Effect of Low-Level Interaction Data in Repeat Purchase Prediction Task
Eduard Kuric, Adam Puskas, Peter Demcak, Denisa Mensatorisova · International Journal of Human-Computer Interaction · 2023
Loyal customers play an important role in every store’s success. They tend to buy regularly and help stabilize incomes. Being able to identify potential repeat buyers allows marketers to act promptly and convince the users not to search for better offers elsewhere. Current customer behavior prediction approaches are based on non-interaction data (e.g., server logs). We see a gap in the research of the impact of low-level interaction data capturing user behavior more precisely (e.g., cursor movements, scrolls, inputs). We introduce three new datasets collected year-long from three different ecommerce stores. We evaluate the merit of low-level interactions for the task of repeat purchase prediction and study feature sets utilizing low-level interactions, together with non-interaction data. We compare their performance in the classification task to benchmark approaches relying solely on non-interaction data. Our experiments show inclusion of interaction data improves the prediction performance compared to the baseline non-interaction feature set.