Towards Accurate Predictions of Customer Purchasing Patterns

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

Abstract

A 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). Towards Accurate Predictions of Customer Purchasing Patterns. In 2017 IEEE International Conference on Computer and Information Technology (CIT). https://doi.org/10.1109/cit.2017.58

• Conference Name: 2017 IEEE International Conference on Computer and Information Technology (CIT)
• Conference Location: Helsinki, Finland
• Start Date: Aug 21, 2017
• End Date: Aug 23, 2017
• Online Publication Date: Sep 14, 2017
• Publication Date: 2017-08
• Deposit Date: Dec 15, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Book Title: 2017 IEEE International Conference on Computer and Information Technology (CIT)
• ISBN: 978-1-5386-0959-0
• DOI: https://doi.org/10.1109/cit.2017.58
• Publisher URL: https://ieeexplore.ieee.org/document/8031468