##### Scenario A delivery service is receiving customer complaints that meals arrive cold. ##### Question Customers complain their food is cold on delivery. How would you investigate and solve this? Detail data needed, metrics, analyses/experiments, and operational or product changes you’d recommend. ##### Hints Think root-cause analysis, A/B testing, delivery logistics, packaging, driver routing.

DoorDash data scientist case on cold meal deliveries, covering lifecycle timestamps, complaint metrics, segmentation, root-cause analysis, dispatch and packaging fixes, A/B testing, and marketplace guardrails.

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

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Investigate Causes of Cold Meal Deliveries | DoorDash Interview Question

Investigate and Reduce Cold Food Deliveries

A delivery service is receiving customer complaints that meals arrive cold. You need to investigate the root causes and recommend data-driven product or operations changes.

Constraints & Assumptions

Complaints are noisy and may reflect reporting bias; include objective proxies where possible.
Consider the full delivery lifecycle from food ready time to customer handoff.
Diagnose before prescribing a solution.
Preserve marketplace health while reducing cold-food experiences.

Clarifying Questions to Ask

Did the complaint count increase, the complaint rate increase, or both?
Is the issue concentrated by merchant, cuisine, distance, courier mode, city, daypart, or weather?
Do we have timestamps for food ready, dasher arrival, pickup, dropoff, and handoff?
Were there recent changes in batching, routing, packaging, merchant operations, or support reason codes?

What a Strong Answer Covers

Data needed from orders, merchants, couriers, customers, platform dispatch, weather, GPS, packaging, support, refunds, and ratings.
Metrics: cold complaint rate, refund rate, CSAT, cook-to-pickup time, pickup-to-dropoff time, total heat-risk time, batch size, stack position, detours, distance, packaging compliance, and courier bag usage.
Root-cause analyses by segment, time-series change point, funnel decomposition, and contribution analysis.
Data-quality checks for taxonomy changes, missing timestamps, denominator shifts, support-policy changes, and underreporting.
Candidate fixes such as dispatch timing, batching limits, insulated bags, packaging improvements, merchant throttling, route optimization, and prep-time prediction.
Experiment design with treatment/control, unit of randomization, primary metric, guardrails, duration, sample size, and rollout plan.
Guardrails for delivery time, courier earnings, cost, merchant burden, cancellation, food quality, and customer retention.

Follow-up Questions

How would you distinguish restaurant-side cooling from transit-side cooling?
Which segment should receive the first intervention?
How would you validate a predicted temperature-risk score?
What if the fix reduces cold complaints but increases delivery time?

Investigate and Reduce Cold Food Deliveries

A delivery service is receiving customer complaints that meals arrive cold. You need to investigate the root causes and recommend data-driven product or operations changes.

Constraints & Assumptions

Complaints are noisy and may reflect reporting bias; include objective proxies where possible.

Consider the full delivery lifecycle from food ready time to customer handoff.

Diagnose before prescribing a solution.

Preserve marketplace health while reducing cold-food experiences.

Clarifying Questions to Ask

Did the complaint count increase, the complaint rate increase, or both?

Is the issue concentrated by merchant, cuisine, distance, courier mode, city, daypart, or weather?

Do we have timestamps for food ready, dasher arrival, pickup, dropoff, and handoff?

Were there recent changes in batching, routing, packaging, merchant operations, or support reason codes?

What a Strong Answer Covers

Data needed from orders, merchants, couriers, customers, platform dispatch, weather, GPS, packaging, support, refunds, and ratings.

Metrics: cold complaint rate, refund rate, CSAT, cook-to-pickup time, pickup-to-dropoff time, total heat-risk time, batch size, stack position, detours, distance, packaging compliance, and courier bag usage.

Root-cause analyses by segment, time-series change point, funnel decomposition, and contribution analysis.

Data-quality checks for taxonomy changes, missing timestamps, denominator shifts, support-policy changes, and underreporting.

Candidate fixes such as dispatch timing, batching limits, insulated bags, packaging improvements, merchant throttling, route optimization, and prep-time prediction.

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

Guardrails for delivery time, courier earnings, cost, merchant burden, cancellation, food quality, and customer retention.

Follow-up Questions

How would you distinguish restaurant-side cooling from transit-side cooling?

Which segment should receive the first intervention?

How would you validate a predicted temperature-risk score?

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

Investigate Causes of Cold Meal Deliveries

Quick Overview

Investigate Causes of Cold Meal Deliveries

Investigate and Reduce Cold Food Deliveries

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer

Investigate Causes of Cold Meal Deliveries

Quick Overview

Investigate Causes of Cold Meal Deliveries

Investigate and Reduce Cold Food Deliveries

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer