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Predicting online shopping cart abandonment with machine learning approaches

Title data

Rausch, Theresa Maria ; Derra, Nicholas Daniel ; Wolf, Lukas:
Predicting online shopping cart abandonment with machine learning approaches.
In: International Journal of Market Research. (2020) . - 24 S..
ISSN 2515-2173
DOI: https://doi.org/10.1177/1470785320972526

Abstract in another language

Excessive online shopping cart abandonment rates constitute a major challenge for e-commerce companies and can inhibit their success within their competitive environment. Simultaneously, the emergence of the Internet’s commercial usage results in steadily growing volumes of data about consumers’ online behavior. Thus, data-driven methods are needed to extract valuable knowledge from such big data to automatically identify online shopping cart abandoners. Hence, this contribution analyzes clickstream data of a leading German online retailer comprising 821,048 observations to predict such abandoners by proposing different machine learning approaches. Thereby, we provide methodological insights to gather a comprehensive understanding of the practicability of classification methods in the context of online shopping cart abandonment prediction: our findings indicate that gradient boosting with regularization outperforms the remaining models yielding an F1-Score of 0.8569 and an AUC value of 0.8182. Nevertheless, as gradient boosting tends to be computationally infeasible, a decision tree or boosted logistic regression may be suitable alternatives, balancing the trade-off between model complexity and prediction accuracy.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Classification; E-commerce; Machine learning; Prediction; Shopping cart abandonment; Supervised learning
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XIV - Marketing and Innovation
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XIV - Marketing and Innovation > Chair Business Administration XIV - Marketing and Innovation - Univ.-Prof. Dr. Daniel Baier
Result of work at the UBT: Yes
DDC Subjects: 300 Social sciences > 330 Economics
Date Deposited: 20 Nov 2020 08:56
Last Modified: 20 Nov 2020 08:56
URI: https://eref.uni-bayreuth.de/id/eprint/60165