Metrics question
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
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What this question tests
Funnel diagnosis and experiment prioritization skill across a full consumer monetization lifecycle.
How to approach it
- Pull conversion rates at each funnel stage, landing page to paywall view, paywall to checkout start, checkout to completed purchase, activation, and renewal, to find the steepest drop off.
- Segment the biggest drop off by cohort, for example by acquisition channel or device, to see if the leak is broad or concentrated in one segment.
- Estimate leverage as drop off rate times volume times downstream value, since fixing a small leak in a high volume top of funnel stage can beat a bigger percentage fix deep in the funnel.
- Generate hypotheses for the top two to three leverage points, for example a confusing paywall message, a checkout friction step, or weak onboarding that hurts activation and later renewal.
- Prioritize the first three experiments using an effort versus impact framework, favoring cheap, fast tests on the highest leverage stage first.
- Define the guardrail metric for each experiment, such as ensuring a paywall change does not just shift revenue timing without growing it.
What a strong answer includes
- Uses a leverage formula, drop off times volume times value, instead of just chasing the largest percentage gap.
- Names concrete hypotheses tied to real funnel stages, not generic improve the paywall.
- Segments the diagnosis by cohort to avoid over fixing a problem specific to one channel.
- Defines a guardrail metric alongside each experiment's primary metric.
Common mistakes
- Picking the stage with the worst raw conversion rate without weighting by volume or downstream value.
- Proposing experiments with no guardrail, risking a short term win that hurts retention.
Likely follow-up questions
- How would you decide between a quick paywall copy test and a deeper checkout redesign?
- What would make you stop an experiment early?
More metrics questions
- 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
- 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
- Suno launches a beta for team workspaces. Creation and weekly usage are strong, but very few accounts expand after the first month. How would you diagnose the problem, which metrics would you inspect first, and what product or go-to-market experiments would you run next?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