I started with instrumentation issues because that's the first place I always go and it's usually right.
Start by structuring your answer around a hypothesis-driven approach: first segment the problem (e.g., by time, user cohort, platform, geography) to localize the drop, then brainstorm potential root causes across internal and external factors. For each cause, propose specific analyses and metrics to validate or invalidate it, prioritizing the most likely and impactful ones.
Pro tip: Demonstrate business acumen by linking each root cause to a measurable metric and considering the cost of false positives/negatives in your validation. Also, mention the importance of checking data pipeline integrity early, as data issues often masquerade as product problems.
Ask clarifying questions to understand the metrics' definitions, time frame, and whether the drop is sudden or gradual. Confirm if it's isolated to specific segments (e.g., new vs. existing users, platform, region).
Break down the metrics by dimensions such as time, user cohort, device, geography, and acquisition channel to identify where the drop is concentrated. Use funnel analysis to pinpoint the stage where conversion is lost.
Generate hypotheses across categories: technical (site bugs, tracking issues), product (UI changes, feature releases), external (seasonality, competitors, market events), and user behavior (shift in traffic quality).
For each plausible cause, design analyses (e.g., A/B test results, anomaly detection, cohort analysis, correlation with external events) to confirm or rule out. Prioritize based on likelihood and impact.
Based on validated causes, suggest immediate fixes and long-term preventive measures. Propose monitoring dashboards and alerts to catch future anomalies early.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the engagement drop and the proposed fix, then outline a structured A/B test design covering hypothesis, metrics, randomization, sample size, and analysis. Emphasize how you'd ensure validity and interpret results to inform a decision.
Pro tip: Proactively discuss guardrail metrics and potential novelty effects, showing you understand the broader business impact and long-term user behavior, which is critical at Amazon.
Clearly state the hypothesis linking the fix to improved engagement, and define primary, secondary, and guardrail metrics (e.g., daily active users, session duration, revenue).
Choose the randomization unit (e.g., user ID), determine control and treatment groups, and ensure proper randomization and blinding to avoid bias.
Calculate required sample size using power analysis (alpha, beta, MDE) and decide experiment duration to capture enough data and account for novelty effects.
Launch the experiment, monitor for technical issues, sample ratio mismatch, and early guardrail violations, but avoid peeking at primary metrics prematurely.
Perform statistical tests (e.g., t-test, sequential testing) to compare groups, check practical significance, and recommend rollout or iteration based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Picked DAU and conversion rate as primary since those were the ones that dropped.
Start by clarifying the experiment's goal and the specific change being tested, then define a primary metric that directly measures success, secondary metrics that provide additional context, and guardrail metrics to ensure no harm. Explain the rationale for each choice, linking them to the business objective and potential trade-offs.
Pro tip: Tie metrics to Amazon's leadership principles, such as Customer Obsession and Ownership, by emphasizing long-term customer impact and avoiding short-term gains that harm the customer experience.
Ask clarifying questions to understand the hypothesis, the change being tested, and the target user segment. This ensures metrics align with the intended outcome.
Select a single metric that directly measures the experiment's success and is sensitive to the change. It should be tied to the business objective and ideally a leading indicator of long-term value.
Identify additional metrics that provide insight into user behavior and potential side effects. These help explain the primary metric and detect unintended consequences.
Select metrics that ensure the experiment does not degrade critical aspects of the user experience or business health. These act as safety checks and often include latency, error rates, or customer satisfaction.
Explain why each metric was chosen, how they relate to each other, and any potential trade-offs. Discuss how you would handle conflicting results between metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.