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.

客户细分将客户划分为具有与营销相关的相似模式或特征的消费者组，尤其是在目标广告方面。这些模式或特征可能涉及年龄，性别，兴趣和消费习惯。客户细分模型为决策者提供了有效的营销资源分配，并最大程度地提高了交叉销售和向上销售的机会。聚类分析是了解业务参与者并获得具有相似的人口统计，行为或趋势的客户细分的关键方法。在电子商务中，在线贸易组织将客户数据存储在分布式数据中心中，以限制基础结构或遵守公司规范。逐渐地，他们需要更新客户信息，以使业务报价与现代趋势保持一致。在一个位置收集电子客户数据以进行集中式群集是昂贵且详尽的。因此，分布式集群最适合对固有分布的资源中的客户数据进行分类和分段。当前，分布式体系结构遭受通信开销或不正确的全局解决方案的困扰。本文提出了一种新颖的多层分层超对等P2P（MT-SP2P）网络体系结构。提出的MT-SP2P体系结构提供了一种解决方案，可在保持群集质量的同时提高分布式群集问题的速度。还提出了一种使用MT-SP2P体系结构的新型分布式聚类算法，以在不影响聚类质量的前提下提高聚类速度。客户细分也被视为管理概念。我们的分布式细分模型和体系结构可通过有效且可扩展的客户细分来帮助企业提高利润并提高客户服务水平。讨论了计算结果和管理洞察力。我们从具有不同配置和大小的不同实际客户数据的实验结果中发现。与集中式方法相比，所提出的体系结构和分布式聚类算法将聚类速度提高了90％以上。