Metrics question
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
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What this question tests
Funnel diagnosis fluency: can you name concrete events and segments to examine, and reason about which drop-off point to prioritize rather than trying to fix the whole funnel at once.
How to approach it
- Break the funnel into stages: landing page view, scroll or engagement with product content, sign-up form start, sign-up form completion, and first meaningful action after sign-up.
- Segment by traffic source and query intent, since a visitor arriving from a branded search behaves very differently from one arriving from a generic design-tool query.
- Segment by device, since logged-out funnels often lose a large share of mobile visitors at the sign-up form specifically.
- Look at leading indicators like scroll depth and time on page before the drop, which show whether visitors are engaging with content before they bounce, versus bouncing immediately.
- Prioritize the drop-off stage with the largest volume times the largest gap versus a reasonable benchmark, not just the stage with the lowest raw percentage.
What a strong answer includes
- Names specific funnel stages and segmentation dimensions, like device and query intent, instead of a generic funnel description.
- Uses a volume-times-gap prioritization rule, which is a concrete way to pick where to focus first among several drop-offs.
- Distinguishes engagement signals like scroll depth from conversion events, giving a fuller picture of why people drop rather than just where.
Common mistakes
- Describes the funnel abstractly without naming concrete events or segments to examine.
- Prioritizes the stage with the lowest percentage without considering the volume of traffic at that stage.
Likely follow-up questions
- Which segment would you expect to have the biggest opportunity and why.
- How would you distinguish a UX problem from a traffic-quality problem at the same drop-off point.
More metrics questions
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- 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
- 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
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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