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

Machine Learning;Machine Learning