Huber, Martin 2024 This study reviews three primary purposes of causal machine learning (CML) in marketing, merging impact evaluation of marketing interventions with machine learning algorithms for learning statistical patterns from data. Firstly, CML enables more credible impact evaluation by considering important control variables that simultaneously influence the intervention and business outcomes (such as sales) in a data-driven manner. Secondly, it facilitates the data-driven detection of customer segments for which a marketing intervention is particularly effective or ineffective, a process known as effect moderation or heterogeneity analysis. Thirdly, closely related to the second point, it allows for optimal customer segmentation into groups that should and should not be targeted by the intervention to maximize overall effectiveness. The discussion is grounded in recent empirical applications, all of which aim to enhance decision support in marketing by leveraging data-driven evaluation and optimization of interventions across different customer groups. application/pdf https://sonar.ch/global/documents/329942 https://sonar.ch/documents/329942/files/causalmachinelearninginmarketing.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.56734/ijbms.v5n7a1 info:eu-repo/semantics/altIdentifier/issn/2694-1430 info:eu-repo/semantics/openAccess License undefined International Journal of Business & Management Studies. - Institute for Promoting Research and Policy Development. - 2024, vol. 05, no. 07, p. 1-6 Causal Machine Learning Marketing Algorithms Impact Evaluation info:eu-repo/classification/udc/33 Causal Machine Learning in Marketing http://purl.org/coar/resource_type/c_6501