Churn Analytics in the Telecom Industry

March 24, 2010

Gunjan Thakuria, one of our finest consultants, educates us on Churn Analytics

The landscape and dynamics of the telecommunication industry has changed drastically with so many service providers entering the market. The Indian telecommunications industry is one of the fastest growing in the world and India is projected to become the second largest telecom market globally. According to TRAI, the number of telecom subscribers in the country increased to 562.21 million in December 2009, an increase of 3.5 per cent from 543.20 million in November 2009.With so many service providers fighting it out for the same customer base, there is  lot of focus and attention given to churn reduction and customer retention. The fact that customer acquisition is a very expensive exercise has led to more emphasis given to customer retention strategies.

To determine customer retention strategies, it is very important to determine which are the customers who are most likely to churn, and then device strategies based on that. Logistic regression methodology is extensively used to predict churn. Using logistic regression it can be determined not only who is going to churn, but also what the drivers of churn are. It tries to model the log of odds of churning taking into consideration the various characteristics of the customers. The equation in a logistic regression is as follows:

Log (p/1-p)= B0 +B1 X1 + B2X2 +B3X3


P is probability of churning

X1, X2 and X3 are the covariates effecting churn

Bo is the intercept and B1, B2 and B3 are the coefficients

The intercept and coefficient values are determined using the maximum likelihood estimation. Before performing a logistic regression, the data set is divided into two parts, training and validation. The model is developed on the training set and the probability model is validated by using the equation on the validation set. For the model to be validated decile analysis, lift chart and confusion matrix has to be checked.

The customers are then grouped according to their propensity of attrition. Once this is done it becomes easier for the CRM team to device strategies for customer retention. Usually different types of customer retention campaigns are done on the different groups of customers.

This methodology of churn reduction has proved to be highly profitable and productive for telecommunication companies in reducing churn and hence increasing profitability.


One Response to “Churn Analytics in the Telecom Industry”

  1. Krishna Prasad Says:

    I would also like to add that Logistic is a powerful application that helps in predicting the churn but, more appropriate tools like Survival Analysis, Accelerated failure time models, Cox Proportional Model would help in robust and dynamically predicting the churn not only at aggregate level but also at individual level, one can also compute the CLTV (Customer Life Time Value) in this scenario.

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