Start by validating the 10% drop and defining the metric precisely, then systematically slice the data across dimensions like time, geography, and delivery characteristics to isolate where the drop is concentrated. Form hypotheses about potential causes (e.g., supply, demand, logistics, external factors) and prioritize them based on data patterns. Finally, use statistical tests to confirm or rule out each hypothesis, ensuring you account for seasonality, sample size, and multiple comparisons.
Pro tip: Always quantify the impact of the drop in business terms (e.g., lost revenue, affected customers) and consider whether the drop is statistically significant and practically meaningful before diving deep. Also, check for data pipeline issues or metric definition changes first—sometimes the 'drop' is an artifact.
Confirm the 10% drop is real by checking data quality, metric definitions, and seasonality. Ensure you're comparing apples-to-apples (e.g., same time period, same territory definition).
Break down the metric by dimensions such as time (daily/weekly trends), geography (sub-regions, zip codes), delivery type (restaurant vs. grocery), customer segments (new vs. returning), and operational factors (dasher supply, delivery times).
Based on the slices, generate hypotheses for the root cause: e.g., increased competition, dasher shortage, restaurant closures, pricing changes, app issues, or external events (weather, holidays). Prioritize hypotheses by likelihood and potential impact.
Use appropriate tests to validate each hypothesis: e.g., t-tests for comparing means, chi-square for categorical shifts, regression to control for confounders, and time-series analysis for trends. Consider A/B tests if an intervention was made.
Combine findings to identify the most probable root cause(s), quantify their impact, and propose actionable next steps or further experiments to confirm and address the issue.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.