Leveraging Machine Learning in Modern Revenue Management

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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.