Prediction and profitability in market segmentation typing tools

Marco Vriens (), Nathan Bosch (), Chad Vidden () and Jason Talwar ()
Additional contact information
Marco Vriens: Kwantum Analytics
Nathan Bosch: Kwantum Analytics
Chad Vidden: University of Wisconsin, La Crosse
Jason Talwar: Brown University School of Engineering

Journal of Marketing Analytics, 2022, vol. 10, issue 4, No 6, 360-389

Abstract: Abstract A vital component in strategic segmentation is the typing tool. Little is known about their prediction performance. Even less is known how well they perform at the segment-level, in imbalanced situations, and how well they predict the smallest (minority) segment. We investigate using simulated and real-life data, how well typing tools perform overall and at the specific segment-level and we show the following. One, even when overall prediction accuracy is good, specific segments may be predicted poorly. Two, for valuable (minority) segments with high targeting costs misclassification can have a substantial impact on the profitability of the segmentation strategy. Poor prediction of a minority segment can happen in high and mildly imbalanced segments. Three, prediction of minority segments can vary substantially across different base classifiers and across imbalance correction methods. We find that performance can vary substantially across base classifiers and that support vector machines, overall, perform best. Four, the prediction of a (minority) segment can always be improved by using imbalance correction methods, and overall random under-sampling performs best.

Keywords: Strategic market segmentation; Typing tools; Imbalance correction methods; Classification (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1057/s41270-021-00145-4

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