Diagnostic Plan: March Ride-Volume Drop

Context

After normalizing for days-in-month, completed trips in March are down 14% vs February and down 9% vs prior-year March. Build a data-driven diagnostic plan to:

• (a) Confirm the drop is real (not instrumentation/definition drift).
• (b) Decompose it into demand-side vs supply-side drivers.
• (c) Quantify the top two root causes.

Assume access to standard mobility marketplace logs (rider sessions/requests, driver online/acceptance, pricing/surge, cancellations, ETA, trips) and supporting data (weather, events, promotions, app versions).

Requirements

1. Define primary and guardrail metrics (e.g., request rate, conversion to accepted, pickup ETA, cancellation rate by initiator, completion rate, price indices, surge exposure, wait-time distribution, session-to-request funnel).
2. List the exact data points and cuts you will check first: by city/region, hour-of-day, weekday/weekend, rider/driver cohorts (tenure, loyalty), product type (UberX/Pool/Lux), price buckets, promo exposure, surge bins, weather, events/holidays (DST shift, spring break), airport vs non-airport, device/app version, major app releases.
3. Describe how you would separate demand shrinkage from supply constraints (e.g., rides = requests × P(accept) × P(complete); use pickup ETA/cancellation elasticity, driver online minutes, active driver count, queue depth).
4. Specify statistical methods to establish significant drop and attribute causes (e.g., interrupted time series with seasonality/holiday controls, difference-in-differences across unaffected control cities, synthetic control).
5. Outline checks for measurement artifacts (schema changes, event loss, sampling, timezone/DST alignment, deduplication).
6. Propose 1–2 quick experiments or natural experiments to validate a suspected cause (e.g., restore prior promo budget in matched markets; temporarily cap ETA via supply incentives), including success metrics, minimum detectable effect (MDE), and duration.