The part I fumbled initially was jumping straight into segmentation without anchoring on what 'success' even means.
Start by clarifying the campaign's objective and constraints, then propose a data-driven approach to identify users with the highest incremental impact per dollar. Focus on designing an experiment to measure heterogeneous treatment effects and targeting users who are most responsive to the coupon.
Pro tip: Emphasize that the goal is to maximize incremental rides, not just conversions—many users would ride without a coupon, so targeting should be based on uplift, not propensity. Mention that you'd validate the targeting strategy with a holdout group to measure true incrementality.
Define the campaign's success metric (e.g., incremental commuter rides) and understand budget limitations, coupon value, and target audience.
Use historical data to segment users based on commute patterns, past coupon usage, and responsiveness. Consider features like ride frequency, time of day, and route regularity.
Apply causal inference methods (e.g., uplift modeling, A/B test with holdout) to predict which users would take more rides only if given a coupon.
Prioritize users with the highest predicted incremental rides per dollar spent. Use optimization techniques to allocate coupons within budget.
Run a pilot experiment to measure actual lift and ROI, then refine the targeting model based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.