Market Segmentation Trees

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 data sets. We also provide a customizable, open-source code base for training MSTs in Python that uses several strategies for scalability, including parallel processing and warm starts. Finally, we assess the practical performance of MSTs on several synthetic and real-world data sets, showing that our method reliably finds market segmentations that 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 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 data sets. Moreover, MSTs are interpretable because 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. Disclaimer: This work was done prior to Ryan McNellis joining Amazon. Funding: This work was supported by the National Science Foundation [Grants CMMI-1763000 and CMMI-1944428]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1195 .

Research on the Marketing Strategy of Sexy Tea

“…These choices make it keep in a stable condition, and this market segmentation can be deepened by building some models. Market segmentation trees (MSTs) is a general methodology, and it builds interpretable decision trees for joint market segmentation and response model, which can be used for a variety of personalized decision-making applications [17]. This is a more detailed way to accurately analyse the market and target consumers.…”

Constrained optimization of objective functions determined from random forests

“…(2016); Ban & Rudin (2018); Aouad et al. (2019); Cohen et al. (2020); Bertsimas & Kallus (2020); Ban & Keskin (2021); Chen et al.…”

Trading safety stock for service response time in inventory positioning

“…The use of isotonic regression is also critical for computing demand elasticity. For more advanced tree-based and isotonic regression-based demand analytics, we refer the interested readers to the work by Aouad et al (2022), Bertsimas et al (2019), Chen et al (2022), andSimchi-Levi et al (2022).…”

“…For more advanced tree–based and isotonic regression–based demand analytics, we refer the interested readers to the work by Aouad et al. (2022), Bertsimas et al. (2019), Chen et al.…”