Leveraging Machine Learning in Modern Revenue Management

近十年来，收入管理在获取更丰富的个人数据和实施复杂算法方面取得了巨大的增长。例如，基于社交网络的个性化推荐和推荐程序已经成为在线零售商的常见做法。然而，黑箱机器学习算法在帮助公司进行规范性决策分析、为管理人员生成可解释的报告以及说服客户和监管机构没有制定歧视性政策方面存在不足。在本提案中，我们计划利用随机森林、梯度增强和高斯过程等尖端机器学习算法的出色实际性能，并提供可解释的建模框架，以弥合机器学习和收入管理的最佳部分。首先，我们提出了一种新的客户选择二叉决策树模型，并将树的集合与随机森林联系起来。该模型可以让零售商解码各种类型顾客的购买模式，如搜索和替代效应，并准确预测顾客在面对新设计产品时的行为。其次，我们提出用高斯过程对顾客之间的社会互动进行建模。高斯过程通常用于机器学习和贝叶斯优化来调整超参数。它们允许合并复杂而微妙的社会关系以及异质的客户类型。作为一种易于处理的监督学习算法，gp -回归框架可以更深入地理解消费者的社会活动如何诱导购买，并最终提高企业营销策略和收入的有效性。第三，我们提出将梯度增强回归树应用于客户对个性化产品推荐的反应建模和预测。梯度增强在算法交易和生存分析中被证明是非常成功的。在个性化的产品推荐中，回归树将提供简单而强大的客户和产品特性交互视图，从而转化为高性能的推荐策略。通过提出的研究，我们希望利用机器学习算法，这些算法最初是为“预测性”任务设计的，并将它们转化为为公司和监管机构提供“规范性”政策建议的强大工作机器。从公司的角度来看，我们希望在我们的框架下，准确预测的百分比（评估机器学习算法的常见性能指标）可以转化为收入和利润的显着增加。从监管机构的角度来看，我们希望拟议的研究将在复杂的算法与公平透明的商业实践之间架起一座桥梁。

In the recent decade, revenue management has seen tremendous growth in the acquisition of richer personal data and the implementation of sophisticated algorithms. For example, personalized recommendations and referral programs based on social networks have become common practices of online retailers. However, black-box machine learning algorithms fall short in helping firms conducting prescriptive decision analytics, generating interpretable reports for managers, and convincing customers and regulators that no discriminatory policies are devised. In this proposal, we plan to leverage the outstanding practical performance of cutting-edge machine learning algorithms such as random forests, gradient boosting and gaussian processes and provide interpretable modeling frameworks to bridge the best parts of machine learning and revenue management. First, we propose a novel model of customer choices as binary decision trees, and connect the aggregation of trees to random forests. The model may allow retailers to decode the purchasing patterns of various types of customers such as searching and substitution effects, and accurately predict customer behavior when facing newly designed products. Second, we propose to model social interaction between customers by Gaussian processes. Gaussian processes are commonly used in machine learning and Bayesian optimization to tune hyper-parameters. They allow to incorporate complex and nuanced social ties as well as heterogeneous customer types. As a tractable supervised learning algorithm, the GP-regression framework may lead to deeper understanding of how customers' social activities induce purchasing and eventually increase the efficacy of firms' marketing strategies and revenues. Third, we propose to apply gradient boosting regression trees to the modeling and prediction of customers' reactions to personalized product recommendations. Gradient boosting has been shown to be extremely successful in algorithmic trading and survival analysis. In personalized product recommendations, the regression trees will provide a simple and powerful view of the interactions of customer and product features and thus be translated to high-performance recommendation policies. By the proposed study, we hope to leverage machine learning algorithms, which are originally designed for "predictive" tasks, and transform them to powerful workhorses for "prescriptive" policy recommendations for firms and regulators. From the firms' point of view, we hope that under our framework, the percentage of accurate predictions, a common performance indicator in evaluating machine learning algorithms, can be translated to the significant increase in revenues and profits. From the regulators' point of view, we hope that the proposed study will provide a bridge between sophisticated algorithms and fair and transparent business practices.