Customer satisfaction and market segmentation of Hong Kong supermarket industry

Abstract

With the increasingly fierce competition in the business environment of Hong Kong, customer satisfaction is a critical success factor for servicing industries. This factor is especially important in the rapidly expanding grocery giants market – supermarkets. However, there is a lack of in-depth study on the factor structure of customer satisfaction. Furthermore, the store choice behaviour of customers and the critical factor affecting their overall satisfaction are still unclear. Without these kinds of information, supermarket operators may be incapable of satisfying all customers with unique marketing management programs. As a result, resources utilization and profit maximization may not be complete. In this research, factors for measuring customer satisfaction are identified. The relationship between the demographic profiles of customers and their decision criteria in store choice is investigated. The critical factors of overall customer satisfaction are compared across different clusters. A survey was conducted to collect the levels of satisfaction and importance of customers towards two chain supermarket leaders in Hong Kong. Firstly, factor analysis was used to identify factors in measuring customer satisfaction. This resulted in five factors including promotion, convenience, personal interaction, physical aspects and technical quality. Secondly, customers were grouped into clusters using cluster analysis to study their store choice behaviour. Three clusters were identified and given the names ‘empathy-seeking group’, ‘demanding group’ and ‘handy-seeking group’. Finally, structural equation modelling was employed to find out the critical factors affecting overall satisfaction among different clusters. Based on the results obtained, supermarket operators can concentrate on the various selection criteria of customers to boost up their satisfaction. Findings from this research are also particularly useful for new market entrants to identify the clusters that their potential customers belong to.