Metrics question

How would you measure the success of Shazam?

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What this question tests

Tests metrics design for a music recognition utility: distinguishing a north star from supporting and guardrail metrics for a single-purpose app.

State Shazam's core value: instantly identifying a song a user hears, so success should track recognition usage and accuracy, not generic engagement.
Propose a north star metric such as successful identifications per active user per week.
Add supporting metrics: recognition accuracy rate, time to identify, and conversion from identification to a follow-on action like adding to a playlist or streaming.
Add retention metrics: weekly active users and repeat usage rate, since Shazam is often a low-frequency, high-utility app.
Add guardrail metrics: false-positive identification rate and app crash or latency rate, since a bad recognition breaks trust immediately.
Segment by context, such as identifications made in noisy environments (bars, concerts) versus quiet ones, since accuracy will vary.

Chooses successful identifications, not raw app opens, as the north star, since Shazam's entire value is captured in that one action.
Ties monetization and platform health together by tracking conversion to streaming (illustrative, such as 30 percent of identifications leading to a stream), which is how Shazam justifies its Apple Music integration.
Treats accuracy and latency as guardrails, not vanity metrics, since a slow or wrong result destroys the core promise.
Segments by use context (loud venue versus quiet room) to catch where accuracy actually breaks down.

Defaulting to generic app metrics like DAU or session length that do not reflect Shazam's single-purpose use case.
Ignoring accuracy and latency as core guardrails, not secondary concerns.

How would you detect that recognition accuracy is degrading before users start complaining?
How would you weigh a feature that increases session length but does not increase successful identifications?