I jumped straight to metric selection and kind of glossed over the setup stuff like randomization unit and sample size.
Start by clarifying the campaign's goal and the key metric you want to move, then outline a structured A/B test plan covering hypothesis, randomization, sample size, and success criteria. Emphasize how you would ensure validity and interpret results to make a data-driven decision.
Pro tip: Highlight the importance of defining a clear primary metric and guardrail metrics upfront, and mention how you would handle common pitfalls like novelty effects or network effects, especially in a marketplace like Uber.
Clarify the campaign's goal (e.g., increase conversions, engagement) and state a testable hypothesis about how the change will impact a specific metric.
Determine the control and treatment groups, randomization unit (e.g., user, session), and ensure proper sample size and power calculation to detect a meaningful effect.
Choose a primary success metric, secondary metrics, and guardrail metrics to monitor for unintended negative consequences.
Launch the test, monitor for data quality, and ensure no external factors bias the results; avoid peeking at results prematurely.
After the test duration, analyze results for statistical significance, practical significance, and segment-level insights; decide whether to roll out, iterate, or abandon.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.