Investigate a Surge of Incorrect-Delivery Complaints at Scale

A manager tells you that customer support is seeing a sudden spike in complaints about orders delivered incorrectly, involving thousands of orders, but gives no additional information. What do you do next?

Requirements and Constraints

The platform handles a very high total delivery volume. Manually reviewing every reported order is not feasible. Describe how you would clarify and measure the issue, identify patterns, organize a response, and confirm recovery. Do not presume a specific cause—such as a courier, restaurant, or software deployment—until evidence supports it.

Clarifying Questions

• In the complaints, what does "incorrectly delivered" actually refer to: wrong destination, wrong contents, missing items, or a status mismatch?
• Is the complaint rate rising relative to delivered orders, or is only the raw count of complaints going up?
• Which time window, locations, service versions, and order types are impacted?
• Which identifiers and timestamps can connect support records to order, dispatch, and delivery events?

Hint — Use manual inspection to test a pattern: A small, deliberately chosen sample can confirm an aggregate hypothesis. It should not take the place of a scalable view of all affected orders.

What a Strong Answer Covers

• An early severity assessment, clear incident ownership, and requests for actionable examples and data.
• Definitions and denominators that separate a genuine rise in delivery errors from changes in support intake or order volume.
• Automated joins, grouping, and anomaly comparisons that narrow down the affected population.
• Representative manual checks of possible explanations without reviewing thousands of orders one by one.
• Proportionate mitigation, stakeholder communication, and evidence that errors and complaints are recovering.
• Follow-up monitoring and ownership of corrective work.

Follow-up Questions

1. What would you check if complaints doubled while total deliveries also doubled?
2. How would you avoid concluding that the busiest delivery region caused the incident just because it has the most complaints?
3. What evidence would justify rolling back a recent change, and how would you verify that the rollback helped?

Overview: Triage thousands of delivery complaints by using rates, joined operational data, focused sampling, mitigation, and evidence of recovery.