Metrics question
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
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What this question tests
Ability to design a rigorous experimentation plan with the right guardrails to avoid gaming short term revenue at the expense of trust.
How to approach it
- Start with two to three concrete hypotheses grounded in likely friction points, for example unclear plan comparison on the landing page, too many steps in checkout, or an unclear trial to paid transition.
- Sequence tests by expected impact and implementation ease, running the cheapest, highest confidence test first, likely landing page messaging or a checkout step reduction.
- Set the primary metric per test, purchase conversion rate for a checkout test, paywall to checkout start rate for a landing page test.
- Set guardrail metrics that would catch a false win, such as 30 day retention, refund rate, and support ticket volume, since a dark pattern style change could lift conversion while hurting these.
- Define the required sample size and test duration upfront to avoid stopping early on a noisy positive result.
- Set a rule: only ship a winning variant if the primary metric improves and no guardrail metric moves meaningfully worse.
What a strong answer includes
- Names concrete hypotheses tied to specific funnel friction points, not generic test everything.
- Explicitly defines guardrail metrics like retention and refund rate to catch a manipulative short term win.
- Sets a pre registered ship rule, primary up, guardrails not down, rather than deciding after seeing results.
- Addresses statistical rigor, sample size and duration, to avoid a false positive.
Common mistakes
- Optimizing only for checkout conversion with no guardrail against retention or trust erosion.
- No mention of sample size or test duration, risking premature conclusions.
Likely follow-up questions
- What would you do if the primary metric won but refund rate also rose?
- How would you sequence these tests if you could only run one at a time?
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
- 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
- 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