Leveraging Network Science for Customer Segmentation and Product Recommendation

The rapid growth in e-commerce has forced the development and implementation of enhanced customer segmentation and recommendation systems, improving business results and improving customer experience. Traditional approaches, such as RFM analysis and clustering algorithms like K-means, are very helpful in many situations but usually fail to catch complex interdependencies among customers and products. This paper proposes a new approach using network science methodologies, a bipartite graph model, toward the advancement of customer segmentation and product recommendation. It implements a bipartite graph of customers and products using the "Online Retail II" dataset and proceeds with community detection, segmenting customers into unique groups. This current study converts the bipartite graph into a customer-to-customer network, where complex interrelations are revealed that may allow the development of highly targeted and precise marketing strategies. Centrality measures like degree, closeness, and betweenness centrality are applied to quantify the power of customers and their interconnectedness in the network. These metrics provide very important information to focused marketing, on customer roles such as the key influencer and bridging nodes. Indeed, network analysis approaches help explore customers' behaviors much deeper than traditional methodologies, such as in community detection, centrality analysis, and collaborative filtering for much more personalized recommendations. This research by the authors has been done to emphasize the need for network science methodologies within customer segmentation and product recommendations for insights on targeted marketing strategies, customer loyalty initiatives, and cross-selling possibilities in competitive e-commerce contexts.