This one sprawled in a way I wasn't ready for.
Start by defining clear success metrics that align with Meta's business goals, then design a robust experiment (e.g., A/B test) to measure incremental impact while accounting for cannibalization and auction dynamics. Finally, proactively address risks like selection bias and auction interference, and propose mitigation strategies.
Pro tip: Emphasize the importance of measuring incremental lift through user-level randomization and holdout groups, and discuss how to detect and correct for auction interference using techniques like switchback tests or auction-level randomization.
Identify primary and secondary metrics that capture both advertiser value (e.g., ad performance, ROI) and user experience (e.g., engagement, satisfaction). Include guardrail metrics to monitor potential negative effects.
Propose a randomized controlled experiment (A/B test) with proper randomization unit (e.g., advertiser or user) and sufficient power. Consider using a holdout group to measure incremental impact.
Analyze treatment effect on key metrics, and assess cannibalization by comparing organic vs. AI-assisted ad creation. Use techniques like difference-in-differences or causal inference to isolate incremental lift.
Identify potential selection bias (e.g., early adopters) and auction interference (e.g., changes in bid landscape). Propose methods to mitigate, such as stratified randomization or auction-level analysis.
Synthesize findings to recommend launch, iterate, or abandon. Consider trade-offs between short-term metrics and long-term strategic value, and suggest next steps if launching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.