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Design Experiments to Measure Promotion Scheduling Impact

DoorDashData Scientist · Onsite
MediumData ScientistOnsiteAnalytics & Experimentation

Last updated

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design Experiments to Measure Promotion Scheduling Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

Platform is releasing flexible promotion scheduling (time-of-day deals, merchant-funded discounts, broader eligibility).

Question

What business goals and success metrics should be set for this feature? How would you design and monitor an experiment to assess its impact? How would you use pre-period data when interpreting results, and would you ramp 80/20 or 50/50? Why?

Hints

Define primary KPI (incremental GMV, margin, merchant adoption); apply CUPED or diff-in-diff; weigh risk vs. speed when choosing ramp-up.