Smart Support System for Evaluating Clustering as a Service: Behaviour Segmentation Case Study
Galal, M., Salah, T., Aref, M., ElGohary, E. (2022). Smart Support System for Evaluating Clustering as a Service: Behaviour Segmentation Case Study. EKB Journal Management System, 22(2), 35-43. doi: 10.21608/ijicis.2021.69041.1074
Mohamed Galal; Tamer Salah; Mostafa Aref; Esam ElGohary. "Smart Support System for Evaluating Clustering as a Service: Behaviour Segmentation Case Study". EKB Journal Management System, 22, 2, 2022, 35-43. doi: 10.21608/ijicis.2021.69041.1074
Galal, M., Salah, T., Aref, M., ElGohary, E. (2022). 'Smart Support System for Evaluating Clustering as a Service: Behaviour Segmentation Case Study', EKB Journal Management System, 22(2), pp. 35-43. doi: 10.21608/ijicis.2021.69041.1074
Galal, M., Salah, T., Aref, M., ElGohary, E. Smart Support System for Evaluating Clustering as a Service: Behaviour Segmentation Case Study. EKB Journal Management System, 2022; 22(2): 35-43. doi: 10.21608/ijicis.2021.69041.1074

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

International Journal of Intelligent Computing and Information Sciences
Article 3, Volume 22, Issue 2, May 2022, Page 35-43  XML PDF (612.58 K)
Document Type: Original Article
DOI: 10.21608/ijicis.2021.69041.1074
View on SCiNiTO View on SCiNiTO
Authors
Mohamed Galal email orcid 1; Tamer Salah2; Mostafa Areforcid 3; Esam ElGoharyorcid 4
1Department of Computer Science, Faculty of Computer and Information Science, Ain Shams University, and (National Bank of Egypt), Cairo, Egypt
2Minimax Projects Manager Mansoura, Egypt
3Department Computer Science, Faculty of Computer and Information Sciences,Ain Shams University, Cairo, Egypt.
4Department of Information systems, Institute of National Planning, (CLIP project manager) Cairo, Egypt
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.
Keywords
Customer Segmentation; Hierarchical Clustering; Clustering as a Service (CaaS)
Statistics
Article View: 208
PDF Download: 186