Evaluate Impact of $1 Fee on Fast-Food Profitability

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 Evaluate Impact of $1 Fee on Fast-Food Profitability states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Evaluate Impact of $1 Fee on Fast-Food Profitability

Company: DoorDash

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### Scenario About 10 % of fast-food restaurants are unprofitable. Product wants to add a $1 delivery-fee surcharge to those orders to cover the deficit. ##### Question Design an experiment to evaluate the impact of the $1 fee on restaurant-level profitability, order volume, and customer satisfaction. What specific techniques would you employ to increase the statistical power of this experiment? ##### Hints Consider stratified randomization, CUPED, longer experiment horizon, geo-testing, or covariate blocking to reduce variance.

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 Evaluate Impact of $1 Fee on Fast-Food Profitability 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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Evaluate Impact of $1 Fee on Fast-Food Profitability

Experiment Design: $1 Delivery-Fee Surcharge on Unprofitable Restaurants

Scenario

About 10% of fast-food restaurants on the platform are unprofitable. Product wants to add a $1 delivery-fee surcharge to orders from those restaurants to cover the deficit.

Task

Design an experiment to evaluate the impact of the $1 fee on:

  • Restaurant-level profitability
  • Order volume (conversion and frequency)
  • Customer satisfaction

Include:

  1. Experimental unit and randomization plan (address marketplace spillovers/interference).
  2. Primary/secondary metrics and guardrails, with clear hypotheses.
  3. Sample size and duration guidance (state assumptions if needed).
  4. Analysis plan (e.g., ITT vs. TOT, variance reduction, decomposition of effects).
  5. Concrete techniques to increase statistical power (e.g., stratified randomization, CUPED, covariate blocking, geo-testing, longer horizon), and when to use each.

Assume the $1 fee is only applied to currently unprofitable restaurants. If needed, make minimal additional assumptions explicit.

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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