Marketing wants to send a discount only to customers it will actually persuade. How would you build and validate that targeting model?

What the interviewer wants to hear

• The goal is uplift (treatment effect), not purchase probability
• Needs randomised data with treated and control customers
• Use uplift methods such as two-model, T-learner or causal forests
• Evaluate with uplift or Qini curves on held-out randomised data
• Confirm with a new test comparing targeting against random

Model answer

The trap is to target people with a high purchase probability, because many of them would buy anyway and the discount just loses margin. What we want is uplift: how much the discount raises the chance of buying for each customer. To learn that we need randomised data, so I would run an experiment where a random subset gets the discount. Then I can fit uplift models, for example a T-learner with one model for treated and one for control, or a causal forest, and estimate each customer effect. Evaluation is tricky because we never see both outcomes for one person, so I use uplift or Qini curves on held-out randomised data, checking that the top-scored groups show the largest treated-minus-control difference. Finally I would run a confirmation test comparing model targeting with random targeting, measured on incremental profit after discount costs.

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