Start by validating the metric definition and data quality, then decompose the rate into its numerator (reported accidents) and denominator (completed trips) to see which component drives the pattern. Segment the data by geography, time, driver/rider cohorts, and product type to identify where the spike is concentrated, and finally test hypotheses with experiments or causal methods to confirm root causes.
Pro tip: Always check for reporting artifacts first—changes in reporting thresholds, app updates, or incentive programs can create artificial spikes. Also, consider seasonality and external factors (e.g., weather, holidays) that might explain the rise and fall.
Confirm how 'reported accidents' and 'completed trips' are defined, and check for data pipeline issues, reporting changes, or missing data that could distort the trend.
Break down the rate into numerator and denominator trends to see if the spike is due to more accidents, fewer trips, or both. Also, check if the pattern holds across different segments.
Analyze by geography, time of day, driver tenure, rider demographics, product type (e.g., UberX, Pool), and other relevant dimensions to localize the issue.
Form hypotheses about potential causes (e.g., seasonal weather, new driver onboarding, app feature changes, incentives) and test them using historical data, A/B tests, or quasi-experimental methods.
Summarize the likely drivers, quantify their impact, and propose next steps such as further analysis, product changes, or policy adjustments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.