I started with the obvious cuts: city, time of day, ride type.
Start by decomposing wait time into its core supply and demand drivers, then systematically rule out hypotheses using a structured funnel from macro to micro factors. Frame the analysis around measurable KPIs and use dimensional slicing (geography, time-of-day, driver cohort) to isolate where the degradation is concentrated before jumping to conclusions.
Pro tip: Demonstrate that you know correlation isn't causation — explicitly state you'd check whether the 20% increase is uniform across all markets or concentrated in a few, since a single city like San Francisco skewing the average is a very different problem than a platform-wide supply shortage.
Confirm the definition of 'wait time' (ETA at request vs. actual pickup time) and verify the 20% figure isn't a data artifact, seasonality effect, or sampling bias. Check if the metric moved consistently across all percentiles (median, p90, p99) or only in the tail.
Break wait time into its fundamental equation: Wait Time = f(Driver Supply, Rider Demand, Matching Efficiency). Identify whether the issue stems from fewer available drivers, a spike in ride requests, or degraded dispatch/matching algorithms.
Segment the data across geography (city, zone), time (hour-of-day, day-of-week, week-over-week trend), ride type (shared vs. standard), and driver cohort (new vs. tenured drivers) to pinpoint where the degradation is concentrated and generate targeted hypotheses.
Cross-reference the timeline with external events (competitor promotions, local events, weather, regulatory changes) and internal changes (pricing updates, driver incentive policy changes, app releases) to identify potential causal triggers.
Rank hypotheses by their estimated contribution to the 20% increase using back-of-envelope calculations, then propose targeted interventions (e.g., surge pricing adjustments, driver incentives, rebalancing) with measurable success criteria to validate the root cause.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem as a causal inference question: estimate the impact of dynamic pricing on key metrics using a controlled experiment or quasi-experimental design. Then, quantify trade-offs by translating each metric into a common currency (e.g., dollars or utility) and computing net effects, while acknowledging heterogeneity across rider and driver segments.
Pro tip: Emphasize the importance of measuring long-term effects and potential feedback loops (e.g., driver supply response, rider churn) rather than just short-term gains; propose a holdout group that persists beyond the initial test to capture these dynamics.
Identify the key metrics for each dimension: wait times (e.g., ETA, match rate), revenue (e.g., gross bookings, take rate), rider experience (e.g., cancellation rate, NPS), and driver earnings (e.g., hourly earnings, utilization). Formulate hypotheses about how dynamic pricing affects each.
Propose an A/B test with random assignment of riders/drivers to static vs. dynamic pricing, ensuring sufficient power. If randomization is infeasible, consider difference-in-differences or synthetic control using historical data.
Use appropriate statistical methods (e.g., regression adjustment, propensity score matching) to estimate the causal impact on each metric, controlling for confounders like time, location, and demand shocks.
Convert effects into a common unit (e.g., dollars) using elasticities or willingness-to-pay estimates. Compute net benefit by summing weighted impacts, and perform sensitivity analysis on key assumptions.
Analyze segment-level differences (e.g., by rider loyalty, driver tenure) and model long-term consequences such as driver retention and rider lifetime value to avoid myopic conclusions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Geo-cluster randomization was the right answer here and I got there eventually, but I wasted time talking about standard A/B testing first.
Start by defining clear success metrics (e.g., wait times, rider cancellations, driver utilization) and then design a randomized experiment that accounts for interference between riders and drivers. Use a switchback or cluster randomization design to mitigate interference, and analyze results with appropriate statistical methods to ensure validity.
Pro tip: In two-sided marketplaces, interference can bias results; consider using a switchback design where you alternate treatment across time periods for the entire market, or use cluster randomization by geography to isolate effects. Also, pre-register your analysis plan to avoid p-hacking.
Identify primary metric (wait time) and guardrail metrics (e.g., rider cancellations, driver earnings, match rate). Formulate null and alternative hypotheses.
Select a design that minimizes interference: switchback (time-based randomization) or cluster randomization (e.g., by city or zone). Discuss trade-offs.
Randomly assign treatment (dynamic pricing) vs control (current pricing) at the chosen unit. Ensure proper implementation and monitor for compliance.
Use appropriate methods (e.g., difference-in-differences, mixed models) to estimate treatment effect while accounting for interference. Check for spillovers.
Run power analysis, check for novelty effects, and validate results with holdout groups. Consider long-term effects and potential feedback loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.