The experiment design part I felt okay about.
Start by outlining a rigorous A/B test design with clear hypotheses, primary and guardrail metrics, and power analysis. Then, when addressing the conflicting signals, emphasize that conversion rate and AOV must be evaluated together using a north-star metric like revenue per user, and consider segment-level impacts and long-term effects. Finally, recommend a decision framework that weighs statistical significance, practical significance, and business strategy, and propose next steps such as deeper analysis or a follow-up experiment.
Pro tip: Always tie the metrics back to the company's overarching goal (e.g., total revenue or profit) and show that you understand trade-offs; product teams appreciate a clear recommendation with quantified risks rather than just data.
Clearly state the hypothesis, primary metric (e.g., conversion rate), secondary metrics (e.g., AOV), and guardrail metrics (e.g., revenue per user, customer satisfaction). Include sample size and duration calculations.
Examine both conversion rate and AOV, and compute a combined metric like revenue per user. Check for statistical significance and confidence intervals for each metric.
Break down results by user segments (e.g., new vs. existing, geography, device) to see if the drop in AOV is concentrated in certain groups. Consider long-term value and repeat purchase behavior.
Assess whether the increase in conversion compensates for the decrease in AOV in terms of total revenue or profit. Consider strategic factors like market competitiveness and user acquisition.
Provide a clear recommendation (launch, not launch, or iterate) backed by data, and suggest follow-up actions such as a longer-term holdout or a modified experiment to optimize the payment method.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.