Metrics question

Netflix's subscription renewal rate is declining year over year. What hypotheses would you propose, and how would you validate them?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Tests structured hypothesis generation and validation for a churn or retention problem.

Segment first: is the decline concentrated in a plan tier, region, tenure cohort, or device and platform.
Hypothesize causes: content library fatigue, the password-sharing crackdown pushing away marginal users, price increases, and fragmentation from rising competitors like Disney+ and Max.
Validate each hypothesis: check content release cadence against renewal dips, survey churned users on stated reason, and compare renewal rate before and after the last price change.
Prioritize the hypothesis with the strongest data correlation before proposing any action.
Recommend action tied to the validated cause, for example personalized re-engagement nudges before renewal date if content fatigue is the driver.

Lists concrete, current hypotheses, password-sharing changes, price hikes, competitive fragmentation, grounded in Netflix's real recent history.
Insists on validating with segmented data before recommending a fix, not guessing first.
Ties each hypothesis to a specific, checkable data source rather than leaving validation abstract.

Proposing a fix like adding more content without first validating which hypothesis the data actually supports.
Treating renewal decline as one uniform problem across all cohorts.

How would you design a survey to validate the churn reason without biasing responses?
Which cohort would you prioritize retaining first if resources are limited?