Integration of Statistical Analysis and Machine Learning with Two-Sided Matching to Achieve Win-Win

For many business applications, statistical modeling or machine learning is commonly employed to optimize an objective. While successful in practice, this approach is one-sided, typically from the developer or corporate perspective which may not necessarily be beneficial to the target audience (e.g., customers, employees). In this paper, we propose a mutually beneficial approach using two-sided matching. Consider the following two cases.

Customer-Product Matching in Marketing: Product recommendation engines are often developed to maximize customer purchase or engagement by selecting customers who are most responsive to a product offer or marketing intervention. On the other hand, if customer preference or experience can be quantified, a model can be trained to recommend the right products such that customer preference or value to customer is maximized. How do we integrate the models to drive both value to customer and value to business?

Employee-Project Matching in Project Assignment: If a firm has a group of employees with certain skills (e.g., data scientists) and a large number of projects, how should the employees be assigned to projects? Often it is based on historical experience, skills, and availability, which are important factors to drive success aided by statistical analysis or predictive/prescriptive analytics that benefits the firm. However, employees' perspective is taken as a priority, their interests can be captured to construct a machine learning or rule-based model.

We propose an approach using the deferred acceptance algorithm in conjunction with statistical modeling and machine learning methods to generate the optimized solutions, which achieve "stability" in the sense that no pair of agents (customer/product, employee/project in our cases) would prefer each other to their match recommended by the algorithm.

Two-Sided Matching

statistical modeling and machine learning

Employee-Project Matching

Customer-Product Matching 

Expert

Practice and Applications

Symposium on Data Science and Statistics (SDSS) 2023