Netflix Engagement Diagnosis: A Million Paying Accounts With No Logins
Company: Google
Role: Product Manager
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Other
You are on the Netflix product team. One million subscribers whose subscriptions are active are not logging in to the service. How would you investigate what is going on?
```hint Size it before you chase it
Before listing causes, decide what the number is measured against: how big a million is relative to the subscriber base, and what the same figure looked like in earlier periods.
```
### Clarifying Questions
- Does "not logging in" mean these members have no viewing sessions at all, or that they have been signed out and cannot get back in?
- Over what period have they been absent, and is this a sudden jump or a slow build-up compared with before?
- Do we already know whether it is concentrated in a device type, app version, region or plan?
- Has anyone confirmed that the session and login data behind this number is complete and accurate?
### What a Strong Answer Covers
- A precise definition of the problem, its time window and baseline, and a judgment of how serious the number is
- Verification of the data and instrumentation before treating the signal as real
- A structured set of possible causes, internal and external, technical and behavioral, checked in a sensible order with the evidence each would leave
- The segmentation cuts that would isolate the cause and the data or tests that would confirm it
- Next steps once a cause is found, including the effect on retention and how the issue would be monitored
### Follow-up Questions
- Suppose the absent members are spread evenly across devices, regions and signup dates. Where would you look next?
- If most of them have simply stopped watching but still pay, what would you do, and what are the risks of contacting them?
- What alerting would you put in place so the team spots a pattern like this sooner?
Overview: A metric-diagnosis question for a Netflix product team: one million subscribers with active subscriptions are not logging in. It tests how a candidate defines the problem, sizes it against a baseline, verifies the data, segments by device, region and billing channel, and orders hypotheses before proposing next steps.