Metrics question
Code to Design adoption is below target even though the core conversion technology appears acceptable. How would you diagnose whether the constraint is awareness, discoverability, activation friction, output quality, trust, or poor workflow fit, and what metrics or experiments would you use to decide what to fix first?
- Figma
- Metrics
- Hard
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What this question tests
Tests structured diagnosis of low feature adoption despite acceptable core technology, separating awareness, discoverability, activation, quality, and trust as distinct causes.
How to approach it
- Check awareness first: what percent of eligible users have even seen or heard about the Code to Design feature.
- Check discoverability: among aware users, how many can actually find the entry point within their existing workflow.
- Check activation friction: among users who find it, how many successfully complete a first conversion versus abandoning partway through.
- Check output quality and trust: among users who complete a conversion, do they keep or discard the result, and what do they say about its usability.
- Use a funnel breakdown across these stages to find where the biggest relative drop off is, rather than assuming based on anecdote.
- Prioritize the fix matched to the biggest drop off stage, running a targeted experiment there before investing broadly across all five possible causes.
What a strong answer includes
- Breaks the diagnosis into a clear funnel, awareness to discoverability to activation to quality to trust, rather than treating low adoption as one vague problem.
- Uses downstream behavior, whether output is kept or discarded, as a sharper trust and quality signal than adoption rate alone.
- Prioritizes the fix based on where the funnel data shows the biggest drop off rather than guessing which of the five causes matters most.
Common mistakes
- Assuming low adoption is a quality problem without first checking simpler explanations like awareness or discoverability.
- Investing broadly across all five possible causes at once instead of prioritizing based on actual funnel data.
Likely follow-up questions
- How would you measure discoverability specifically within the existing product workflow?
- What would you do if awareness is high but activation still fails at a high rate?
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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