KopiCulture: Unveiling Customer Loyalty in Malaysia's Coffee Market through Clustering Algorithms for Local Cafe Insights

Tuan Norhafizah, Tuan Zakaria and Che Ku Nuraini, Che Ku Mohd (2024) KopiCulture: Unveiling Customer Loyalty in Malaysia's Coffee Market through Clustering Algorithms for Local Cafe Insights. Journal of Data Science, 2024 (35). ISSN 2805-5160

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Abstract

In recent years, the coffee market in Malaysia has expanded significantly, propelled by an expanding cafe culture and consumer demand for unique coffee experiences. It is crucial for coffee retailers, such as global chains and local cafes, to comprehend customer loyalty in this dynamic environment. This study aims to identify customer loyalty patterns in the Malaysian coffee market, focusing on the Malaysia Starbucks customer survey dataset. Using clustering algorithms such as KMeans, KMeans with Principal Component Analysis (PCA), single linkage, complete linkage, DBScan, and DBScan in conjunction with PCA, we identify distinct customer segments based on loyalty patterns. Our findings give Starbucks and local coffee shops valuable insights, allowing them to tailor their marketing strategies and improve customer retention efforts. Through this analysis, we contribute to the expanding body of knowledge on customer loyalty in the context of the Malaysian coffee market and offer implications for coffee retailers seeking to thrive in this competitive environment.

Item Type:	Article
Uncontrolled Keywords:	Customer loyalty, clustering algorithms, coffee market, machine learning, marketing strategies
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HD Industries. Land use. Labor Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	05 Nov 2024 08:50
Last Modified:	05 Nov 2024 08:50
URI:	http://eprints.intimal.edu.my/id/eprint/2014

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