Smart Support System for Evaluating Clustering as a Service: Behaviour Segmentation Case Study

M. Galal, Tamer Salah, M. Aref, Esam Elgohary
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Abstract

: Modern surveys reveal diminishing of socio-demographic segment descriptors, and evolution of dramatic increase of online services and customers. These conditions attract both researchers and decision makers to enhance market segmentation to gain customer loyalty and prevent customer attrition. This research contributes in developing a minor expert system to automate the evaluation of clustering process to enhance the Clustering as a Service (CaaS) through customer behavior segmentation case study. It comes as a part of the software development process to develop Customer Loyalty Intelligent Personalization (CLIP) system. The proposed expert system has been successfully implemented and tested over four months in two different dataset to proof the flexibility of implementation . The used data is a real customer data, it consists of 1659 customers, 146 products, and 5685 orders. The other datset consists of 668 transactions of real data in restaurant. The clustering is applied using the hierarchical clustering and it reached a good results with high efficiency. The proposed solution aims to be integrated with a plug and play product as it will be configured in different domains.
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评价聚类服务的智能支持系统:行为分割案例研究
现代调查揭示了社会人口细分描述符的减少,以及在线服务和客户急剧增加的演变。这些条件吸引了研究人员和决策者加强市场细分,以获得客户忠诚度和防止客户流失。本研究通过对客户行为细分案例的研究,开发了一个小型专家系统来实现聚类过程的自动化评估,以增强聚类即服务(CaaS)。它是客户忠诚智能个性化(CLIP)系统软件开发过程的一部分。所提出的专家系统已经在两个不同的数据集上成功实施和测试了四个多月,以证明实施的灵活性。使用的数据是真实的客户数据,由1659个客户、146个产品和5685个订单组成。另一个数据集由668笔餐厅真实数据组成。采用层次聚类方法进行聚类,取得了较好的聚类效果,效率高。建议的解决方案旨在与即插即用产品集成,因为它将在不同的域中配置。
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