Emphasizes connecting metric design to product strategy and business outcomes. Covers metric taxonomy such as north star metric, outcome metrics, driver metrics, and leading versus lagging indicators, governance and ownership of metrics, and preventing metric gaming. Includes thinking about long term versus short term trade offs, how to influence product direction through metric design, attribution challenges, prioritizing instrumentation and data science investment, and communicating metric driven insights to stakeholders. Appropriate for senior level discussions where metrics inform strategy, roadmap decisions, and organizational alignment.
MediumSystem Design
0 practiced
You must design an executive dashboard for a consumer marketplace showing: the north star metric, top outcome metrics (revenue, GMV), driver metrics (supply, demand), and guardrail metrics (fraud, refunds). Requirements: near-real-time for critical alerts, daily summaries, role-specific views (CEO, PMs, Ops), explainability of numbers, and minimal cognitive load. Describe the high-level layout, data sources, refresh strategy, access controls, alerting strategy, and how you'd validate dashboard numbers.
MediumTechnical
0 practiced
Discuss trade-offs between measuring metrics at the user-level (unique users) vs. session-level (sessions). Provide scenarios where each is more appropriate, how to deduplicate overlapping events across sessions, and the impact on metric stability and interpretation.
HardTechnical
0 practiced
How would you detect survivorship bias when calculating retention rates for cohorts? Describe the statistical issues that can arise, diagnostics you would run (e.g., comparing entry cohorts, attrition curves), and techniques such as censoring, Kaplan-Meier curves, or intent-to-treat analyses to produce unbiased retention estimates.
MediumTechnical
0 practiced
Near-real-time dashboards sometimes show incomplete or delayed data. Propose techniques to handle missing or delayed events while keeping stakeholders informed: include data freshness indicators, imputation strategies, last-known-good snapshots, confidence bands, and automated backfill procedures. Discuss trade-offs for each approach.
MediumTechnical
0 practiced
Describe a process to align product metrics to company OKRs and the product roadmap. Include how to cascade metrics from company-level OKRs down to team-level KPIs, how to set measurable targets, and how you'd resolve conflicts when team-level metrics diverge from company objectives.
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