I started with the metrics layer and worked backward, which felt right in the moment but I think I skipped over some data availability assumptions too quickly.
Start by framing the business problem and the decision at hand, then outline the data and metrics you'd use to evaluate the feature's potential. Emphasize a hypothesis-driven approach that accounts for limited data, such as using proxies, Bayesian methods, or staged rollouts. Conclude with how you'd define success and make a recommendation.
Pro tip: Acknowledge the data limitation upfront and propose a pragmatic approach like a pilot in a few markets with a holdout group, rather than waiting for perfect data. This shows you can balance rigor with business velocity.
Understand what problem the geo-targeted feature solves and what decision needs to be made (e.g., launch, iterate, or kill). Align with stakeholders on the goal and constraints.
List the data needed (e.g., historical geo-level performance, user behavior, market characteristics) and evaluate what's available versus what can be proxied or collected quickly.
Select primary metrics (e.g., conversion, order frequency) and guardrail metrics (e.g., delivery time, customer satisfaction) that reflect the feature's impact and potential risks.
Propose a test design that works with limited data, such as a geo-based A/B test, switchback experiment, or Bayesian analysis with priors. Include sample size and duration considerations.
Interpret results with confidence intervals and sensitivity analysis, then provide a clear recommendation (e.g., launch, iterate, or gather more data) with expected impact and risks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This part tripped me up more than I expected.
Start by describing what a retention curve shows and how to compare test vs. control. Then, systematically generate hypotheses based on the shape, magnitude, and timing of differences, and outline a plan to validate them using additional data and statistical tests.
Pro tip: Focus on the timing of divergence—early differences suggest onboarding or initial experience issues, while later differences point to long-term value or habit formation. Also, consider novelty effects and ensure your retention metric is correctly defined (e.g., day-7 retention vs. rolling retention).
Explain that a retention curve plots the percentage of users still active over time. Compare test and control curves to identify if they diverge and when.
Look for differences in early retention (first few days), long-term retention (flattening of curve), or crossing curves. Note if the test group performs better or worse.
Based on the pattern, hypothesize why the difference exists. For example, early divergence might indicate onboarding issues; later divergence might suggest changes in user engagement or value perception.
Propose analyses such as segmenting by user demographics, checking engagement metrics, or running statistical tests to confirm if the difference is significant.
If the test is positive, consider scaling; if negative, investigate root causes. Suggest further experiments to isolate the cause or improve the feature.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.