We seek to provide an interpretable framework for segmenting users in a
population for personalized decision-making. The standard approach is to
perform market segmentation by clustering users according to similarities in
their contextual features, after which a "response model" is fit to each
segment to model how users respond to personalized decisions. However, this
methodology is not ideal for personalization, since two users could in theory
have similar features but different response behaviors. We propose a general
methodology, Market Segmentation Trees (MSTs), for learning interpretable
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 customizable, open-source code base for
training MSTs in Python which employs several strategies for scalability,
including parallel processing and warm starts. We provide a theoretical
analysis of the asymptotic running time of our training method validating its
computational tractability on large datasets. We assess the practical
performance of MSTs on several synthetic and real world datasets, showing our
method reliably finds market segmentations which accurately model response
behavior. Further, when applying MSTs to historical bidding data from a leading
demand-side platform (DSP), we show that MSTs consistently achieve a 5-29%
improvement in bid landscape forecasting accuracy over the DSP's current model.
Our findings indicate that integrating market segmentation with response
modeling consistently leads to improvements in response prediction accuracy,
thereby aiding personalization