Metrics question
Activation is strong in Linear, teams sign up quickly and complete onboarding, but adoption of an advanced planning feature stays low after the first two weeks. How would you diagnose whether the issue is discoverability, weak product value, poor user-feature fit, or go-to-market positioning, and how would you decide what to change first?
- Linear
- Metrics
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Tests diagnosing weak feature adoption after strong onboarding across four plausible causes, discoverability, value, fit, and go-to-market, and deciding what to fix first with evidence.
How to approach it
- Check discoverability first: what percent of activated teams even open or trigger the advanced planning feature within the first two weeks, since a discoverability problem shows up as near-zero engagement, not just low retention.
- If discoverability is fine but usage still drops, check value: do teams who try it complete a meaningful planning task, or do they abandon mid-flow, which points to weak value delivery in the feature itself.
- Check user-feature fit: segment by team size or workflow maturity, since advanced planning may simply not matter yet for smaller or less structured teams, which would show up as high adoption in one segment and none in another.
- Check go-to-market positioning: was the feature communicated with a clear before-after value proposition, or just announced as available, since positioning gaps look identical to product gaps in raw usage data.
- Interview a handful of teams that tried once and stopped, since their specific reason will disambiguate between these causes faster than aggregate data alone.
- Fix in order of evidence strength: a discoverability problem is usually the cheapest and fastest fix, so address it first if data points there, before investing in deeper product changes.
What a strong answer includes
- Sequences the diagnosis by cheapest-to-verify first, discoverability, before assuming a harder product or fit problem.
- Uses segment-level data, team size or maturity, to catch a fit problem that would be invisible in aggregate adoption numbers.
- Explicitly separates a positioning problem from a product problem, since both produce the same low-usage number.
- Grounds the diagnosis in direct interviews with try-once-and-stop teams to disambiguate between the four causes quickly.
Common mistakes
- Assuming weak value or product quality without first checking the cheaper discoverability explanation.
- Looking only at aggregate adoption numbers, missing a segment-specific fit problem hidden in the average.
- Treating a positioning or messaging gap as if it were a product quality issue, prescribing the wrong fix.
Likely follow-up questions
- How would you separate a discoverability problem from a genuine value problem in the data?
- What would you do if all four causes seem to contribute a little?
More metrics questions
- Linear is launching a major feature for product and engineering teams. How would you define the positioning, choose the target audience, equip sales and marketing to tell a consistent story, and determine whether the launch messaging is actually landing in the market?Linear · Metrics · Hard
- How would you measure the performance and health of a Netflix Original?Microsoft · Metrics · Hard
- Imagine you are the PM in charge of Reactions on Facebook - the new way to interact with posts by using “love”, “haha”, “wow”, “sad”, and “angry” reactions. What would success look like in terms of number of non-like reactions per post at launch and how do you come up with this? Would this number differ by reaction? Why or why not?Meta · Metrics · Hard
- You launched a new signup flow to encourage new users to add more profile information. A/B test results indicate that the % of people that added more information increased by 8%. However, 7 day retention decreased by 2%. What do you do?Google · Metrics · Hard
- Facebook Ads dropped by 20%. Mark Zuckerberg calls you and asks you to fix it. What do you do?Meta · Metrics · Hard
- Imagine you're the product manager for Facebook Marketplace. Since many sellers don't mark items as sold, what existing functionality and metrics could you use to determine whether an item has likely sold?Meta · Metrics · Hard
More questions from Linear
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