Category role aided market segmentation approach to convenience store chain category management

• Propose an innovative market segmentation model to support category management in chain convenience stores.
• Develop a new similarity measure (named HCsim()) to evaluate the distance between two stores.
• Develop an improved weighted fuzzy K-means clustering algorithm (WFKM).
• Conduct an empirical study on collected data from PetroChina convenience stores.

Category management (CM) plays an increasingly important role in retailing management, as it aids retailers to increase their core competitiveness, maximise profits and ensure a good long-term customer relationship. This technique has been successfully applied to diverse large manufacturers and wholesale retailers. However, it remains a challenging task to directly employ the CM technique in convenience store (CVS) chain(s). This is because CVS chains are often distributed in a variety of areas, each store has impulsive consumers, and the traditional market segmentation attributes (e.g. consumer age, salary, and background) are difficult to collect under such circumstances. This makes it impractical to apply one general CM solution to all CVS chains. Hence, it is crucial to segment a market region and then apply customised CM solutions to the corresponding segments. This paper presents an innovative market segmentation model which is driven by category-role (CR), for the first time, to support CM in CVS chains. A new similarity measure (named HCsim()) and an improved weighted fuzzy K-means clustering algorithm (WFKM) are developed in an effort to cluster the CVSs. The usefulness and applicability of this study is illustrated by means of an empirical study to provide marketing strategy decision support. The derived results are also discussed and compared with existing methods.