Design Experiments to Measure Promotion Scheduling Impact

Q: Design Experiments to Measure Promotion Scheduling Impact

This question evaluates a data scientist's competency in experimental design, causal inference, metric definition, interpretation of pre-period data, and selection of ramping strategies for a promotion scheduling feature.

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Question

Scenario

A food delivery marketplace is releasing flexible promotion scheduling (e.g., time-of-day deals, merchant-funded discounts, and broader eligibility). Merchants in treatment would be able to set up scheduled promos; control merchants continue with current tooling.

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?
Would you ramp 80/20 or 50/50? Why?

Design Experiments to Measure Promotion Scheduling Impact

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Design Experiments to Measure Promotion Scheduling Impact

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Scenario

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Solution

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