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Promotions and discounts have become key components of modern e-commerceplatforms. For online travel platforms (OTPs), popular promotions include roomupgrades, free meals and transportation services. By offering these promotions,customers can get more value for their money, while both the OTP and its travelpartners may grow their loyal customer base. However, the promotions usuallyincur a cost that, if uncontrolled, can become unsustainable. Consequently, fora promotion to be viable, its associated costs must be balanced by incrementalrevenue within set financial constraints. Personalized treatment assignment canbe used to satisfy such constraints.  This paper introduces a novel uplift modeling technique, relying on theKnapsack Problem formulation, that dynamically optimizes the incrementaltreatment outcome subject to the required Return on Investment (ROI)constraints. The technique leverages Retrospective Estimation, a modelingapproach that relies solely on data from positive outcome examples. The methodalso addresses training data bias, long term effects, and seasonalitychallenges via online-dynamic calibration. This approach was tested via offlineexperiments and online randomized controlled trials at Booking .com - a leadingOTP with millions of customers worldwide, resulting in a significant increasein the target outcome while staying within the required financial constraintsand outperforming other approaches.

Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints