Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2018 062

Conversion uplift in e-commerce: A systematic benchmark of modeling strategies

Robin Gubela
Artem Bequé
Fabian Gebert
Stefan Lessmann

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.

e-commerce analytics, machine learning, uplift modeling, real-time targeting

JEL Classification: