Kindle 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.

You are the PM for Kindle experiences used by families, including households where parents and children share devices, use Family Library, Kids profiles, parental controls, subscriptions, and reading progress features. Over the last two weeks, overall Kindle engagement for family users has dropped by 20%. The decline is large enough to raise concern, but the cause is not yet known.

Your task is to diagnose the issue as an RCA. Treat “engagement” as a metric that needs to be clarified before analysis: it could refer to reading sessions, minutes read, books opened, active family accounts, Kids profile usage, subscription interactions, or device/app activity. You should determine whether the drop reflects a real customer behavior change, a measurement problem, a product regression, content/selection issue, lifecycle or seasonality effect, or an external factor.

Focus on how you would structure the investigation, what data you would inspect, how you would segment the affected users, and how you would move from hypotheses to evidence. The scenario is intentionally broad, so define the scope and assumptions you need, then build a practical diagnostic path suitable for an Amazon-scale consumer product.

The experience should consider:

- How to confirm the anomaly: metric definition, baseline period, denominator, statistical significance, and whether the 20% drop is absolute or relative.

- Segmentation by family account type, Kids profiles, Prime/Kindle Unlimited usage, device versus app, geography, language, age band, acquisition cohort, and new versus existing family users.

- Instrumentation and data-quality checks, including event logging changes, app/device version releases, backend pipeline issues, identity linking, Family Library attribution, and parental-control events.

- Product and operational hypotheses such as a recent Kindle app update, device firmware change, content discovery issue, purchase or borrowing friction, subscription entitlement bug, profile switching problem, or notification change.

- Customer behavior and external factors, including school calendars, holidays, competing entertainment, pricing or catalog changes, device availability, and household reading routines.

- Evidence needed to prioritize hypotheses, such as funnel movement, error rates, crash logs, customer contacts, reviews, support tickets, A/B test exposure, release timelines, and cohort-level trend breaks.

- Short-term mitigation options, communication needs, rollback or hotfix considerations, and how to prevent recurrence through monitoring and alerting.

The goal is to demonstrate a clear, disciplined RCA approach: define the anomaly, isolate where it is happening, separate measurement issues from real behavior changes, generate plausible Kindle-family-specific hypotheses, validate them with data, and propose next steps without jumping prematurely to a single cause.