By intervention I mean any action. In a marketing context, we can think of personalized coupons, advertising, customer care, etc.

- we cannot properly evaluate the performance of the model in terms of actual business impact without post-intervention data. The reason is that without historical data on a similar intervention, we cannot evaluate how the targeted intervention will perform. For instance, while we can predict who is most likely to purchase dairy products from a large existing transactional database, we cannot tell whether they would redeem a coupon that is targeted to them unless we have some data post a similar coupon campaign.
- we cannot build a predictive model that is optimized with the intervention goal unless we have post-intervention data. For example, if coupon redemption is the intervention performance metric, we cannot build a predictive model optimizing coupon redemption unless we have data on coupon redemption.
A predictive model is trained on past data. To evaluate the effect of an intervention, we must have some post-intervention data in order to build a model that aims at optimizing the intervention goal, and also for being able to evaluate model performance in light of that goal. A pilot study/period is therefore a good way to start: either deploy it randomly or to the sample that is indicated by a predictive model to be optimal in some way (it is best to do both: deploy to a sample that has both a random choice and a model-indicated choice). Once you have the post-intervention data on the intervention results, you can build a predictive model to optimize results on a future, larger-scale intervention.
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