Conversion Uplift in E-Commerce: A Systematic Benchmark of Modeling Strategies

Robin Gubela, Artem Bequé, Stefan Lessmann and Fabian Gebert
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Robin Gubela: School of Business and Economics, Humboldt-University of Berlin, Unter den Linden 6, 10099 Berlin, Germany
Artem Bequé: School of Business and Economics, Humboldt-University of Berlin, Unter den Linden 6, 10099 Berlin, Germany
Stefan Lessmann: School of Business and Economics, Humboldt-University of Berlin, Unter den Linden 6, 10099 Berlin, Germany
Fabian Gebert: Data Science Department, Akanoo GmbH, Mittelweg 121, 20148 Hamburg, Germany

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 03, 747-791

Abstract: Uplift modeling combines machine learning and experimental strategies to estimate the differential effect of a treatment on individuals’ behavior. The paper considers uplift models in the scope of marketing campaign targeting. Literature on uplift modeling strategies is fragmented across academic disciplines and lacks an overarching empirical comparison. Using data from online retailers, we fill this gap and contribute to literature through consolidating prior work on uplift modeling and systematically comparing the predictive performance and utility of available uplift modeling strategies. Our empirical study includes three experiments in which we examine the interaction between an uplift modeling strategy and the underlying machine learning algorithm to implement the strategy, quantify model performance in terms of business value and demonstrate the advantages of uplift models over response models, which are widely used in marketing. The results facilitate making specific recommendations how to deploy uplift models in e-commerce applications.

Keywords: E-commerce analytics; machine learning; uplift modeling; real-time targeting (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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DOI: 10.1142/S0219622019500172

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