Machine Learning Models for Predicting Consumer Purchasing Behavior

Michael A. Thompson

Machine Learning Models for Predicting Consumer Purchasing Behavior

Author(s)	Dr. Michael A. Thompson
Country	United States
Abstract	Predicting consumer purchasing behavior has become an important application of machine learning in digital commerce, retail management, customer relationship management, and personalized marketing. Conventional statistical techniques provide useful explanations of consumer decisions but may struggle to capture nonlinear relationships, high-dimensional behavioral signals, and complex interactions among demographic, transactional, and digital-engagement variables. This study evaluates the potential of logistic regression, decision tree, random forest, and gradient-boosting models for predicting whether a consumer will complete a purchase. Because no verified organizational dataset was provided, the investigation is designed as an explicitly simulation-based study using a hypothetical dataset of 5,000 consumer interaction records. The simulated variables include browsing frequency, session duration, prior purchases, average transaction value, discount responsiveness, product-page engagement, cart abandonment, email interaction, and customer tenure. Model performance is assessed using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve. The simulated results indicate that gradient boosting provides the strongest overall predictive performance, achieving an illustrative accuracy of 88.6% and an area under the curve of 0.92. Random forest produces closely comparable results and offers greater robustness than a single decision tree. Logistic regression remains valuable as an interpretable benchmark but demonstrates weaker performance when behavioral relationships are nonlinear. The study concludes that predictive effectiveness depends not only on model selection but also on data quality, temporal validity, explainability, privacy protection, and responsible use of consumer information. Machine learning should support customer-centered decision-making without enabling manipulative targeting, discriminatory segmentation, or inappropriate collection of personal data.
Keywords	consumer purchasing behavior, machine learning, predictive analytics, customer analytics, gradient boosting, random forest, digital marketing.
Field	Engineering
Published In	Volume 1, Issue 1, January-February 2019
Published On	2019-02-04

View / Download PDF File

About AIJRI Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@aijri.com

American International Journal of Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Machine Learning Models for Predicting Consumer Purchasing Behavior

Share this