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.

HardTechnical

0 practiced

Propose a governance model for metric ownership in a cross-functional organization where product metrics drive roadmap decisions. Include roles, responsibilities, change control for metric definitions, and how to handle disputes between PMs and data teams.

HardTechnical

0 practiced

Construct an analysis plan to attribute long-term retention improvements to a feature introduced 6 months ago, accounting for confounders like seasonality, concurrent initiatives, and cohort effects. What statistical methods and robustness checks would you use?

MediumTechnical

0 practiced

Write pseudocode (or Python) to compute retention cohorts: Day-0 signups grouped weekly and the percentage still active on days 1, 7, 30. Describe assumptions about event schema and how to scale the computation for millions of users.

MediumSystem Design

0 practiced

Design a monitoring dashboard for product health that includes at least 6 panels (metrics) covering activation, engagement, retention, revenue linkage, and instrumentation health. Describe each panel, its visualization type, and alert thresholds you would set.

EasyTechnical

1 practiced

Provide three examples of early-warning product health metrics (leading indicators) that can predict future retention problems. For each, explain why it's predictive and how you would monitor it operationally.

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