Customer segmentation using clustering techniques / Mohamad Amir Salihin Mustafa

This study explores clustering techniques for customer segmentation, focusing on the K-Means algorithm in particular, and uses a dataset that was obtained from the customer data of an international supermarket company. Studying clustering methodologies, creating a K-Means clustering model, and asses...

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Main Author:	Mustafa, Mohamad Amir Salihin
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/96325/1/96325.pdf
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id	my-uitm-ir.96325
record_format	uketd_dc
spelling	my-uitm-ir.963252024-06-04T07:20:34Z Customer segmentation using clustering techniques / Mohamad Amir Salihin Mustafa 2024 Mustafa, Mohamad Amir Salihin Algorithms This study explores clustering techniques for customer segmentation, focusing on the K-Means algorithm in particular, and uses a dataset that was obtained from the customer data of an international supermarket company. Studying clustering methodologies, creating a K-Means clustering model, and assessing its quality for efficient client segmentation are the main goals. The Elbow method is employed to determine the ideal number of clusters (K value), resulting in the segmentation of customers according to their buying patterns. Customer profiling is the result of the segmentation process. The Silhouette Score is used to assess the quality of the clustering model, and it achieves a good value of 0.54. By demonstrating a good balance between cluster cohesiveness and cluster separation, this score shows that the K-Means method is successful in identifying unique customer categories. The supermarket company can improve overall business performance and customer satisfaction by using tailored customer engagement and targeted marketing strategies made possible by the insightful customer profiles that are produced. 2024 Thesis https://ir.uitm.edu.my/id/eprint/96325/ https://ir.uitm.edu.my/id/eprint/96325/1/96325.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Isa, Norulhidayah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Isa, Norulhidayah
topic	Algorithms
spellingShingle	Algorithms Mustafa, Mohamad Amir Salihin Customer segmentation using clustering techniques / Mohamad Amir Salihin Mustafa
description	This study explores clustering techniques for customer segmentation, focusing on the K-Means algorithm in particular, and uses a dataset that was obtained from the customer data of an international supermarket company. Studying clustering methodologies, creating a K-Means clustering model, and assessing its quality for efficient client segmentation are the main goals. The Elbow method is employed to determine the ideal number of clusters (K value), resulting in the segmentation of customers according to their buying patterns. Customer profiling is the result of the segmentation process. The Silhouette Score is used to assess the quality of the clustering model, and it achieves a good value of 0.54. By demonstrating a good balance between cluster cohesiveness and cluster separation, this score shows that the K-Means method is successful in identifying unique customer categories. The supermarket company can improve overall business performance and customer satisfaction by using tailored customer engagement and targeted marketing strategies made possible by the insightful customer profiles that are produced.
format	Thesis
qualification_level	Bachelor degree
author	Mustafa, Mohamad Amir Salihin
author_facet	Mustafa, Mohamad Amir Salihin
author_sort	Mustafa, Mohamad Amir Salihin
title	Customer segmentation using clustering techniques / Mohamad Amir Salihin Mustafa
title_short	Customer segmentation using clustering techniques / Mohamad Amir Salihin Mustafa
title_full	Customer segmentation using clustering techniques / Mohamad Amir Salihin Mustafa
title_fullStr	Customer segmentation using clustering techniques / Mohamad Amir Salihin Mustafa
title_full_unstemmed	Customer segmentation using clustering techniques / Mohamad Amir Salihin Mustafa
title_sort	customer segmentation using clustering techniques / mohamad amir salihin mustafa
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/96325/1/96325.pdf
_version_	1804889985499791360

Customer segmentation using clustering techniques / Mohamad Amir Salihin Mustafa

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