Optimization Levers for Promotions Personalization Under Limited Budget
Optimization Levers for Promotions Personalization Under Limited Budget
复制标题
有限预算下促销个性化的优化杠杆
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Guy Tsype
中科院分区：
文献类型：
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作者：
Dmitri Goldenberg;Javier Albert;Guy Tsype
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。正如预期的那样，收入也受到促销活动的影响，专门的预算通常会限制货币损失。为了在预算约束内有效地分配促销，营销人员可以使用基于因果机器学习的个性化沿着约束优化工具。在本文中，我们研究了四个决策杠杆的促销活动，允许最佳和个性化的优惠分配预算限制。我们展示了在现实生活中的促销活动的优化问题，并制定他们的背包问题的变化，使我们能够引入有效的应用解决方案。我们证明，这样的解决方案在Booking.com-一个世界领先的在线旅游平台的促销活动产生了重大影响。
Modern e-commerce platforms make use of promotional offers, such as discounts and rewards, to encourage customers to complete purchases. As expected, revenue is also affected by promotions, and a dedicated budget usually limits monetary losses. In order to allocate promotions efficiently within budget constraints, a marketer can use causal machine learning based personalization along with constrained optimization tools. In this paper we study four decision levers of promotional campaigns, allowing optimal and personalized offers allocation within budget constraints. We demonstrate the optimization problems in real-life promotional campaigns and formulate them as variations of the Knapsack problem, allowing us to introduce efficient applied solutions. We demonstrate that such solutions have a significant impact on promotional campaigns at Booking.com - a world leading online travel platform.