This is where I spent way too long listing possibilities without organizing them.
Start by clarifying the metric definition and scope (e.g., successful orders = completed orders, geographic market = specific region, time frame = 4 weeks). Then systematically break down potential causes into internal (product, operations, marketing) and external (competition, seasonality, macro) factors, and outline how you would validate each with data.
Pro tip: Demonstrate a hypothesis-driven approach by prioritizing the most likely causes first (e.g., check for data pipeline issues or recent product changes) and quantifying their impact before exploring less probable ones. This shows efficiency and business acumen.
Define what 'successful orders' means (e.g., completed, delivered, not canceled) and confirm the geographic market and time period. Ensure you understand any recent changes in tracking or definitions.
Break down the decline by dimensions such as user cohort (new vs. existing), device, order type, restaurant partner, and time (daily/weekly). This helps localize the issue.
List potential causes across internal factors (e.g., app bugs, pricing changes, delivery delays, marketing campaigns) and external factors (e.g., competitor promotions, weather, holidays, economic shifts).
For each hypothesis, identify data sources and analyses (e.g., funnel analysis, A/B tests, correlation with external events) to confirm or rule out causes. Prioritize based on likelihood and impact.
Summarize findings, quantify the impact of each cause, and propose next steps for deeper investigation or immediate action. Highlight any data limitations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metric definition and the scope of the drop (e.g., which metric, time period, and segment). Then systematically slice the data across dimensions like time, geography, user segments, and platform to isolate the source, and finally validate with statistical tests and cross-reference with external factors.
Pro tip: Always start with a data quality check—many apparent drops are due to instrumentation issues, logging errors, or pipeline failures. Mentioning this upfront shows you're pragmatic and have real-world experience.
Confirm the exact metric definition, the magnitude and timing of the drop, and whether it's a sudden or gradual change. This ensures you're solving the right problem.
Verify that the drop is real by checking for logging errors, pipeline failures, or changes in data collection. Rule out false positives before diving deeper.
Break down the metric by time (hourly, daily, weekly) and key dimensions such as geography, platform (iOS/Android/web), user cohorts, and acquisition channels to localize the drop.
Examine upstream and downstream metrics in the user journey (e.g., sessions, conversion rates, order completion) to identify where the drop originates and its impact.
Check for concurrent events like app releases, marketing campaigns, competitor actions, or seasonality. Use statistical tests to confirm significance and avoid false conclusions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining each problem type in terms of its expected impact on key metrics and user behavior. Then propose a systematic diagnostic process that isolates the root cause by segmenting data across time, geography, user cohorts, and the supply-demand funnel. Emphasize the importance of validating data quality first before drawing conclusions.
Pro tip: Always check data instrumentation first—many apparent demand or supply issues are actually logging bugs. Use a 'trust but verify' mindset: cross-reference multiple data sources and run a quick sanity check on raw event counts.
Check for instrumentation bugs by verifying event logging, data pipelines, and metric definitions. Compare against source-of-truth systems and look for sudden drops or spikes in raw event counts.
Examine user-facing metrics like app sessions, search queries, order attempts, and conversion rates. If these drop while supply remains stable, it's likely a demand-side issue.
Look at dasher availability, acceptance rates, and delivery times. If demand is stable but supply metrics degrade, it's a supply-side problem.
Check for issues in order fulfillment, such as increased cancellations, delayed deliveries, or support tickets. Operational issues often manifest as bottlenecks in specific regions or times.
Combine insights from steps 1-4 to pinpoint the root cause. Use segmentation (e.g., by market, time, user type) to confirm the problem type and rule out alternatives.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Proposed a pricing fee experiment and a merchant availability intervention.
Start by framing the problem with a clear hypothesis about the root cause and the metric you aim to improve. Then describe two experiments: one to validate the root cause (e.g., via a diagnostic A/B test or holdback) and one to test an improvement, specifying primary metric, guardrails, and how you'd interpret ambiguous results. Emphasize statistical rigor, practical significance, and decision-making under uncertainty.
Pro tip: Show you think beyond statistical significance: discuss effect size, confidence intervals, and business impact. Also, mention how you'd pre-register the analysis plan to avoid p-hacking and ensure trustworthy results.
Clearly state the metric to improve, the suspected root cause, and a testable hypothesis. Explain why this root cause is plausible based on data or domain knowledge.
Propose an experiment that isolates the root cause, such as an A/B test where you manipulate the suspected factor. Specify primary metric, guardrails, sample size, and duration.
Outline a second experiment that implements a potential fix or improvement based on the validated root cause. Define success metrics, guardrails, and how you'll measure impact.
Describe how you'd analyze results, including checking for statistical significance, practical significance, and guardrail metrics. Explain how you'd handle ambiguous outcomes (e.g., inconclusive, mixed, or surprising results) with follow-up tests or deeper dives.
Summarize how you'd use the results to make a recommendation (ship, iterate, or abandon) and what next steps you'd take, emphasizing continuous learning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.