Investigate a Surge of Incorrect-Delivery Complaints at Scale

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? ```hint 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. ``` ### What a Strong Answer Covers - An initial severity assessment, incident ownership, and requests for actionable examples and data. - Definitions and denominators that distinguish a real delivery-error increase from changes in support intake or order volume. - Automated joins, grouping, and anomaly comparisons that narrow the affected population. - Representative manual checks of candidate explanations without examining thousands of orders individually. - 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 the total number of deliveries also doubled? 2. How would you avoid concluding that the busiest delivery region caused the incident merely 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?