This is the kind of question where you think you're doing fine and then the follow-ups expose all the stuff you glossed over.
Start by defining clear hypotheses about how the Shopping feature affects user engagement and revenue, then design a randomized controlled experiment (A/B test) with appropriate metrics and guardrails. Address potential biases like selection bias and network effects by using techniques such as cluster randomization or switchback testing, and plan for robust analysis.
Pro tip: At Meta, it's crucial to consider both short-term and long-term effects, and to use holdout groups to measure incremental impact. Also, be mindful of cross-platform interactions and novelty effects.
Clearly state the primary hypothesis (e.g., Shopping increases engagement and revenue) and secondary hypotheses. Define what success looks like for both engagement and revenue.
Decide on randomization unit (user-level, cluster-level) and address network effects. Consider using cluster randomization, switchback testing, or a holdout group to isolate the feature's impact.
Identify key metrics: engagement (time spent, likes, comments, shares), revenue (ad revenue, purchase conversion), and guardrail metrics (user retention, satisfaction). Define minimum detectable effect and statistical power.
Mitigate selection bias through proper randomization and stratification. Handle network effects by using cluster randomization or measuring spillover. Consider novelty effects and seasonality.
Use appropriate statistical methods (e.g., t-tests, regression, CUPED) to analyze results. Check for heterogeneous treatment effects and long-term impact. Make a recommendation based on statistical and practical significance.
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