The main goals of the project was to decrease the churn rate using all the data available about the customers and their history.
How we handled this
We set up a pilot to identify customers ‘at-risk’ to churn using predictive modelling and launched an email campaign to these customers with a sales offer. What we did briefly:
- Create a customer view with all available historical data: clicks on website, subscription details (GB, mins, fee), customer demographics, customer service tickets, CRM, etc.
- Train- and test different classification models on the data to identify customers ‘at-risk’. ML algorithms we used are decision tree, random forest, logistic regression, XGBoost and SVM
- Model evaluation and - selection
- Use ‘winning’ model to score existing customers resulting in a churn probability for each individual customer (satisfied customers vs customers at risk)
- Set up a campaign for customers at risk of churn
- Campaign evaluation