Salma ALTHUBYANI Bahjat FAKIEH and Arwa WALI
Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
The exponential growth of global e-commerce, particularly, in the online fashion sector, has increased the demand for customer-centric analytical frameworks. Customer lifetime value (CLV) is an important concept for identifying, evaluating, classifying, and allocating resources to customers based on the value they add to the organization. This, in turn, helps organizations to get most benefit from their customers. While traditional CLV models rely primarily on transactional and financial data, they often overlook behavioral, emotional, and social factors that influence customer decisions in digital markets. Despite progress in this area, research on the impact of non-financial factors on CLV remains limited, particularly in the context of fashion e-commerce. This study explores the non-financial factors affecting CLV in Saudi Arabia’s online fashion e-commerce sector. A quantitative research methodology was employed, utilizing a questionnaire to collect 779 valid responses from customers who made online purchases and lived in Saudi Arabia. The theoretical framework and hypotheses were developed using relationship marketing theory. CLV was measured from the customer perspective using loyalty, usage quantity, and word-of-mouth dimensions. Both regression and correlation analysis were applied to test nineteen hypotheses derived from the literature. The results showed that customer trust and satisfaction, emotional connection, customer lifestyle, quality, product innovation, personalization, brand reputation, social media presence, convenience, accessibility, and demographic factors had a significant impact on CLV. However, customer age, price, brand experience, and influencer marketing showed no significant impact on CLV, and the related hypotheses were rejected. This research can contribute to a robust customer-centric predictive model that enhances the accuracy of CLV estimation using non-financial data, filling a critical gap in the existing literature.