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.
