Multiple Treatment Modeling for Target Marketing Campaigns: A Large-Scale Benchmark Study

Robin M. Gubela (), Stefan Lessmann () and Björn Stöcker ()
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Robin M. Gubela: Humboldt-Universität zu Berlin
Stefan Lessmann: Humboldt-Universität zu Berlin
Björn Stöcker: BAUR Versand

Information Systems Frontiers, 2024, vol. 26, issue 3, No 3, 875-898

Abstract: Abstract Machine learning and artificial intelligence (ML/AI) promise higher degrees of personalization and enhanced efficiency in marketing communication. The paper focuses on causal ML/AI models for campaign targeting. Such models estimate the change in customer behavior due to a marketing action known as the individual treatment effect (ITE) or uplift. ITE estimates capture the value of a marketing action when applied to a specific customer and facilitate effective and efficient targeting. We consolidate uplift models for multiple treatments and continuous outcomes and perform a benchmarking study to demonstrate their potential to target promotional monetary campaigns. In this use case, the new models facilitate selecting the optimal discount amount to offer to a customer. Large-scale analysis based on eight marketing data sets from leading B2C retailers confirms the significant gains in the campaign return on marketing when using the new models compared to relevant model benchmarks and conventional marketing practices.

Keywords: Artificial Intelligence; Business Analytics; Personalization; Uplift Modeling; Machine Learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10796-022-10283-4

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