Identify potential lost customers on time to allow a company take actions to avoid loosing customers. Use historical data and customer’s characteristics to learn who, why and when a customer will churn.
The team built a classification model to predict the probability of losing a customer. The trained model considers trends and statistics of the historical revenue as well as customer’s demographics like country and industry among others.
- Data Science Experience (DSX)
Data Science Techniques
- Binary Classification
This company was able to identify the customers that are at a high risk of being lost with 6 months of anticipation. In addition, the model outputs the key factors explaining why each customer will potentially churn. As a result, the company has 6 months to take action on high risk customers and re-engage with them.
Need help with a similar case?
Contact us to find out how we can help!