Distributed clustering using multi-tier hierarchical overlay super-peer peer-to-peer network architecture for efficient customer segmentation

Abstract Customer segmentation divides customers into groups of consumers with similar patterns or characteristics relevant to marketing, especially with respect to target advertising. These patterns or characteristics could involve age, gender, interests, and spending habits. A customer segmentation model provides decision-makers with an effective allocation of marketing resources and maximization of cross-selling and up-selling opportunities. Clustering analysis is a key approach to understand business participants and obtain customers’ segments with similar demographics, behaviors, or trends. In e-business, online trade organizations store customer data in distributed data centers to limit the infrastructure or comply with the company norms. Incrementally, they need to update customer’s information to align the business offers with the contemporary trend. It is costly and exhaustive to collect the e-customer data at one location to exert centralized clustering. Therefore, distributed clustering is best suitable to categorize and segment customer data from inherently distributed resources. Currently, distributed architectures suffer from communication overhead or inaccurate global solutions. In this paper, a novel multi-layer hierarchical super-peer P2P (MT-SP2P) network architecture is proposed. The proposed MT-SP2P architecture provides a solution to enhance the speed of a distributed clustering problem while maintaining clustering quality. A novel distributed clustering algorithm is also proposed using the MT-SP2P architecture to improve the clustering speed without compromising the clustering quality. Customer segmentation is also seen as a managerial concept. Our distributed segmentation model and architecture help enterprises increase profits and improve customer service levels through effective and scalable customer segmentation. Computational results and managerial insights are discussed. We found from experimental results on different real customer data with various configurations and sizes. The proposed architecture and distributed clustering algorithm have improved the clustering speed by more than 90% compared to the centralized approaches. The findings show that the proposed model provided better insights and managerial implications concerning the chosen clustering techniques and distributed customer segments.