Market Segmentation Trees

Aouad, A, Elmachtoub, A N, Ferreira, K J and McNellis, R (2023) Market Segmentation Trees. Manufacturing and Service Operations Management, 25 (2). pp. 648-667. ISSN 1523-4614

Abstract

Problem Definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision-making.
Methodology / Results: We propose a general methodology, Market Segmentation Trees (MSTs), for learning market segmentations explicitly driven by identifying differences in user response patterns. To demonstrate the versatility of our methodology, we design two new, specialized MST algorithms: (i) Choice Model Trees (CMTs), which can be used to predict a user’s choice amongst multiple options and (ii) Isotonic Regression Trees (IRTs), which can be used to solve the bid landscape forecasting problem. We provide a theoretical analysis of the asymptotic running times of our algorithmic methods, which validates their computational tractability on large datasets. We also provide a customizable, open-source code base for training MSTs in Python which employs several strategies for scalability, including parallel processing and warm starts. Finally, we assess the practical performance of MSTs on several synthetic and real world datasets, showing that our method reliably finds market segmentations which accurately model response behavior.
Managerial Implications: The standard approach to conduct market segmentation for personalized decision-making is to first perform market segmentation by clustering users according to similarities in their contextual features, and then fit a “response model” to each segment in order to model how users respond to decisions. However, this approach may not be ideal if the contextual features prominent in distinguishing clusters are not key drivers of response behavior. Our approach addresses this issue by integrating market segmentation and response modeling, which consistently leads to improvements in response prediction accuracy, thereby aiding personalization. We find that such an integrated approach can be computationally tractable and effective even on large-scale datasets. Moreover, MSTs are interpretable since the market segments can easily be described by a decision tree and often require only a fraction of the number of market segments generated by traditional approaches.