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Personalizing Benefits Allocation Without Spending Money: Utilizing Uplift Modeling in a Budget Constrained Setup

Published: 13 September 2022 Publication History

Abstract

Modern e-commerce platforms make use of promotional offers such as discounts and rewards to encourage customers to complete purchases. While offering the promotions has a great effect on the sales, it also generates a monetary loss. By utilizing causal machine learning and optimization, our team at Booking.com was able to personalize the promotions allocation to customers, while efficiently controlling the spend within a given budget. In this talk we’ll share the personalized promotion assignment techniques, such as uplift modeling and constrained optimization, which helped us to predict the outcomes of discounts offering and allocate them efficiently. This solution allowed us to unlock promotional campaigns to bring more value to the customers and grow our business.

Supplementary Material

MP4 File (recsys.mp4)
Personalizing Benefits Allocation Without Spending Money - Industry Talk Video

References

[1]
Javier Albert and Dmitri Goldenberg. 2021. E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling.
[2]
Susan Athey and Guido W Imbens. 2015. Machine learning methods for estimating heterogeneous causal effects. stat 1050, 5 (2015), 1–26.
[3]
Dmitri Goldenberg, Javier Albert, Lucas Bernardi, and Pablo Estevez. 2020. Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints. In Proceedings of the 14th ACM Conference on Recommender Systems.
[4]
Dmitri Goldenberg, Javier Albert, and Guy Tsype. 2021. Optimization Levers for Promotions Personalization Under Limited Budget. In Workshop of Multi-Objective Recommender Systems (MORS’21), in conjunction with the 15th ACM Conference on Recommender Systems, RecSys, Vol. 21. 2021.
[5]
Dmitri Goldenberg, Kostia Kofman, Javier Albert, Sarai Mizrachi, Adam Horowitz, and Irene Teinemaa. 2021. Personalization in Practice: Methods and Applications. In Proceedings of the 14th International Conference on Web Search and Data Mining.
[6]
Dmitri Goldenberg, Guy Tsype, Igor Spivak, Javier Albert, and Amir Tzur. 2021. Learning to Persist: Exploring the Tradeoff Between Model Optimization and Experience Consistency. In Companion Proceedings of the Web Conference 2021. Association for Computing Machinery, New York, NY, USA, 527–529. https://doi.org/10.1145/3442442.3452051
[7]
Nam Pham Irene Teinemaa, Javier Albert. 2021. UpliftML: A Python Package for Scalable Uplift Modeling. https://github.com/bookingcom/upliftml. Version 0.0.1.
[8]
Irene Teinemaa, Javier Albert, and Dmitri Goldenberg. 2021. Uplift Modeling: from Causal Inference to Personalization. In Companion Proceedings of the Web Conference 2021.

Cited By

View all
  • (2024)DISCO: An End-to-End Bandit Framework for Personalised Discount AllocationMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70381-2_3(33-49)Online publication date: 22-Aug-2024
  • (2023)Two-Sided Instant Incentive Optimization under a Shared Budget in Ride-Hailing Services2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00267(3481-3493)Online publication date: Apr-2023

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      RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems
      September 2022
      743 pages
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      New York, NY, United States

      Publication History

      Published: 13 September 2022

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      Author Tags

      1. Discounts
      2. Personalization
      3. Recommender Systems
      4. Travel

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      View all
      • (2024)DISCO: An End-to-End Bandit Framework for Personalised Discount AllocationMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70381-2_3(33-49)Online publication date: 22-Aug-2024
      • (2023)Two-Sided Instant Incentive Optimization under a Shared Budget in Ride-Hailing Services2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00267(3481-3493)Online publication date: Apr-2023

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