Investigate a Surge of Incorrect-Delivery Complaints at Scale
DoorDashSoftware Engineer · Onsite
Last updated
Triage thousands of delivery complaints using rates, joined operational data, focused sampling, mitigation, and evidence of recovery.
A manager reports that customer support has received a sudden surge of complaints about incorrectly delivered orders, affecting thousands of orders, and provides no further detail. What would you do next?
Requirements and Constraints
The service has a large overall delivery volume. You cannot inspect every reported order manually. Explain how you would clarify and quantify the problem, find patterns, coordinate a response, and verify recovery. Do not assume a particular cause, such as a courier, restaurant, or software deployment, before evidence supports it.
Clarifying Questions
- What does "incorrectly delivered" mean in the complaints: wrong destination, wrong contents, missing items, or a status mismatch?
- Is the complaint rate increasing relative to delivered orders, or only the total number of complaints?
- Which time window, locations, service versions, and order types are affected?
- What identifiers and timestamps can link support records to order, dispatch, and delivery events?
Hints
Hint 1
Use manual inspection to test a pattern: A small, deliberately selected sample can validate an aggregate hypothesis. It should not replace a scalable view of all affected orders.
Follow-up questions
- What would you check if complaints doubled while the total number of deliveries also doubled?
- How would you avoid concluding that the busiest delivery region caused the incident merely because it has the most complaints?
- What evidence would justify rolling back a recent change, and how would you verify that the rollback helped?
Solution walkthrough is for members
Get the full walkthrough with reference code and complexity analysis, plus Kitty Coach at 300 messages a day. Hints, the code runner and community answers stay free.