Logistics engagement dropped 20% in two weeks among families. Diagnose the issue

Problem Statement Description

Product context: Amazon is a commerce, logistics, media, devices, and cloud company; its products include Marketplace, Prime, Prime Video, Alexa devices, ads, fulfillment, and AWS.

Amazon has observed a 20% drop over the past two weeks in logistics engagement among family households. For this case, “logistics engagement” can be interpreted as customer interaction with delivery-related experiences such as shipment tracking, delivery notifications, delivery instructions, pickup/drop-off options, returns scheduling, or other post-purchase logistics touchpoints.

You are the product manager responsible for diagnosing whether this is a real customer behavior change, a measurement issue, or the result of operational, product, seasonal, or segment-specific factors. The affected users are families who may have multiple household members ordering, coordinating deliveries, managing subscriptions, receiving time-sensitive items, and relying on convenience and predictability.

This is a root-cause analysis exercise. Your task is not to immediately propose a fix, but to structure the investigation, identify the most likely drivers, determine what evidence would confirm or reject each hypothesis, and explain how you would prioritize next steps in an Amazon-scale logistics environment.

The experience should consider:

- How “logistics engagement” is defined, including numerator, denominator, tracked events, and whether the metric reflects customer intent or system-generated activity.

- Whether the 20% decline is isolated to families or also visible across other customer segments, geographies, devices, delivery speeds, order categories, or fulfillment methods.

- Instrumentation and data-quality checks, including event logging changes, app/web releases, notification delivery issues, identity/household classification changes, or dashboard pipeline delays.

- Customer journey points where families interact with logistics, such as order confirmation, tracking, delivery rescheduling, package handoff, returns, and customer support.

- Operational hypotheses, such as delivery delays, fewer shipments, route disruptions, inventory availability, carrier performance, pickup-point changes, or changes in delivery promise accuracy.

- Product and UX hypotheses, such as notification changes, tracking page discoverability, app performance, account-sharing behavior, or changes to delivery preference flows.

- External or seasonal factors, such as school schedules, holidays, weather events, regional disruptions, competitor promotions, or category mix shifts that may affect family ordering patterns.

- How you would separate correlation from causation and decide what mitigations, monitoring, and prevention mechanisms are needed.

The goal is to demonstrate a rigorous RCA approach: frame the anomaly clearly, segment the data intelligently, validate measurement integrity, generate testable hypotheses, identify the evidence needed, and communicate a practical path toward mitigation and long-term prevention without jumping prematurely to a solution.