From Clicks to Conversions: Leveraging Apriori and Behavioural Segmentation in E-Commerce

Abstract

    In this study, a data mining and machine learning approach is presented to analyse visitor behaviour and preferences on an e-commerce platform. The Apriori algorithm is employed for association rule mining to uncover patterns between item views, cart additions, and purchases. Visitor segmentation is performed based on browsing activity, and a logistic regression model is developed to predict purchase behaviour. It is observed that visitors who view specific items are more likely to add them to their cart or proceed to purchase, and that cart additions significantly increase the likelihood of purchase. Four distinct visitor segments are identified through clustering, reflecting varying levels of engagement. Among the features analysed, the number of items viewed and the total view count are found to be the most influential predictors of purchasing intent. Using these two features, the logistic regression model achieves an accuracy of 0.89, demonstrating the effectiveness of a simple, interpretable approach for behaviour-based personalization in e-commerce contexts.