Enhancing Customer Engagement in Loyalty Programs Through AI-Powered Market Basket Prediction Using Machine Learning Algorithms

Artificial Intelligence (AI) has paved the way for numerous technological advancements in various fields, seamlessly connecting one domain to another. In the realm of large-scale retail, AI has been particularly impactful in understanding consumer behavior, as machine learning algorithms play a significant role in this area. In pursuit of this objective, the present work focuses on predicting the next market basket for each user among consumers engaged in loyalty programs. By adopting a hybrid approach and incorporating customer transaction data from those participating in these programs, a link between large retailers and their customers is created. This encourages a deeper understanding of consumer behavior and the improvement of purchasing strategies. To achieve this goal, different machine learning methods have been utilized to predict the next market basket, including Support Vector Machines (SVM), Decision Trees, Random Forests, Logistic Regression, and k-Nearest Neighbors (k-NN). This assortment of methods enables selecting the one that exhibits the best performance, guaranteeing the highest precision of predictions. By harnessing the power of AI and machine learning algorithms, a connection can be established between consumer behavior prediction and effective marketing strategies. In turn, this empowers retailers to enhance their understanding of their customers, leading to increased satisfaction and the optimization of customer engagement in loyalty programs.