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

Given the following simplified events table for a mobile app:

| user_id | event_name | event_date ||---------|--------------|-------------|| 1 | first_open | 2025-01-01 || 1 | session | 2025-01-02 || 2 | first_open | 2025-01-01 || 2 | session | 2025-01-08 || 3 | first_open | 2025-01-02 |

Define Day-7 retention and compute whether users 1, 2, 3 are retained at day 7 using this table. Explain assumptions and how you would calculate Day-7 retention in SQL for a large dataset.

HardTechnical

0 practiced

Design a governance process for changing a core metric definition (e.g., 'active user' changes from any event to 'core-action'). Include stakeholder approval flow, metric versioning, backfill strategy, rollback plan, and how BI ensures continuity for historical reporting.

MediumTechnical

0 practiced

You're asked to define analytics for a new 'Groups' collaboration feature. Propose a north-star metric for the feature and 5 supporting metrics (activation, adoption, engagement, retention, monetization). Explain event names and precise instrumentation needed to compute each metric.

MediumTechnical

0 practiced

List and explain the data-quality checks you would implement on product telemetry pipelines to ensure reporting accuracy. Include checks for schema changes, event volume monitoring, uniqueness constraints, null rates, and SLA monitoring for delayed ingestion.

HardTechnical

0 practiced

Event ingestion costs are high. Propose a sampling strategy that reduces data cost but preserves the ability to detect statistically significant changes to core metrics and run experiments. Explain potential biases introduced by sampling and strategies to correct or measure them.

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