Explain false positives and negatives for churn

WHAT THIS TESTS: Whether you can drop the jargon and explain model errors in terms a product manager acts on, connecting each error type to a concrete business cost and to the lever you can pull, the decision threshold.

A GOOD ANSWER COVERS: Frame both errors around the customer, not the matrix. A false positive is when the model says a customer will churn but they actually would have stayed. The business cost is whatever you spend reacting, a retention discount or outreach, given to someone who never needed it, plus possible annoyance. A false negative is when the model says a customer is safe but they actually leave. That cost is the lost customer and their entire future revenue, usually far larger. Then explain the lever: the model outputs a probability, and where you set the threshold trades the two errors against each other. Lowering it catches more real churners but wrongly flags more loyal ones. So the right threshold depends on the relative costs, and if losing a customer dwarfs the price of an offer, you accept more false positives.

COMMON WRONG ANSWERS: Reciting precision, recall, and confusion-matrix definitions with no business meaning. Treating both errors as equally costly when they rarely are.

LIKELY FOLLOW-UPS: How would you pick the threshold given specific costs? Which metric, precision or recall, matters more here? How would you measure the actual savings after deployment?

ONE CONCRETE EXAMPLE: A retention offer costs ten dollars, but a lost customer is worth two hundred. The analyst tells the PM that wrongly flagging a loyal customer costs ten dollars, while missing a real leaver costs two hundred, so the model is tuned to catch more churners even though that means handing some discounts to people who would have stayed anyway.