My first instinct was to jump straight into supply-side explanations like driver ETAs getting worse, but I caught myself and slowed down to segment the 5% first.
Start by clarifying the metric definition and validating the data to rule out measurement issues. Then segment the 5% increase by dimensions like time, geography, rider cohort, and product surface to localize the problem. Finally, form hypotheses about root causes (e.g., pricing, ETA, driver behavior, app changes) and test them with data and experiments.
Pro tip: Always check for seasonality and external events (e.g., holidays, weather, competitor promotions) before assuming an internal product change caused the shift. Also, consider that cancellations may be driven by driver-side factors, not just rider-side.
Clarify what 'rider cancellations' means (e.g., rider cancels after requesting, before driver accepts, etc.) and ensure the 5% increase is statistically significant and not due to data pipeline issues or logging changes.
Break down the increase by time (hour, day, week), geography (city, region), rider demographics (new vs. existing, platform), and product type (Lyft, Lux, Shared). Look for concentrated spikes that point to a specific cause.
Brainstorm potential causes: increased ETAs, driver cancellations leading to rider cancellations, pricing changes, app bugs, competitor promotions, or external events. Prioritize based on likelihood and impact.
Use A/B tests, regression analysis, or correlation studies to validate each hypothesis. For example, compare cancellation rates before and after a pricing change, or correlate with driver supply metrics.
Based on findings, propose actionable fixes (e.g., improve ETA accuracy, adjust pricing, fix bugs) and set up monitoring to track the metric post-intervention.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.