Market area analysis for in-store online order fulfillment: a data-driven approach

With the explosive growth of e-commerce in the past few decades, many conventional brick-and-mortar retailers have entered the online shopping market with an in-store fulfillment strategy that utilizes existing stores to pick online orders and deliver them from stores. The purpose of this study is to provide a systematic e-fulfilment store location and product allocation strategy in an omnichannel retail environment. We propose a data-driven approach that integrates the spatial cluster detection method and market basket analysis based on online customer order data from an omnichannel retailer in South Korea. The proposed framework is as follows: First, product categories suitable for in-store order fulfilment services are determined based on consumer behavior and historical sales records for the target month of the year. Second, a hotspot analysis was performed to detect spatial clusters of high-demand locations for specific product groups. Then, stores desirable for e-fulfilment services are determined based on their accessibility to hotspots of online orders. Finally, store-specific product groups are suggested based on the association rule mining results of customer orders within the service area of each selected store. The same-day delivery service provided by e-fulfillment stores can provide consumers with the benefits of quick commerce. From a retailer’s perspective, the outcome of this study can provide store-specific customized marketing strategies to improve the effectiveness of in-store online fulfilment services, thus providing the opportunity to revitalize existing B&M stores.