I jumped straight into metrics before thinking about what 'better' even means, which was probably the wrong move.
Start by clarifying the business goal and defining success metrics that align with rider and driver experience, then outline a rigorous A/B test design with proper randomization, sample size, and guardrail metrics. Emphasize the importance of measuring both short-term and long-term impacts, and consider potential network effects and marketplace dynamics.
Pro tip: In marketplace experiments, be wary of interference between test and control groups due to shared supply/demand; consider switchback or cluster randomization to isolate the treatment effect.
Clarify what 'better' means: faster matches, higher completion rates, improved rider/driver satisfaction, or increased revenue. Formulate a clear hypothesis for the new algorithm.
Choose primary success metrics (e.g., match rate, ETA, cancellation rate) and guardrail metrics (e.g., driver utilization, rider wait time) to ensure no negative side effects.
Decide on randomization unit (rider, driver, or region), sample size, duration, and whether to use A/B testing, switchback, or cluster randomization to handle interference.
Launch the experiment, monitor for technical issues and novelty effects, and ensure data quality. Avoid peeking at results prematurely.
Perform statistical analysis to determine significance, check for heterogeneous treatment effects, and decide whether to roll out, iterate, or abandon the new algorithm.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.