Data Driven Decision Making Questions

Using metrics and analytics to inform operational and strategic decisions. Topics include defining and interpreting operational measures such as throughput cycle time error rates resource utilization cost per unit quality measures and on time delivery, as well as growth and lifecycle metrics across acquisition activation retention and revenue. Emphasis is on building audience segmented dashboards and reports presenting insights to influence stakeholders diagnosing problems through variance analysis and performance analytics identifying bottlenecks measuring campaign effectiveness and guiding resource allocation and investment decisions. Also covers how metric expectations change with seniority and how to shape organizational metric strategy and scorecards to drive accountability.

MediumTechnical

0 practiced

Explain correlation versus causation using a product example where naive correlation could mislead decisions (e.g., ads correlate with retention). Describe two practical methods (besides randomized experiments) you could use to strengthen causal claims in product analytics and when each is appropriate.

HardTechnical

0 practiced

Retention has declined across multiple cohorts after a product release. Propose a rigorous causal analysis plan to determine whether the release caused the decline: include data validation steps, choice of counterfactuals (e.g., difference-in-differences, synthetic control), segment-level checks, possible confounders, and robustness checks. Explain how you'd present findings and confidence to executives.

EasyTechnical

0 practiced

Describe the AAARRR funnel (Acquisition, Activation, Retention, Revenue, Referral) for a subscription-based SaaS product. For each stage specify 2–3 concrete metrics (include metric name, definition, and how to compute it from events or transactions), explain how you'd instrument them in analytics, and give one product action your team could take to improve that stage.

EasyTechnical

0 practiced

Explain leading versus lagging indicators for product teams. Provide two examples of leading indicators and two examples of lagging indicators for a two-sided marketplace, and describe how you would use leading indicators to forecast future performance and trigger pre-emptive action.

HardTechnical

0 practiced

Explain the statistical problem of 'peeking' in experiments and why continuous checks inflate false positive rates. Describe valid sequential testing approaches (for example: alpha-spending functions, group sequential tests, and Bayesian sequential methods) and recommend how to implement stopping rules and guardrails in an experimentation platform used by many product teams.

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