Frame the problem as a constrained optimization: maximize incremental profit by targeting users with high uplift and low organic conversion probability, using causal ML to estimate heterogeneous treatment effects. Propose a two-stage system: first, offline uplift modeling to personalize offers, then online experimentation with holdouts to measure true lift and guard long-term margins.
Pro tip: Emphasize that the goal is not to maximize conversions but to maximize incremental profit per dollar spent, and that you'd use a long-term holdout to monitor cannibalization and retention effects beyond the campaign window.
Clarify the business goal (e.g., incremental orders, profit) and constraints (budget, margin floor, user experience). Establish a north-star metric that balances short-term lift and long-term value.
Use historical data to build uplift models (e.g., meta-learners, causal forests) that predict the difference in outcome between treated and untreated for each user. Identify 'persuadables' who are likely to convert only with a promo.
Formulate as a knapsack or linear programming problem: allocate offers to users where expected incremental profit (uplift * margin - cost) is positive, subject to budget. Personalize offer type and timing based on user features.
Deploy a randomized controlled trial (A/B test) with a holdout group to measure true incremental lift. Use techniques like switchback or cluster randomization if interference is a concern. Monitor guardrail metrics (e.g., long-term retention, margin).
Continuously retrain models with new data, adjust targeting as user behavior shifts, and maintain a long-term holdout to detect degradation in organic behavior or margin erosion. Use multi-armed bandits for adaptive allocation if appropriate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.