Advancing Towards Sustainable Retail Supply Chains: AI-Driven Consumer Segmentation in Superstores
Year: 2024
Type: conference-paper
Abstract: Artificial intelligence has revolutionized retail by enhancing business decision-making. This research applies the RFM (Recency, Frequency, Monetary) framework for customer segmentation, promoting sustainable consumer behaviour and eco-friendly products. Mobility issues, such as efficient goods movement and customer access, are also pivotal in sustainable retail supply chains. A systematic literature review (SLR) and Python-based clustering techniques (K-Means, hierarchical, DBSCAN) are employed to analyse a four-year dataset of customer data. The SLR identified six key areas from 71 articles. Clustering results varied: RFM binning found four clusters, K-Means and Mean Shift found three, and hierarchical and DBSCAN found two. The study emphasizes a data-centric retail strategy and the transformative impact of machine learning on customer engagement.
Authors: Golam Sakaline, László Buics
Institution: Széchenyi István University
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: gold
Funder: Széchenyi István Egyetem
OpenAlex ID: W4404189189