Title: Location-based Association Rule Mining for In-store Online Order Fulfillment Strategy
Abstract: In-store fulfillment is one of the omnichannel retailing strategies utilizing store inventories to fulfill online orders either by home delivery or allowing customers to pick them up in stores. However, implementing in-store fulfillment service for all the stores and product groups can be inefficient and costly. To address this issue, this study suggests a location-based association rule mining approach for effective in-store online order fulfillment strategy. This approach integrates association rule mining and spatial statistical methods based on the online customer order data from an omnichannel retailer in South Korea. The outcome of this research can provide store-specific customized retailing strategies to improve the effectiveness of the in-store fulfillment services.
Publication Year: 2023
Publication Date: 2023-12-31
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot