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

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.

The Next ‘Deep’ Thing in X to Z Marketing: An Artificial Intelligence-Driven Approach

“…In "Multiple Treatment Modeling for Target Marketing Campaigns: A Large-Scale Benchmark Study", Gubela et al (2024) focus on causal ML/AI models for campaign targeting. More specifically, the authors introduce multiple treatment revenue uplift modelling to improve decision-making in target marketing campaigns in an attempt to answer the research question of whether multiple treatment models for continuous outcomes realise more campaign return on marketing than multiple treatment models for binary outcomes and single treatment models.…”