Case Study: Marketing Uplift Targeting
Who gets the coupon — from randomized logs to a budgeted targeting policy proven in an A/B
Model interview answer for a coupon-targeting problem: define incremental conversions per euro under a budget with unsubscribe and margin guardrails, mine a past randomized campaign (5 percent treated) with a leakage and balance audit, show numerically why the usual response-model baseline targets sure things and loses money, build T-, X-, DR-learners and a causal forest with cross-fitting, evaluate with Qini/AUUC, decile calibration and placebo tests, turn scores into a budgeted and cost-aware policy, estimate its value from logs with IPS and doubly robust estimators, validate it in a three-arm online test with a ghost-treatment holdout and a worked sample-size calculation, and operate it with batch scoring, effect-drift monitoring and an exploration budget. All numbers come from a fixed-seed synthetic simulation with known ground truth.
Practice questions (5)
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The Coupon Budget Is Cut in Half Mid-Quarter
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Only One Percent of Last Year's Customers Were Randomized
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Conversion Rose in the Test but Revenue Did Not
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Critique This Online Test Plan for a Targeting Policy
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Monitoring Effect Drift and Funding Exploration
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