Towards Accurate Predictions of Customer Purchasing Patterns

Valero-Fernandez, Rafael; Collins, David J.; Lam, K.P.; Rigby, Colin; Bailey, James

Abstract

range of algorithms was used to classify online retail customers of a UK company using historical transaction data. The predictive capabilities of the classifiers were assessed using linear regression, Lasso and regression trees. Unlike most related studies, classifications were based upon specific and marketing focused customer behaviours. Prediction accuracy on untrained customers was generally better than 80%. The models implemented (and compared) for classification were: Logistic Regression, Quadratic Discriminant Analysis, Linear SVM, RBF SVM, Gaussian Process, Decision Tree, Random Forest and Multi-layer Perceptron (Neural Network). Postcode data was then used to classify solely on demographics derived from the UK Land Registry and similar public data sources. Prediction accuracy remained better than 60%.

Citation

Valero-Fernandez, R., Collins, D. J., Lam, K., Rigby, C., & Bailey, J. (2017, August). Towards Accurate Predictions of Customer Purchasing Patterns. Presented at IEEE Computer and Information Technology 2017, Helsinki

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IEEE Computer and Information Technology 2017
• Start Date: Aug 21, 2017
• End Date: Aug 23, 2017
• Acceptance Date: Jun 2, 2017
• Publication Date: Aug 23, 2017
• Publicly Available Date: May 26, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Series Title: IEEE Computer and Information Technology, 2017
• DOI: https://doi.org/10.1109/CIT41763.2017
• Keywords: classifiers; Regression; segmentation; customer targeting; ecommerce; database marketing; life value cycle; churn ratio
• Public URL: https://keele-repository.worktribe.com/output/408887
• Publisher URL: https://doi.org/10.1109/cit.2017.58