Predicting Practically? Domain Generalization for Predictive Analytics in Real-world Environments

45 Pages Posted: 22 Apr 2025

See all articles by Hanyu Duan

Hanyu Duan

HKUST Business School

Yi Yang

HKUST Business School

Ahmed Abbasi

University of Notre Dame - Mendoza College of Business - IT, Analytics, and Operations Department

Kar Yan Tam

Hong Kong University of Science and Technology

Date Written: February 14, 2025

Abstract

Predictive machine learning models are widely used in customer relationship management (CRM) to forecast customer behaviors and support decision-making. However, the dynamic nature of customer behaviors often results in significant distribution shifts between training data and serving data, leading to performance degradation in predictive models. Domain generalization, which aims to train models that can generalize to unseen environments without prior knowledge of their distributions, has become a critical area of research. In this work, we propose a novel domain generalization method tailored to handle complex distribution shifts, encompassing both covariate and concept shifts. Our method builds upon the Distributionally Robust Optimization framework, optimizing model performance over a set of hypothetical worst-case distributions rather than relying solely on the training data. Through simulation experiments, we demonstrate the working mechanism of the proposed method. We also conduct experiments on a real-world customer churn dataset, and validate its effectiveness in both temporal and spatial generalization settings. Finally, we discuss the broader implications of our method for advancing Information Systems (IS) design research, particularly in building robust predictive models for dynamic managerial environments.

Keywords: Computational Design, Predictive Analytics, Domain Generalization, Customer Relationship Management, Data Distribution Shift

Suggested Citation

Duan, Hanyu and Yang, Yi and Abbasi, Ahmed and Tam, Kar Yan, Predicting Practically? Domain Generalization for Predictive Analytics in Real-world Environments (February 14, 2025). HKUST Business School Research Paper (forthcoming), Available at SSRN: https://ssrn.com/abstract=5149995 or http://dx.doi.org/10.2139/ssrn.5149995

Hanyu Duan

HKUST Business School ( email )

Clear Water Bay
Kowloon
Hong Kong

Yi Yang (Contact Author)

HKUST Business School ( email )

Clearwater Bay
Kowloon, 999999
Hong Kong

Ahmed Abbasi

University of Notre Dame - Mendoza College of Business - IT, Analytics, and Operations Department

Notre Dame, IN 46556
United States

Kar Yan Tam

Hong Kong University of Science and Technology ( email )

Clear Water Bay, Kowloon
Hong Kong

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