Predicting individual behaviour: an empirical approach in online marketing Online publication date: Tue, 16-Feb-2021
by Sjoerd Borst; Flavius Frasincar; Vladyslav Matsiiako
International Journal of Web Engineering and Technology (IJWET), Vol. 15, No. 3, 2020
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Web Engineering and Technology (IJWET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook