Customer churn predictor for a SaaS product

The brief

A SaaS company loses customers every month and the success team calls them only after they leave. Build a model that predicts who is likely to cancel next month from their usage and support history, and explains the main reasons for each customer. Good looks like: a model that beats a simple rule, a ranked list the team can call, and the top reasons behind each prediction.

What you will build

1. Explore the data and define churn
2. Engineer usage, billing and support features
3. Train and compare logistic regression and gradient boosting
4. Choose a threshold based on the cost of a missed churner
5. Explain predictions and present a call list

Free, open data to build it with

• Telco Customer Churn ↗ Kaggle (IBM sample data)
• KKBox churn prediction challenge ↗ Kaggle

Check each dataset's licence before you publish your work. Your mentor gives your team its own version of the brief (data, scope or region), so every team's project is its own.