Design Experiments to Measure Promotion Scheduling Impact

Quick Overview

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.

Design Experiments to Measure Promotion Scheduling Impact

Company: DoorDash

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### 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.

Quick Answer: 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.

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Aug 4, 2025, 10:55 AM
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Design Experiments to Measure Promotion Scheduling Impact

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?

Clarifying Questions to Ask Guidance

  • Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
  • State assumptions about instrumentation, randomization, sample size, and data quality.
  • Separate descriptive analysis from causal claims.

What a Strong Answer Covers Guidance

  • A metric framework with primary, guardrail, and diagnostic metrics.
  • A credible analysis or experiment design with clear assumptions and bias checks.
  • SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
  • An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions Guidance

  • What sanity checks would you run before trusting the result?
  • How would you handle novelty effects, seasonality, or selection bias?
  • What decision would you make if metrics disagree?
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