Retail analytics: store segmentation using Rule-Based Purchasing behavior analysis

ABSTRACT

Retailers are facing challenges in making sense of the significant amount of data available for a better understanding of their customers. While retail analytics plays an increasingly important role in successful retailing management, comprehensive store segmentation based on Data Mining-based Retail Analytics is still an under-researched area. This study seeks to address this gap by developing a novel approach to segment the stores of retail chains based on ‘purchasing behavior of customers’ and applying it in a case study. The applicability and benefits of using Data Mining techniques to examine purchasing behavior and identify store segments are demonstrated in a case study of a global retail chain in Istanbul, Turkey. Over 600 K transaction data of a global grocery retailer are analyzed and 175 stores in Istanbul are successfully segmented into five segments. The results suggest that the proposed new retail analytics approach enables the retail chain to identify clusters of stores in different regions using all transaction data and advances our understanding of store segmentation at the store level. The proposed approach will provide the retail chain the opportunity to manage store clusters by making data-driven decisions in marketing, customer relationship management, supply chain management, inventory management and demand forecasting.

KEYWORDS:

• Data mining
• purchasing behavior
• store segmentation
• business analytics
• data-driven decision making

Disclosure statement

No potential conflict of interest was reported by the author(s).