Amazon·Product Manager·Onsite - Product Sense / Strategy·Senior

Senior

Jun 2026

Summary

Amazon PM interview with a classic metrics disaster scenario. One question, pretty open-ended, and I spent way too long trying to sound structured instead of just thinking through it out loud.

Questions Asked (1)

Amazon orders are down 25% week over week. What do you do?

Product Analytics & MetricsRoot Cause AnalysisAdaptability & Ambiguity

Author's notes

I jumped straight to external factors (competitor promo, seasonality) and the interviewer just kind of waited.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the metric definition and context, then systematically break down the 25% drop by segmenting data across dimensions like time, geography, product category, and customer cohorts. Prioritize hypotheses based on impact and likelihood, validate with data, and propose actionable next steps while considering both short-term fixes and long-term learnings.

Pro tip: Demonstrate Amazon's leadership principles by showing bias for action and customer obsession—acknowledge the need to dive deep but also act quickly to mitigate customer impact. Avoid jumping to solutions before understanding the root cause, but be prepared to discuss immediate containment steps.

1. Clarify and Define

Ask clarifying questions to ensure you understand the metric: Is it orders or revenue? Week-over-week compared to which weeks? Is it global or specific to a region? What is the baseline seasonality? Confirm the data source and reliability.

2. Segment and Localize

Break down the drop by dimensions such as time (daily trend), geography, product category, customer type (new vs. returning), device, and traffic source. Identify which segments are driving the decline and whether it's broad or concentrated.

3. Generate Hypotheses

Brainstorm potential root causes: internal (site issues, pricing changes, inventory stockouts, marketing campaign pause) and external (competitor promotion, seasonality, economic factors, weather). Prioritize based on data signals and business context.

4. Validate and Prioritize

Use data to test hypotheses: check site analytics, A/B tests, customer feedback, and external benchmarks. Determine the most likely cause(s) and quantify impact. Consider correlation vs. causation.

5. Act and Communicate

Propose immediate mitigation (e.g., fix bug, launch promotion) and long-term preventive measures. Outline how you would communicate findings to stakeholders and monitor recovery. Emphasize learning and iteration.

Key Points to Mention

Segment the data by dimensions like geography, product category, customer cohort, and device to isolate the drop.
Check for internal factors: site outages, pricing errors, inventory issues, or marketing changes.
Consider external factors: competitor actions, seasonality, macroeconomic trends, or weather events.
Use statistical rigor: compare to expected seasonality, check for statistical significance, and avoid correlation-causation fallacies.
Prioritize actions based on impact and urgency, and communicate a clear plan to stakeholders.
Demonstrate Amazon leadership principles: customer obsession, bias for action, dive deep, and ownership.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.