This is a big question and I think I underestimated how much they wanted me to slow down on the hypothesis piece.
Start by clarifying the business goal and defining a clear hypothesis for the cashback program, then outline the experimental design (randomization, sample size, duration) and the key metrics (primary, secondary, guardrail). Finally, explain how you'd analyze results, including statistical tests, segmentation, and potential pitfalls like novelty effects or network effects.
Pro tip: Emphasize the importance of pre-registering the analysis plan and considering PayPal's two-sided network (buyers and merchants) to avoid biased results. Also, mention that you'd monitor for cannibalization of existing payment methods and adjust for multiple comparisons.
Articulate a clear hypothesis (e.g., cashback increases checkout conversion) and select primary metric (e.g., conversion rate), secondary metrics (e.g., average order value, repeat purchase rate), and guardrail metrics (e.g., profit margin, customer support contacts).
Determine randomization unit (user-level), control and treatment groups, sample size via power analysis, and duration to capture full behavior cycles. Consider stratification by user segments and ensure no contamination.
Specify data requirements: user IDs, timestamps, transaction details, cashback redemption, and covariates. Implement logging and check for data quality issues like missing values or sample ratio mismatch.
Use appropriate statistical tests (e.g., t-test, bootstrap) to compare metrics, calculate confidence intervals, and perform segmentation analysis. Check for novelty effects and adjust for multiple testing.
Assess practical significance, weigh trade-offs between metrics, and provide actionable recommendations (e.g., roll out, iterate, or stop). Consider long-term impact and potential network effects.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.