churn

Time-to-event problems (churn, failure, conversion timing) conceal a twist that quietly biases ordinary regression. What shows depth is naming that twist and choosing the right framing. Here is the answer.

A high-AUC churn model that changes no behavior is worthless. The strong answer nails the label definition, chooses classification vs survival deliberately, and is judged on retention uplift, not accuracy.

Rescuing a churning account is diagnosis under pressure: find the real reason, fix what you can, and win back trust with action. Interviewers look for a method, not a discount. Here is the move.