate the adjustment factor
separately for following events and apply/associate subs with it.
Events to be Considered
•
•
•
•
World cup cricket-Mar’11: Reactivation Trigger (RT)
Asia Cup –Mar’12: Reactivation Trigger (RT)
IPL opening ceremony -Apr’12: Reactivation Trigger (RT)
Ramzan (1st Aug’11-29th Aug’11): Deactivation Trigger (DT)
Associate Subscribers with Events
•
•
•
•
Lag of 10 days for Reactivation and Lead of 7 days for Deactivation => “Event Period”
Two consecutive year’s consistent behavior determines Deactivators
How to associate Subs with Events? (Elaborated in next slide)
Include DTs in “Deactivation 30+” model
Solution Framework (Contd.)
How to accommodate Events (Time dependent covariates) in the framework of Sub
level models which produce single propensity as outcome?
Events/Triggers
RT
DT
E1
E2
E3
Status
Subscr (30+
Deact) E1 E2 E3
iber
Sub1
1 1 0 0
Sub2
0 0 0 0
Sub3
1 0 0 1
Sub4
1 1 0 0
Sub5
1 0 0 1
Sub6
1 0 0 0
Sub7
0 0 0 0
Sub8
1 0 1 0
Sub9
1 0 1 0
Sub10
1 0 0 0
Sub11
0 0 0 0
Sub12
0 0 0 0
Sub13
0 0 0 0
Sub14
1 0 1 1
Sub15
1 0 0 0
Solution Framework (Contd.)
Establish accommodation Rules:
•
•
•
•
•
•
Subscribers who get into 30+ deactivation bucket during any time period will get a
Deact30+ flag, D = 1
Out of these subs if the time period when the sub falls into 30+ deactivation bucket is say
Ramzan (1st Aug’11-29th Aug’11 ) then the sub gets Ramzan flag = 1 else 0 (say E1=1)
If E1 = 1, it’s already D = 1 => E1, E2, E3 = 1 are always a subset of D = 1
Above rules apply for reactivation as well
If these binaries (E1 = 1/0) are included in the model and become significant, we have a
Theta/Gamma (+ve) associated with deactivation model
Final propensity (p): A linear combination of deactivation => deactivation Thetas have a
+ve effect, Reactivation Gammas have –ve effect (multiplied by a factor, say Rho)
Solution Framework (Contd.)
Length of time for Model development:
Development – 2 years data (Feb’11 – Jan’13)
•
Validation – 3 months data (Feb’13 – May’13)
•
Model Base
Active
0 - 30
30 - 60
Exclusion criteria: From modeling base
•
•
•
Subscriber having Account on Network < 3months
Subscriber having Total Deactivation > 365 days
Subscriber having Consecutive Deactivation > 180 days
> 60
Solution Framework (Contd.)
•
•
Assumption: Communication pattern post deactivation is known – D + 7, D + 14, D + 21,
D + 28
Weekly scoring to get Sub-wise propensity: Active + De-active <30 base (include only
Deactive Subscriber with Deactivation time = exactly D+7 or D+14 or D+21 or D+28
•
days, depending on above assumption
•
and/or goodness of model fit over time
Test-Control group during communication to measure the success of communication
Experimentation framework in future to select the best communications and/or offers
Sample Deliverable: Data Points Considered
Multiple derived variables were created for testing the improvement in the performance of the
model
Illustrative
Demographics
Base Product Details
Transaction Details
 Ethnicity
•
DTH Connection Type
•
Average Monthly Recharge
 Age
•
Base Product
•
 Marital Status
•
Last Base Product
Modes of Recharge
Transactions
•
Number of Showcase Orders
 Mobile / Email Indexing
•
Product Tenure
•
 Gender
•
Number of add on connections
Number of Add on Purchase /
Recording Requests
 Tenure
•
HD Pack Status
•
Number of Deactivations Days
Last Recharge Amount
•
Changes in Base Pack
•
 City
Customer Service Details
•
Number of Call Center
Interactions
Acquisition Details
•
Zone based classification
•
City based grouping
•
Number of Servicing requests
•
•
Modes of Servicing ex call
center, IVR, online
State wise grouping
•
Acquisition Channel
•
Offer Details for Acquisition
•
Reinstallation Requests.
•
Account Detail Updation
Request
www.valiancesolutions.com
Chota Pack
 Number of Add on Packs
 Last Add on Pack
 Activation Date of Add on Packs
 Total Revenue from Add on Packs
 Number of Ala Carte. and
activation date
 Mode of Ordering Packs
© 2013 Valiance Solutions
Model results
Deactivation Prediction Model
Illustrative
Parameter
Intercept
Total recharge indexed by city
Count of deactivation in last 1 year
Age on network (Dummy High - 1 to 3 years)
Age on network (Dummy Low - Less than 6
months and greater than 4years)
City class
Number of work orders
Ramzan deactivators
Box type
State (Dummy High - "Bihar","Rajasthan","Uttar
Pradesh")
State (Dummy Low "Haryana","Karnataka","Delhi")
Number of upgrades in last 1 year
Percent Concordant
Percent Discordant
Percent Tied
Estimate
1.209
-2.5455
0.9302
0.3157
-0.7495
0.3082
-0.2982
0.2447
-0.4437
0.2647
-0.2201
-0.3612
78.3
21.5
0.2
Decile Total
0
1
79676
4
79685
2
3
5
6
7
8
9
10
Non Deact Deact30+ rate
79685
340059
66021
26%
367580
28878
11%
351065
79687
360432
79684
375526
79686
381559
79687
385883
79687
389794
79687
392925
79685
397659
796849
48141
139129620220
0
19%
37852
15574
70%
80%
97%
3%
258380
60%
94%
4%
6710
50%
89%
5%
10083
40%
30%
84%
5%
11954
70%
20%
78%
6%
13181
10%
59%
8%
90%
100%
100%
Gain Chart Deact30+
1
0.8
0.6
0.4
0.2
0
0
1
2
3
4
5
6
7
8
0
26%
44%
15%
19986
Deciles
Pairs
0
Cum_rate Random
9
10
Valiance Solutions is…
Business




Analytics
Technology
Valiance
Solutions
Valiance Solutions is a Business Intelligence firm based out of Noida India with client
base across US and India.
Valiance combines team of experienced consultants ,business analysts and technology
guys from top Technology & Management Institutes like IIT’s and IIM’s .
Leadership team comes from IIT’s and IIM’s with 24 years of combined experience in
delivering analytical & technology solutions to Investment Banks globally and BFSI
companies in India.
Valiance acts as a strategic partner for it’s clients in conceptualization and
implementation of BI initiatives.
Vision & Mission
We consider data to be a key strategic asset in improving decision making and
tapping new opportunities across all functions of an organization.
Our vision is to
Be the most admired firm delivering breakthrough BI solutions to it’s clients and
helping them unlock new opportunities.
Our mission is to
Help clients unlock business potential of data using analytics and technology.
 Help our clients stay ahead in their domain through breakthrough solutions.
Executive Team
Vikas Kamra (Co-Founder & CEO)
•
•
•
Vikas has six years of extensive experience in business and technology consulting from Fortune 100
companies to startups globally
He currently serves as CEO of Valiance Solutions and works with firms to decide right
solution/framework to address business problems in functional areas of customer acquisition,
retention, marketing or risk using both technology and advanced analytics.
Vikas did his graduation from IIT Delhi and is CFA Level 2 qualified. Prior to co-founding his own firm in
2011, Vikas has worked with Merrill Lynch, Bank of America, Jefferies Investment Bank on several
onsite and offshore engagements.
Ankit Goel (Co-Founder & CTO)
Ankit Goel serves as CTO of Valiance Solutions and is responsible for execution and delivery of
technology initiatives. He takes keen interest in fields cloud computing and big data technologies and
has worked on several projects in these areas.
•
•
Ankit did his graduation from IIT Kharagpur and has 8 years of experience working on technology
projects with investment banks globally.
Shailendra (Co-Founder & Head of Decision Sciences)
•
•
•
Shailendra head analytics function with Valiance and possesses keen business and analytical insight to
solve business problems. He has worked on several advanced level analytics initiatives with Life
Insurance companies, Mutual funds, Credit Card Companies, NBFC’s in India in Credit Risk, Marketing
and Customer Analytics.
Shailendra has 5 Years of experience working with Fortune 100 Financial companies across EMEA, US
and Indian Subcontinent region.
Shailendra did his graduation from DMET/MERI and holds several patents to his name.
Advisory Team
Lokesh Gupta (General Partner, Spice Investment Fund)
•
•
Lokesh is working as General Partner in Spice New Investment fund. In this current role, Lokesh is
responsible for identifying startup companies in Education domain and help them transform their ideas
into big enterprises. Prior to that Lokesh was heading Spice Labs as its CEO.
Lokesh holds his management diploma from IIM-Ahmedabad and bachelors degree in computers from
IIT Delhi.
Dinesh (PHD, IIT Delhi)
•
•
Dinesh has 12 years of strong experience in data driven analytical consulting, modeling and statistical
analysis. He currently serves as Vice President Analytics with premier marketing Analytics firm in
India. Prior to this he has held senior positions in companies like ICICI, GE Capital, Inductis at senior
positions in analytics capacity.
Throughout his career Dinesh has provided analytical & technological leadership, tactical solutions and
measurable delivery of financial opportunities through advanced data mining/predictive analytics
solutions for various business verticals like Retail, Insurance, FMCG, Automobile, Travel & Hospitality,
Telecom, Mutual Funds etc. He also collaborates with academia in trainings, course materials for MBA
students in analytics.
Experience of Executive Team
Key Clients
Business Intelligence – Insurance
Business Intelligence – Sales Performance
Business Intelligence - BFSI
Big Data- Storage & Processing Real time
location updates
Loyalty & Prepaid Cards – Customer
Insights, Transaction processing
Valiance Solutions Private Limited
A-146, Opposite TCS building,
Sector 63, Noida, U.P - 201306
India.
Contact Person: Vikas Kamra
Office No: +91 120 4119409
Contact No: +91 8750068961

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