The document discusses a case study report on churn analysis for a telecom service provider. It outlines the business scenario of a telecom provider losing customers and profitability with average churn rates of 8%, 12%, and 15% over three quarters. The solution proposed uses advanced modeling techniques like neural networks and logistic regression to construct a model that scores each customer's probability of churn. The model helps identify high-value customers likely to churn and informs the provider's retention strategy.

1. Axion Connenct 1
A CASE STUDY REPORT ON CHURN
ANALYSIS
Submitted to
Mr. Sanjay Rao
Founder, Axion connect
Presented by
Amit Kumar

2. Way Forward….
2
 A Business Scenario
 Business Problem
 Available Solution
 Stepwise solution
 Results & Findings

3. A Business Scenario…
3
A leading telecom service provider has a customer base of
1million users. In the cellular base, a customer can choose pre-paid
& post-paid services. In this competitive telecom market customers
have vast array of choices, the cost of acquisition & rate of
customer churn, both are increasing at a rapid pace. In last three
quarters operator’s profitability has gone down & faced problem of
customer churn in one of the operator’s largest circle. The average
attrition rate for each quarters for the operator is 8% , 12% & 15%
respectively.
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4. Business Problem..
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 Telecom service provider is loosing customer base & their
profitability has gone down.
 The average churn rate is around 12%(Q1-8%, Q2-12%, Q3-15%)
 The acceptable return on their retention program is very less & it
has not targeted sharply to the customer churned in second & third
quarters
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5. Available solution..
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 To address these problems, operator wants a robust retention
model/churn model that would help the telecom operator to identify
the propensity of churn & high-value customers.
 Need to use advance modeling techniques like neural
networks, decision trees & logistic regression to construct a model
that can score each customer for his probability of churn over next
quarter.
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6. Stepwise solution..
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 Integration of the data like, billing information, demographic
information, service record information, customer participation in
retention program etc. in a single file to capture all aspects of
customer interaction.
 Understanding of data dimension, functions & association of data
object with functions
 Data object preparation
 Constructing the model
 Validating the model
 Implementing it & track it
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7. Results & Findings..
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 Model will help the operator with other CRM metrics to build
retention strategy.
 Customer with high profitability with high propensity to churn should
be in the highest priority of retention and should offer best incentive.
 Customer with low profitability with high propensity to churn should
encouraged to increase the usage.
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8. Axion Connenct 8
THANK YOU
!