##### Scenario A food-delivery platform is receiving a spike in complaints that delivered meals arrive cold. ##### Question How would you diagnose why customers receive cold food? What key metrics would you break down and monitor? Design an A/B test to evaluate a solution that keeps food hot. ##### Hints Think supply-chain steps, time-in-transit, batching, packaging; define leading/lagging metrics, success criteria, and assignment unit.

DoorDash analytics prompt on diagnosing cold food deliveries, covering complaint metrics, lifecycle timestamps, batching, packaging, weather, segmentation, data quality, A/B testing, and operational guardrails.

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

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

Diagnose Cold Food Deliveries with Key Metrics Analysis

Diagnose Cold Food Deliveries and Test a Fix

A food-delivery platform is receiving a spike in customer complaints that delivered meals arrive cold. You are the Data Scientist asked to diagnose the issue and evaluate a solution intended to keep food hot.

Constraints & Assumptions

Complaints are a noisy lagging signal; include leading metrics and direct measurements where possible.
Consider the full path from kitchen completion to customer handoff.
Segment by merchant, cuisine, distance, weather, batching, courier mode, packaging, and market load.
Design an experiment that measures customer benefit and operational trade-offs.

Clarifying Questions to Ask Guidance

How is "cold food" reported: complaint reason, refund request, rating text, photo, or temperature sensor?
Did complaint volume rise, complaint rate rise, or both?
Were there recent changes to batching, dispatch, packaging, merchant prep, courier supply, or weather?
What solution are we testing: insulated bags, packaging, dispatch timing, batching limits, merchant throttling, or routing?

What a Strong Answer Covers Guidance

Diagnostic framework mapping timestamps: prep complete, courier arrival, pickup, transit, drop-off, and total time at temperature risk.
Key metrics: cold complaint rate, refund rate, rating, repeat rate, cook-to-pickup time, transit time, batching/stacking, detours, distance, courier mode, weather, and packaging compliance.
Root-cause decomposition by merchant, cuisine, market, daypart, weather, order size, courier mode, and stack position.
Data-quality checks for reason-code changes, support-policy changes, denominator shifts, and complaint selection bias.
Experiment design with treatment/control, unit of randomization, eligibility, primary metric, guardrails, sample size, and duration.
Guardrails such as delivery time, cost, courier earnings, batching efficiency, merchant burden, cancellation, and customer satisfaction.

Follow-up Questions Guidance

How would you distinguish food getting cold at the restaurant from coldness during transit?
Which segment would you target first?
What if the fix reduces cold complaints but increases delivery time?
How would you measure actual food temperature?

Diagnose Cold Food Deliveries and Test a Fix

Constraints & Assumptions

Complaints are a noisy lagging signal; include leading metrics and direct measurements where possible.

Consider the full path from kitchen completion to customer handoff.

Segment by merchant, cuisine, distance, weather, batching, courier mode, packaging, and market load.

Design an experiment that measures customer benefit and operational trade-offs.

Clarifying Questions to Ask Guidance

How is "cold food" reported: complaint reason, refund request, rating text, photo, or temperature sensor?

Did complaint volume rise, complaint rate rise, or both?

Were there recent changes to batching, dispatch, packaging, merchant prep, courier supply, or weather?

What solution are we testing: insulated bags, packaging, dispatch timing, batching limits, merchant throttling, or routing?

What a Strong Answer Covers Guidance

Diagnostic framework mapping timestamps: prep complete, courier arrival, pickup, transit, drop-off, and total time at temperature risk.

Key metrics: cold complaint rate, refund rate, rating, repeat rate, cook-to-pickup time, transit time, batching/stacking, detours, distance, courier mode, weather, and packaging compliance.

Root-cause decomposition by merchant, cuisine, market, daypart, weather, order size, courier mode, and stack position.

Data-quality checks for reason-code changes, support-policy changes, denominator shifts, and complaint selection bias.

Experiment design with treatment/control, unit of randomization, eligibility, primary metric, guardrails, sample size, and duration.

Guardrails such as delivery time, cost, courier earnings, batching efficiency, merchant burden, cancellation, and customer satisfaction.

Follow-up Questions Guidance

How would you distinguish food getting cold at the restaurant from coldness during transit?

Which segment would you target first?

What if the fix reduces cold complaints but increases delivery time?

How would you measure actual food temperature?

Diagnose Cold Food Deliveries with Key Metrics Analysis

Quick Overview

Diagnose Cold Food Deliveries with Key Metrics Analysis

Diagnose Cold Food Deliveries and Test a Fix

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer

Diagnose Cold Food Deliveries with Key Metrics Analysis

Quick Overview

Diagnose Cold Food Deliveries with Key Metrics Analysis

Diagnose Cold Food Deliveries and Test a Fix

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer