Start by clarifying the business objectives and defining primary vs. guardrail metrics, then design a randomized experiment with appropriate unit (e.g., dasher or order) and stratification to ensure balance. Address practical challenges like novelty effects, heterogeneous treatment effects, and conflicting metrics by pre-registering a decision framework that weighs trade-offs.
Pro tip: Propose a switchback or cluster randomization if interference between orders is a concern, and emphasize the importance of pre-registering the analysis plan to avoid p-hacking when metrics conflict.
Clearly specify primary metrics (order conversion rate, ETA accuracy, dasher hourly earnings, restaurant prep-time congestion) and guardrail metrics (e.g., customer satisfaction, dasher safety). State directional hypotheses for each.
Select randomization unit (e.g., dasher, order, or time-based switchback) based on interference risk. Stratify by key covariates like market, time of day, and restaurant density to improve power and balance.
Conduct power analysis for each primary metric, accounting for multiple comparisons. Use historical data to estimate variance and minimum detectable effect (MDE), then calculate required sample size per arm.
Plan for novelty effects by running the experiment long enough and analyzing early vs. late periods. Pre-specify subgroups (e.g., dasher tenure, restaurant type) to explore heterogeneous treatment effects.
Define a decision framework that prioritizes primary metrics while respecting guardrails. Specify thresholds for success, failure, and conditions for further iteration when metrics conflict (e.g., primary improves but guardrail degrades).
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