Metrics question
A new Figma AI capability helps users go from idea to prototype, and initial trial is strong, but 4-week repeat usage is weak. How would you determine whether the main issue is onboarding friction, poor output quality, weak workflow fit, or low trust in the results, and what data would you need to separate those causes?
- Figma
- Metrics
- Hard
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What this question tests
Tests structured diagnosis of a strong trial, weak repeat usage pattern by separating onboarding, output quality, workflow fit, and trust as distinct causes.
How to approach it
- Check onboarding friction first: do users who try it once understand how to use it again, or does each session feel like relearning the feature.
- Check output quality: sample generated prototypes and have designers rate whether they were usable as is or needed heavy rework.
- Check workflow fit: does the feature integrate into how designers actually move from idea to prototype, or does it feel bolted on and disconnected.
- Check trust: survey or interview users who tried once and did not return, asking directly whether they doubted the output's quality or relevance.
- Use a funnel breakdown, first use to second use, to see where exactly users drop off, since each cause has a different funnel signature.
- Cross reference quantitative funnel data with qualitative interviews, since a single data source cannot fully distinguish these four hypotheses.
What a strong answer includes
- Names a distinct signal for each of the four hypotheses, output quality rating, workflow fit interviews, trust survey, rather than one generic churn analysis.
- Combines quantitative funnel data with qualitative interviews, recognizing repeat usage causes are often not visible in numbers alone.
- Proposes directly asking non returning users about trust and quality, which is a faster and more direct diagnostic than inferring from behavior alone.
Common mistakes
- Assuming weak repeat usage is an onboarding problem without checking output quality or workflow fit first.
- Relying only on quantitative funnel data without any qualitative signal on why users are not coming back.
Likely follow-up questions
- How would you distinguish a workflow fit problem from an output quality problem if both show the same drop off pattern?
- What would you prioritize fixing first if all four causes show some signal?
More metrics questions
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- Figma wants students and educators to discover the product early and become long-term users. How would you segment the EDU market, choose the initial target audience, define the value proposition, and select the metrics that show the strategy is working beyond just student sign-ups?Figma · Metrics · Hard
- Figma's homepage is getting healthy traffic from design-related queries, but first-visit sign-up conversion is below target. What 3-5 experiments would you run across the homepage and sign-up flow, what hypothesis does each test, and how would you determine whether a result is a true win versus just shifting users downstream?Figma · Metrics · Hard
- Walk me through how you'd diagnose the biggest sources of friction from first visit to completed sign-up in Figma's logged-out funnel. What events, segments, and leading indicators would you examine, and how would you decide which drop-off to tackle first?Figma · Metrics · Medium
- Suppose collaboration rates and 90-day retention flatten for multi-product customers, and research suggests inconsistency in multiplayer, navigation, and core workflows is creating friction. How would you isolate the biggest sources of friction, choose the first platform intervention, and define leading and lagging metrics to know whether the changes worked?Figma · Metrics · Hard
- What metrics would you use to determine whether Figma’s AI features are creating durable user value rather than just generating curiosity-driven trial, and how would those metrics change your product decisions on onboarding, feature investment, and distribution?Figma · Metrics · Medium
More questions from Figma
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