In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition.
For many incumbent operators, retaining high profitable customers is the number one business goal.
Background: To reduce customer churn, telecom companies need to predict which customers are at high risk of churn. We have been hired by a telecom industry giant to look at customer level data and identify customers at high risk of churn and identify the main indicators of churn.
Problem Statement: We need to build a predictive model using advanced Machine Learning algorithms in order to predict the customers at high risk of churn along with the key indicators of churn.
This case study has been completed with the help of my team mate Koushal Deshpande. Thanks Koushal for your help and your key insights!