This document discusses predicting customer churn for mobile operators to prevent customer loss, increase satisfaction and optimize network usage. It provides details on churn rates in India, data collection methods used including oversampling, and analysis performed on factors like network coverage, call quality and customer support to develop a churn prediction model. The analysis was conducted by a team of 7 people using techniques like p-value analysis, affinity diagramming and tweaking oversampling through trial and error.

1. Mango Mobile.
Predicting who will
switch Mobile
operator?

2. Prevented
Churn
Increased Customer
Satisfaction
Optimized Network Usage
Capture Revenue

3. Churn
Term used to describe customer attrition or
loss
Churn Rate
The number of participants who discontinue
their use of a service divided by the average
number of total participants during a period
India
Historically, monthly churn rate that Indian
operators grapple with ranges from 3.5 per
cent to 6 per cent
Source : TRAI

4. Mobile/Wireless subscriber base increased
to 87 crore at the end of May 2013,
registering a monthly growth of 0.37 percent
Source : TRAI
The total telecom base grew by 0.34 percent
to reach 90.005 crore

5. Initial Data
sample
Brainstorming
P-value
Analysis
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54
16

6. Multiple_Sims
Network duration
Mobile Service
Provider-Network Coverage
Provider-Call quality
Provider-Customer support
Provider-Offers and promotions

7. 55% oversampling based on trial and error
Error Report
Class # Cases # Errors % Error
1 430 106 24.65
0 716 104 14.53
Overall 1146 210 18.32

8. Maximum
Distance

9. Top
Deciles

10. Source - http://www.telecomindiaonline.com/customer-retention-the-telco-next-blue-eyed-boy.pdf

11. Structured
Brainstorming /
Affinity Diagram
Usage of ‘p’
values
Tweaking
Oversampling
Hit & Trial
Methodology
Financial Aspects

12. Team:
Amit Narula
Gaurav Gupta
Gunvansh Singh Khanna
Jagveer Singh Brar
Nakul Marwah
Mayank Jain
Saurav Dhole