Churn Modelling for Mobile Telecommunications

What is Churn Modelling 

Churn is one of the biggest threat to the telecommunication industry.  Every telecommunication industry deploys the best models that suit their need to avoid the voluntary or involuntary churn of a customer. This is called churn modelling. Below I will take you through the terms frequently used in building this model.

  • Churn represents the loss of an existing customer to a competitor
  • A prevalent problem in retail:
    • Mobile phone services
    • Home mortgage refinance
    • Credit card
  • Churn is a problem for any provider of a subscription service or recurring purchasable
    • Costs of customer acquisition and win-back can be high
    • Much cheaper to invest in customer retention
    • Difficult to recoup costs of customer acquisition unless customer is retained for a minimum length of time
  • Churn is especially important to mobile phone service providers
    • easy for a subscriber to switch services
    • Phone number portability will remove last important obstacle

Predicting Churn: Key to a Protective Strategy

  • Predictive modelling can assist churn management
    • By tagging customers most likely to churn
  • High risk customers should first be sorted by profitability
    • Campaign targeted to the most profitable at-risk customers
    • Typical retention campaigns include
      • Incentives such as price breaks
      • Special services available only to select customers
  • To be cost-effective retention campaigns must be targeted to the right customers
    • Customers who would probably leave without the incentive
    • Costly to offer incentives to those who would stay regardless

Here, We have a sample telecom data on which we will run Churn Modelling using R code.

Loading data directly from the web

Fitting a Model

How does the linear model perform

How does the Logistic Regression perform

How does the Decision Tree perform

How does the Random Forest perform


Algorithm RMSE Comment
Linear Model 0.3232695
Simpler Linear Model 0.1767
Logistic Regression 0.3179586 better than the linear model
Decision Tree 0.6742183 much worse than the linear model (Overfitting)
Decision Tree (Without type = “class”) 0.2423902 better than the liniar model
Random Forest 0.2217221 improvement from the linear model  so a non-linear decision tree approach is better

You might also like More from author