Primary Determinants and Strategic Implications for Customer Loyalty in Pet-Related Vertical E-Commerce: A Machine Learning Approach

Abstract

In the contemporary and dynamic business landscape, the establishment of a loyal customer base is a fundamental imperative for long-term organizational viability. This research undertakes a comprehensive exploration into the formation of customer loyalty within the niche of pet-related vertical e-commerce, focusing on South Korea, and leverages advanced machine learning methodologies. We identify key factors that significantly impact customer loyalty development using various machine learning models, including logistic regression analysis, decision trees, support vector machines, random forests, and XGBoost. Our empirical study shows that encouraging customer transactions plays a crucial and transformative role in building loyalty regardless of the day of the week. Furthermore, the strategic promotion of mobile application notifications and the active encouragement of customer participation through product reviews are indispensable strategies for strengthening and solidifying customer loyalty. These findings have crucial implications not only for enterprises within the pet-related e-commerce sector but also for the broader e-commerce domain. We hereby propose a methodology to identify loyal customers and systematically analyze the key factors that influence their formation using machine learning in the vertical e-commerce pet industry.