Uplift Modeling in Direct Marketing

Authors

  • Piotr Rzepakowski
  • Szymon Jaroszewicz

DOI:

https://doi.org/10.26636/jtit.2012.2.1263

Keywords:

decision trees, information theory, marketing tools, uplift modeling

Abstract

Marketing campaigns directed to randomly selected customers often generate huge costs and a weak response. Moreover, such campaigns tend to unnecessarily annoy customers and make them less likely to answer to future communications. Precise targeting of marketing actions can potentially results in a greater return on investment. Usually, response models are used to select good targets. They aim at achieving high prediction accuracy for the probability of purchase based on a sample of customers, to whom a pilot campaign has been sent. However, to separate the impact of the action from other stimuli and spontaneous purchases we should model not the response probabilities themselves, but instead, the change in those probabilities caused by the action. The problem of predicting this change is known as uplift modeling, differential response analysis, or true lift modeling. In this work, tree-based classifiers designed for uplift modeling are applied to real marketing data and compared with traditional response models, and other uplift modeling techniques described in literature. The experiments show that the proposed approaches outperform existing uplift modeling algorithms and demonstrate significant advantages of uplift modeling over traditional, response based targeting.

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Published

2012-06-30

Issue

Section

ARTICLES FROM THIS ISSUE

How to Cite

[1]
P. Rzepakowski and S. Jaroszewicz, “Uplift Modeling in Direct Marketing”, JTIT, vol. 48, no. 2, pp. 43–50, Jun. 2012, doi: 10.26636/jtit.2012.2.1263.