How would you approach creating a machine learning model to predict customer churn?

Example Answer

I would start by defining churn very carefully because most churn projects fail at the label, not the algorithm. I want a clear business definition, a prediction horizon, and a decision window that gives the company time to intervene. Otherwise the model can look accurate while still being operationally useless.

Then I would build features around behavior change, engagement decay, support interactions, billing history, tenure, and product usage patterns. I would be aggressive about leakage checks because churn datasets are full of signals that only exist after the customer is already effectively gone. After that, I would choose a model that balances lift, calibration, and interpretability well enough for the retention team to trust and act on it.

I would evaluate the system on both model metrics and intervention value. A churn model is only good if it identifies users you can realistically save and helps the business spend retention effort more intelligently.

Common Poor Answer

A weak answer jumps straight to XGBoost or neural nets without defining churn, checking label leakage, or explaining how the predictions would actually drive retention actions.