Metrics question
Suno’s mobile app has strong first-song generation, but only 18% of new creators make a second song within 7 days. How would you diagnose the drop-off, break down the funnel, choose the most important leading and lagging metrics, and prioritize the first product changes or experiments to improve retention?
- Suno
- Metrics
- Hard
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What this question tests
Tests structured retention diagnosis for a strong first experience but weak repeat creation rate, using leading and lagging metrics to prioritize fixes.
How to approach it
- Break the funnel from first song completion to second song attempt, checking whether users even open the app again within 7 days.
- Check whether users who do reopen the app struggle to start a second song, versus users who never reopen at all, since these point to different fixes.
- Review satisfaction with the first song itself, since a mediocre first output would suppress motivation to try again regardless of app usability.
- Track leading indicators: day 1 and day 3 return rate, and second song attempt rate among returners.
- Track lagging indicators: 7 day and 30 day second song completion rate, and overall creator retention curve shape.
- Prioritize the fix based on where the funnel breaks: a first song quality fix if satisfaction is low, or a re-engagement nudge if users simply never return to try again.
What a strong answer includes
- Separates never returning from returning but not completing a second song, since the fix, re-engagement versus in app flow improvement, is completely different for each.
- Checks first song satisfaction as a root cause hypothesis, recognizing that low quality output would suppress the will to create a second song regardless of retention tactics.
- Uses leading indicators, day 1 or day 3 return rate, to catch the problem early rather than waiting for the lagging 7 day metric alone.
Common mistakes
- Assuming the 18 percent figure is purely a product usability problem without checking whether first song quality itself is the underlying cause.
- Only tracking the lagging 7 day metric without a leading indicator that would let the team react faster.
Likely follow-up questions
- How would you measure first song satisfaction directly rather than inferring it from return behavior?
- What experiment would you run first if the data shows users return but abandon before finishing a second song?
More metrics questions
- You're given Suno's consumer revenue funnel spanning acquisition landing page → paywall → checkout → activation → renewal/winback. How would you diagnose the highest-leverage opportunities across paywalls, purchase and upgrade flows, pricing, offers, and winback, and what framework would you use to prioritize the first three experiments?Suno · Metrics · Hard
- Suppose free-to-paid conversion rose 15% after recent pricing, paywall, and onboarding changes, but 30-day churn also rose. How would you determine whether Suno is acquiring lower-intent subscribers versus creating an activation or expectations problem, and what actions would you take based on that diagnosis?Suno · Metrics · Hard
- Design an A/B testing plan for Suno's click-to-purchase journey from paid acquisition landing page through subscription checkout. Which hypotheses would you test first, what primary and guardrail metrics would you use, and how would you avoid short-term revenue lifts that hurt user trust or downstream retention?Suno · Metrics · Hard
- A paywall experiment increases checkout conversion, but shifts users toward a cheaper plan and lowers retention. How would you evaluate that result within Suno's broader pricing and packaging system, and decide whether to ship, iterate, or roll it back?Suno · Metrics · Hard
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More questions from Suno
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop