Uplift modeling and its implications for B2B customer churn prediction: A segmentation-based modeling approach

Arno de Caigny (), Kristof Coussement (), Wouter Verbeke, Khaoula Idbenjra and Minh Phan
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Arno de Caigny: LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique
Kristof Coussement: LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique
Wouter Verbeke: KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven
Minh Phan: LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique

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Abstract: Business-to-business (B2B) customer retention relies heavily on analytics and predictive modeling to support decision making. Given this, we introduce uplift modeling as a relevant prescriptive analytics tool. In particular, the uplift logit leaf model offers a segmentation-based algorithm that combines predictive performance with interpretability. Applied to a real-world data set of 6432 customers of a European software provider, the uplift logit leaf model achieves superior performance relative to three other popular uplift models in our study. The accessibility of output gained from the uplift logit leaf model also is showcased with a case study, which reveals relevant managerial insights. This new tool thus delivers novel insights in the form of customized, global, and segment-level visualizations that are especially pertinent to industrial marketing settings. Overall, the findings affirm the viability of uplift modeling for improving decisions related to B2B customer retention management.

Keywords: Customer retention; Churn; modeling; Segmentation-based modeling; Interpretability; Visualization (search for similar items in EconPapers)
Date: 2021-11
Note: View the original document on HAL open archive server: https://hal.science/hal-03599615
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Citations: View citations in EconPapers (6)

Published in Industrial Marketing Management, 2021, 99, pp.28-39. ⟨10.1016/j.indmarman.2021.10.001⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03599615

DOI: 10.1016/j.indmarman.2021.10.001

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