Customer profiling using K-means clustering method / Nik Asyraniasna Nik Mohd Asri

Businesses must understand customer behavior in today's ever-changing business environment in order to properly customize their marketing strategy. With the use of the K-means clustering approach, this research seeks to improve customer profiling by allowing businesses to divide their customers...

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Main Author:	Nik Mohd Asri, Nik Asyraniasna
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/95993/1/95993.pdf
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id	my-uitm-ir.95993
record_format	uketd_dc
spelling	my-uitm-ir.959932024-05-30T09:06:23Z Customer profiling using K-means clustering method / Nik Asyraniasna Nik Mohd Asri 2024 Nik Mohd Asri, Nik Asyraniasna Algorithms Businesses must understand customer behavior in today's ever-changing business environment in order to properly customize their marketing strategy. With the use of the K-means clustering approach, this research seeks to improve customer profiling by allowing businesses to divide their customers into discrete groups according to shared behaviors and preferences. Through the analysis of various customer data sets, such as people, products, promotion, place, the K-means algorithm can detect clusters that correspond to consistent client groups. The next phase of the project will concentrate on creating a thorough customer profiling system that makes use of these clusters to produce insightful data on customer preferences. This will allow companies to create customized marketing campaigns that appeal to certain target customers. By providing more relevant content, this strategy not only increases customer satisfaction but also improves marketing effectiveness and boosts conversion rates. In order to facilitate smooth business interactions with the profiling system, the project will incorporate the K-means clustering technique for consumer segmentation and optimize it. The end goal is to provide organizations with an effective tool that helps them better understand their customer segments, enabling them to develop more individualized and successful communication strategies in the context of a competitive market. 2024 Thesis https://ir.uitm.edu.my/id/eprint/95993/ https://ir.uitm.edu.my/id/eprint/95993/1/95993.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Sakamat, Norzehan
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Sakamat, Norzehan
topic	Algorithms
spellingShingle	Algorithms Nik Mohd Asri, Nik Asyraniasna Customer profiling using K-means clustering method / Nik Asyraniasna Nik Mohd Asri
description	Businesses must understand customer behavior in today's ever-changing business environment in order to properly customize their marketing strategy. With the use of the K-means clustering approach, this research seeks to improve customer profiling by allowing businesses to divide their customers into discrete groups according to shared behaviors and preferences. Through the analysis of various customer data sets, such as people, products, promotion, place, the K-means algorithm can detect clusters that correspond to consistent client groups. The next phase of the project will concentrate on creating a thorough customer profiling system that makes use of these clusters to produce insightful data on customer preferences. This will allow companies to create customized marketing campaigns that appeal to certain target customers. By providing more relevant content, this strategy not only increases customer satisfaction but also improves marketing effectiveness and boosts conversion rates. In order to facilitate smooth business interactions with the profiling system, the project will incorporate the K-means clustering technique for consumer segmentation and optimize it. The end goal is to provide organizations with an effective tool that helps them better understand their customer segments, enabling them to develop more individualized and successful communication strategies in the context of a competitive market.
format	Thesis
qualification_level	Bachelor degree
author	Nik Mohd Asri, Nik Asyraniasna
author_facet	Nik Mohd Asri, Nik Asyraniasna
author_sort	Nik Mohd Asri, Nik Asyraniasna
title	Customer profiling using K-means clustering method / Nik Asyraniasna Nik Mohd Asri
title_short	Customer profiling using K-means clustering method / Nik Asyraniasna Nik Mohd Asri
title_full	Customer profiling using K-means clustering method / Nik Asyraniasna Nik Mohd Asri
title_fullStr	Customer profiling using K-means clustering method / Nik Asyraniasna Nik Mohd Asri
title_full_unstemmed	Customer profiling using K-means clustering method / Nik Asyraniasna Nik Mohd Asri
title_sort	customer profiling using k-means clustering method / nik asyraniasna nik mohd asri
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/95993/1/95993.pdf
_version_	1804889977782272000

Customer profiling using K-means clustering method / Nik Asyraniasna Nik Mohd Asri

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