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A Data-Driven Approach to Personalized Bundle Pricing and Recommendation

机译：数据驱动的个性化捆绑定价和推荐方法

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Problem definition: The growing trend in online shopping has sparked the development of increasingly more sophisticated product recommendation systems. We construct a model that recommends a personalized discounted product bundle to an online shopper that considers the trade-off between profit maximization and inventory management, while selecting products that are relevant to the consumer's preferences. Academic! practical relevance: We provide analytical performance guarantees that illustrate the complexity of the underlying problem, which combines assortment optimization with pricing. We implement our algorithms in two separate case studies on actual data from a large U.S. e-tailer and a premier global airline. Methodology: We focus on simultaneously balancing personaliza tion through individualized functions of consumer propensity-to-buy, inventory management for long-run profitability, and tractability for practical business implementation. We develop two classes of approximation algorithms, multiplicative and additive, to produce a real-time output for use in an online setting. Results: Our computational results demonstrate significant lifts in expected revenues over current industry pricing strategies on the order of 2%-7% depending on the setting. We find that on average our best algorithm obtains 92% of the expected revenue of a full-knowledge clairvoyant strategy across all inventory settings, and in the best cases this improves to 98%. Managerial implications: We compare the algorithms and find that the multiplicative approach is relatively easier to implement and on average empirically obtains expected revenues within 1%-6% of the additive methods when both are compared with a full-knowledge strategy. Furthermore, we find that the greatest expected gains in revenue come from high-end consumers with lower price sensitivities, and that predicted improvements in sales volume depend on product category and are a result of providing relevant recommendations.
机译：问题定义：在线购物的增长趋势引发了越来越复杂的产品推荐系统的发展。我们构建了一个模型，向在线购物者推荐个性化的折扣产品捆绑，该模型考虑了利润最大化与库存管理之间的权衡，同时选择了与消费者喜好相关的产品。学术的！实际意义：我们提供的分析性能保证能够说明潜在问题的复杂性，并将分类优化与定价相结合。我们在两个单独的案例研究中对来自一家大型美国电子零售商和一家主要全球航空公司的实际数据实施算法。方法：我们专注于通过个性化的消费者购买倾向功能，库存管理以实现长期获利能力以及易处理性以实现实际业务来同时平衡个性化。我们开发了两类近似算法：乘法和加法，以生成用于在线设置的实时输出。结果：我们的计算结果表明，与当前的行业定价策略相比，预期收入将显着提高，具体取决于设置，其范围为2％-7％。我们发现，在所有库存设置中，平均而言，我们的最佳算法平均可获得全知识千篇一律策略预期收入的92％，在最佳情况下，该算法可提高至98％。对管理的影响：我们对算法进行比较，发现乘法方法相对容易实施，并且在将两者与全知识策略进行比较时，根据经验平均可在加法的1％-6％之内获得预期收入。此外，我们发现，最大的预期收入增长来自对价格敏感度较低的高端消费者，并且预测的销量增长取决于产品类别，并且是提供相关建议的结果。

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