Title: Predicting individual behaviour: an empirical approach in online marketing

Authors: Sjoerd Borst; Flavius Frasincar; Vladyslav Matsiiako

Addresses: Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, NL-3000 DR Rotterdam, The Netherlands ' Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, NL-3000 DR Rotterdam, The Netherlands ' Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, NL-3000 DR Rotterdam, The Netherlands

Abstract: This paper investigates the use and relevance of data mining techniques for online direct marketing. Cookie log files are obtained and transformed into time-aggregated web user characteristics to predict which users are likely to purchase. Given these characteristics, users observe relevant banners. Modern classification techniques, i.e., support vector machines (SVM), random forests (RF), bagging (BA), and boosting (BO), are compared with classic data mining techniques, i.e., multinomial logistic (MNL) regressions, neural networks (NN), and Naive Bayes (NB). We found that, after feature selection, all modern techniques significantly outperform the classic methods NN and NB, accuracy-wise. MNL performs similarly to BO and better than BA. RF performs best with an average accuracy of 70.7%, followed by SVM with an average accuracy of 67.6%. The RF model has led to a decrease in banners served, while preserving the number of sales. Several novel time-related features have also been proposed for online bannering.

Keywords: online marketing; impressions; purchase prediction; web banners; website advertisements; optimisation; forecasting; random forest; data mining.

DOI: 10.1504/IJWET.2020.113067

International Journal of Web Engineering and Technology, 2020 Vol.15 No.3, pp.283 - 306

Published online: 16 Feb 2021 *

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