What to expect, what they’re really testing, and what a strong answer looks like — scored.
Three-sided marketplace (consumers, Dashers, merchants), last-mile logistics, and unit economics. DoorDash PMs must understand how decisions affect all three sides simultaneously and reason carefully about the trade-off between delivery speed, Dasher pay, and merchant take rates.
The question below was asked by DoorDash interviewers. The answer is graded on the five dimensions real PM interviewers use: structure, specificity, reasoning, decision quality, and delivery.
“DoorDash is seeing an increase in late deliveries on Friday nights in urban markets. What do you investigate?”
Late deliveries on Friday nights in urban markets could come from three places: not enough Dashers (supply shortage), restaurants being slower (kitchen capacity), or route inefficiency (dispatch or navigation issues). I'd investigate all three but triage them quickly.
First, I'd pull delivery time breakdown data: time from order placed to Dasher assigned, time from Dasher assigned to pickup, time from pickup to delivery. If the first interval is long, it's a supply issue. If the second interval is long, it's a restaurant/kitchen issue. If the third is long, it's a route or navigation issue.
My hypothesis: Friday nights in urban markets are a supply shortage problem because demand spikes faster than Dasher supply can respond. Dashers don't know demand will spike until they're already online, so there's no pull mechanism to get more Dashers on the road before the spike hits.
Investigation steps: compare Dasher-hours online on late-delivery Fridays vs. Fridays with normal delivery times. If there's a gap, look at what Dasher incentives (boosts, guaranteed minimums) were active that night. If no incentives were active, that's a solvable supply problem.
Proposed quick fix: introduce predictive surge alerts sent to nearby Dashers 45 minutes before the historical Friday peak in high-volume zip codes. 'Earnings typically spike at 7pm tonight in your area.' This is a notification problem, not a pay problem.
Success metric: on-time delivery rate on Friday nights in the affected markets (primary). Secondary: Dasher-hour supply in peak windows. Guardrail: don't increase Dasher incentive spend more than the cost of the late-delivery refunds we're currently issuing.
Three-hypothesis framework, triage approach with data, and targeted investigation path before proposing a solution.
Names specific time intervals to analyze, a specific intervention (predictive surge alerts), and a guardrail tied to current costs.
The 'pull mechanism' framing for Dasher supply is correct; the prediction-vs-pay distinction is non-obvious.
Commits to a specific intervention after investigation; guardrail metric shows cost awareness.
Tight; the delivery time breakdown framework is efficient and reusable.
The strength is the three-interval diagnostic framework — it's the right tool for decomposing delivery failures and it lets the answer stay grounded in data before jumping to solutions. The predictive surge alert is a smart, low-cost intervention hypothesis. The weakness is that the answer doesn't address the restaurant side at all — kitchen delays are a major cause of late DoorDash deliveries and a sharp interviewer will ask about it.
Add one paragraph on the kitchen delay hypothesis — specifically, how you'd use the 'Dasher assigned to pickup' interval to identify restaurants that are chronically over-promising on prep time.
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