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

Dmitri Goldenberg, Javier Albert
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引用次数: 1

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.
不花钱的个性化利益分配:在预算受限的设置中利用提升模型
现代电子商务平台利用折扣和奖励等促销优惠来鼓励客户完成购买。虽然提供促销活动对销售有很大的影响,但它也会产生金钱损失。通过使用因果机器学习和优化,我们在Booking.com的团队能够将促销活动分配给客户,同时有效地将支出控制在给定的预算范围内。在这次演讲中,我们将分享个性化促销分配技术,如提升建模和约束优化,这有助于我们预测折扣提供的结果并有效地分配它们。这个解决方案使我们能够开启促销活动,为客户带来更多价值,并发展我们的业务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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