Product Metrics and Health Questions

Designing and using product specific metrics to measure user experience product health and business impact. Topics include identifying a north star metric and supporting metrics at company product and feature levels, measuring activation adoption engagement retention daily active users and monthly active users feature adoption rates and time to value, using product telemetry experimentation and funnel analysis to measure feature impact, and connecting product metrics to monetization and strategic objectives. Candidates should be able to propose metrics for new features justify trade offs instrument tracking and explain how product metrics inform prioritization roadmap and stakeholder alignment.

EasyTechnical

0 practiced

You have events(user_id, event_time, event_name). Write a SQL query to compute the funnel conversion from 'signup' -> 'onboard_complete' -> 'first_purchase' for users who signed up in the last 30 days. Return counts and conversion percentages between steps and overall conversion from signup to purchase.

MediumTechnical

0 practiced

Create a plan to measure the long-term (90-day) impact of a UX redesign on revenue. Describe experiment vs observational approaches, what cohorts you'd track, key metrics, and how you'd account for churn and seasonality.

HardTechnical

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A product manager and sales leader disagree on prioritizing a new feature. As a data analyst, outline a plan to use metrics and analyses to facilitate alignment. Include one experiment or analysis you would run, what data you'd present, and how you'd translate results into a recommendation.

HardTechnical

0 practiced

Design a transparent, explainable composite product health score for a consumer app combining engagement, retention, and quality signals. Describe component metrics, weighting rationale, normalization approach, and how you'd surface drivers when the score changes.

MediumTechnical

0 practiced

You need to compute the percent of users who adopt a feature within 14 days of exposure, but events arrive late and may be backfilled. Describe how you would handle late-arriving data in your measurement pipeline and how that affects reporting and confidence in near-real-time metrics.

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