Retail serves as a crucial element in connecting product to end-customer. Accordingly, product assortment and placement are key factors in enhancing a store’s attractiveness and promote convenience shopping. Therefore, customizing retail store layout must abide with customer behaviour. Market basket analysis (MBA) and association rule is the common framework to understand customer behaviour through historical transaction data. Yet, it can be extended to inform store layout improvement based on buying patterns. The current study aims to unveil customer buying pattern through MBA and association rules, then, use the collected insights to propose a new store layout design. We employed the Apriori algorithm to extract itemset relationships from the historical transaction data of a local convenience store brand. Furthermore, we integrate leverage metric to strengthen rule validation, offering more reliable interpretation compared to prior studies. Our findings suggest five solid rules that became the foundation of the proposed store layout, including a notably strong relationship between snack and drink products. The proposed framework can be adopted by retail businesses to improve store layout design tailored to their customer buying pattern.
Widiyanesti et al. (Tue,) studied this question.