Two questions in one, which I didn't fully appreciate until I was halfway through talking about surge multipliers and realized I hadn't even touched the experimentation side.
Start by framing the problem around Lyft's business goals—balancing rider affordability, driver earnings, and platform profitability. Then outline a dynamic pricing system that uses real-time data and machine learning to adjust prices, and describe a rigorous A/B testing framework to measure its impact. Emphasize cross-functional collaboration and iterative learning.
Pro tip: Acknowledge the two-sided marketplace: dynamic pricing affects both riders and drivers, so your A/B test must measure impact on both sides and account for network effects. Also, mention guardrail metrics to prevent long-term brand damage.
Clarify the goals of dynamic pricing (e.g., increase revenue, improve match rate, reduce wait times) and define primary and guardrail metrics for both riders and drivers.
Outline a system that ingests real-time demand and supply data, uses predictive models to forecast, and applies pricing algorithms (e.g., surge multipliers) with constraints to avoid extreme prices.
Determine randomization unit (e.g., city, rider, or trip), control and treatment groups, sample size, and duration. Ensure test groups are comparable and account for network effects.
Launch the test, monitor key metrics in real-time, and watch for unintended consequences. Use guardrail metrics to pause or adjust if needed.
Analyze results for statistical significance and practical impact. Decide whether to roll out, refine, or abandon the pricing strategy, and document learnings for future tests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.