How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at DoorDash.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at DoorDash during technical interviews.

Measure Impact of Merchant Variety on Consumer Experience

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 Measure Impact of Merchant Variety on Consumer Experience states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Measure Impact of Merchant Variety on Consumer Experience

Scenario

DoorDash's product team is exploring how merchant variety/selection affects consumer experience and marketplace health, and is considering expanding the breadth or evenness of available merchants.

Question

How would you define merchant variety or selection in an operational, measurable way?
What success metrics would you track at both the merchant and consumer levels, plus marketplace guardrails?
How would you design an A/B test (or an alternative quasi-experimental design) to evaluate the effect of a change in variety, and how would you analyze the results?

Hints

Define variety along breadth, depth, evenness, coverage, and novelty; use diversity indices (Shannon entropy, effective number of categories, Simpson diversity / HHI).
Cover conversion, retention, order frequency, revenue per user, plus reliability and unit-economics guardrails.
Choose the unit of randomization by the lever (user/session for a ranking change, geo-cluster for a supply change); discuss interference, power (ICC/design effect, CUPED), heterogeneous effects, and cannibalization.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

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

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

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?

Quick Overview

Measure Impact of Merchant Variety on Consumer Experience

Scenario

DoorDash's product team is exploring how merchant variety/selection affects consumer experience and marketplace health, and is considering expanding the breadth or evenness of available merchants.

Question

How would you define merchant variety or selection in an operational, measurable way?

What success metrics would you track at both the merchant and consumer levels, plus marketplace guardrails?

How would you design an A/B test (or an alternative quasi-experimental design) to evaluate the effect of a change in variety, and how would you analyze the results?

Hints

Define variety along breadth, depth, evenness, coverage, and novelty; use diversity indices (Shannon entropy, effective number of categories, Simpson diversity / HHI).

Cover conversion, retention, order frequency, revenue per user, plus reliability and unit-economics guardrails.

Choose the unit of randomization by the lever (user/session for a ranking change, geo-cluster for a supply change); discuss interference, power (ICC/design effect, CUPED), heterogeneous effects, and cannibalization.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.

If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.

Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

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

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

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?

Measure Impact of Merchant Variety on Consumer Experience

Quick Overview

Measure Impact of Merchant Variety on Consumer Experience

Measure Impact of Merchant Variety on Consumer Experience

Scenario

Question

Hints

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer

Measure Impact of Merchant Variety on Consumer Experience

Quick Overview

Measure Impact of Merchant Variety on Consumer Experience

Measure Impact of Merchant Variety on Consumer Experience

Scenario

Question

Hints

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer