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Customer profiling for Malaysia online retail industry using K-Means clustering and RM model

Malaysia's online retail industry is growing sophisticated for the past years and is not expected to stop growing in the following years. Meanwhile, customers are becoming smarter about buying. Online Retailers have to identify and understand their customer needs to provide appropriate services...

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Main Authors: Tan, Chun Kit, Mohd. Azmi, Nurulhuda Firdaus
Format: Article
Language:English
Published: Science and Information Organization 2021
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/94714/1/NurulhudaFirdaus2021_CustomerProfilingforMalaysiaOnlineRetail.pdf
http://eprints.utm.my/id/eprint/94714/
http://dx.doi.org/10.14569/IJACSA.2021.0120114
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spelling my.utm.947142022-03-31T15:53:31Z http://eprints.utm.my/id/eprint/94714/ Customer profiling for Malaysia online retail industry using K-Means clustering and RM model Tan, Chun Kit Mohd. Azmi, Nurulhuda Firdaus T Technology (General) Malaysia's online retail industry is growing sophisticated for the past years and is not expected to stop growing in the following years. Meanwhile, customers are becoming smarter about buying. Online Retailers have to identify and understand their customer needs to provide appropriate services/products to the demanding customer and attracting new customers. Customer profiling is a method that helps retailers to understand their customers. This study examines the usefulness of the LRFMP model (Length, Recency, Frequency, Monetary, and Periodicity), the models that comprised part of its variables, and its predecessor RFM model using the Silhouette Index test. Furthermore, an automated Elbow Method was employed and its usefulness was compared against the conventional visual analytics. As result, the RM model was selected as the finest model in performing K-Means Clustering in the given context. Despite the unusefulness of the LRFMP model in K-Means Clustering, some of its variables remained useful in the customer profiling process by providing extra information on cluster characteristics. Moreover, the effect of sample size on cluster validity was investigated. Lastly, the limitations and future research recommendations are discussed alongside the discussion to bridge for future works. Science and Information Organization 2021 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/94714/1/NurulhudaFirdaus2021_CustomerProfilingforMalaysiaOnlineRetail.pdf Tan, Chun Kit and Mohd. Azmi, Nurulhuda Firdaus (2021) Customer profiling for Malaysia online retail industry using K-Means clustering and RM model. International Journal of Advanced Computer Science and Applications, 12 (1). pp. 106-113. ISSN 2158-107X http://dx.doi.org/10.14569/IJACSA.2021.0120114
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Tan, Chun Kit
Mohd. Azmi, Nurulhuda Firdaus
Customer profiling for Malaysia online retail industry using K-Means clustering and RM model
description Malaysia's online retail industry is growing sophisticated for the past years and is not expected to stop growing in the following years. Meanwhile, customers are becoming smarter about buying. Online Retailers have to identify and understand their customer needs to provide appropriate services/products to the demanding customer and attracting new customers. Customer profiling is a method that helps retailers to understand their customers. This study examines the usefulness of the LRFMP model (Length, Recency, Frequency, Monetary, and Periodicity), the models that comprised part of its variables, and its predecessor RFM model using the Silhouette Index test. Furthermore, an automated Elbow Method was employed and its usefulness was compared against the conventional visual analytics. As result, the RM model was selected as the finest model in performing K-Means Clustering in the given context. Despite the unusefulness of the LRFMP model in K-Means Clustering, some of its variables remained useful in the customer profiling process by providing extra information on cluster characteristics. Moreover, the effect of sample size on cluster validity was investigated. Lastly, the limitations and future research recommendations are discussed alongside the discussion to bridge for future works.
format Article
author Tan, Chun Kit
Mohd. Azmi, Nurulhuda Firdaus
author_facet Tan, Chun Kit
Mohd. Azmi, Nurulhuda Firdaus
author_sort Tan, Chun Kit
title Customer profiling for Malaysia online retail industry using K-Means clustering and RM model
title_short Customer profiling for Malaysia online retail industry using K-Means clustering and RM model
title_full Customer profiling for Malaysia online retail industry using K-Means clustering and RM model
title_fullStr Customer profiling for Malaysia online retail industry using K-Means clustering and RM model
title_full_unstemmed Customer profiling for Malaysia online retail industry using K-Means clustering and RM model
title_sort customer profiling for malaysia online retail industry using k-means clustering and rm model
publisher Science and Information Organization
publishDate 2021
url http://eprints.utm.my/id/eprint/94714/1/NurulhudaFirdaus2021_CustomerProfilingforMalaysiaOnlineRetail.pdf
http://eprints.utm.my/id/eprint/94714/
http://dx.doi.org/10.14569/IJACSA.2021.0120114
_version_ 1729703211173937152
score 13.211869

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