Causal Machine Learning in Marketing
BP2-STS
- Huber, Martin ORCID University of Fribourg
- 2024
Published in:
- International Journal of Business & Management Studies. - Institute for Promoting Research and Policy Development. - 2024, vol. 05, no. 07, p. 1-6
English
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.
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.
- Faculty
- Faculté des sciences économiques et sociales et du management
- Department
- Département d'économie politique
- Language
-
- English
- Classification
- Economics
- License
- License undefined
- Open access status
- green
- Identifiers
-
- DOI 10.56734/ijbms.v5n7a1
- ISSN 2694-1430
- Persistent URL
- https://folia.unifr.ch/unifr/documents/329942
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