Soyemi Jumoke

Work place: Department of Computer Science, The Federal Polytechnic, Ilaro, Nigeria



Research Interests: Computer Science & Information Technology


Jumoke Soyemi is a Lecturer of Computer Science and have been teaching and researching in last seventeen years. She holds a B.S.c, M.Sc. and Ph.D in Computer Science from Obafemi Awolowo University, Ile-Ife, University of Ibadan, Ibadan and Covenant University, Ota respectively. In the area of research, she has won many travel fellowship awards to attend conferences, workshops and training outside Nigeria aside winning research grants. She has several publications in high impact journals. She has also shared a number of her research outputs both within and outside the country via conference presentations.

Author Articles
A Novel Approach to Customer Segmentation for Optimal Clustering Accuracy

By Hammed Mudasiru Soyemi Jumoke

DOI:, Pub. Date: 8 Feb. 2024

Customer segmentation is not only limited to the identification of user groups but searching and determining the attitude of individual customer groups toward a particular product or service aside helping organization in developing better marketing strategies. Many studies have proposed different techniques for customer segmentation, but some of these studies failed to examine individual customer’s needs in the cluster. In a customer segmentation, when customers are grouped into various cluster based on their common needs, there may be customers that have other needs that differ from the general needs of the group. In a situation where the needs of individual were not captured, organizations may find it difficult to control the rendering of their services. The aim of this study is to extract the individual customer’ needs to enhance organizations’ services that meet the needs of customers, as well as increase organization profits. This study, therefore, proposes the use of an associative rules mining algorithm augmented with assignment optimization to properly examine the needs of individual customers in the group. This approach enhances the cross-segmentation of customers for better marketing strategies and the assignment technique also improved the segmentation processing speed. The degree of accuracy of the system developed was tested with about 9,500 customers’ dataset that was obtained from goggle multi category online store dataset. Both customer transaction history dataset and customer purchasing behavior dataset were obtained for segmentation which achieved 94.5% customer segmentation accuracy. The implementation was done using Python programming language.

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