Application of Machine Learning Algorithms in Predicting Customer Loyalty towards Retailers

Customer loyalty is a long-term relationship with consumers that every company strives for. The specificity of food retailing lies in a much broader range of factors, price and non-price characteristics of the retailer, which can influence customer loyalty towards the retailer (brand) than is the case, for example, with loyalty to a single manufacturer's brand. Customer loyalty towards the retailer can encompass more dimensions than repeat purchase itself. In this paper, it is examined as a multidimensional construct using three dimensions: Purchase Intention refers to the intention to buy again, as it mainly concerns retailers with which consumers have already had certain experiences; Purchase Share is an important dimension, as most consumers combine their purchases at several retailers, while Willingness to Recommend is an emotional dimension of loyalty, as it represents the connection between consumer and the retailer's brand. The paper aims to evaluate the application of machine learning algorithms in predicting loyalty towards retailers, focusing on the high accuracy and reliability of the prediction results. Particular attention will be paid to analyzing the factors and identifying the most important factors that contribute to the aforementioned dimensions of loyalty and allow for more precise targeting and adaptation of marketing strategies. The machine learning algorithms aim to gain a more detailed and accurate understanding of the factors influencing customer loyalty towards retailers. Keywords: Customer Loyalty, Retail Sector, Supervised Machine Learning, Prediction, Price Dynamics