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Jimmi Chitra
Jerry Heikal

Abstract

This research aims to employ the K-Means clustering algorithm to segment customers in foreign banks operating within the Indonesian market. The primary objective is to enhance marketing strategies and customer service by identifying distinct customer groups based on their banking behaviours and preferences. By analysing demographic, transactional, and psychographic data, the study seeks to uncover patterns that can facilitate personalized offerings and targeted communication strategies. Overall, this research contributes to the optimization of marketing efforts and the enhancement of customer satisfaction in the competitive landscape of foreign banking in Indonesia. Through effective segmentation and targeted value propositions, foreign banks can strengthen their market position and foster long-term relationships with diverse customer segments. Overall, this research contributes to the optimization of marketing efforts and the enhancement of customer satisfaction in the competitive landscape of foreign banking in Indonesia. Through effective segmentation and targeted CVPs, foreign banks can strengthen their market position and foster long-term relationships with diverse customer segments.

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How to Cite
Chitra, J., & Heikal, J. (2024). Customer segmentation using the K-Means Clustering algorithm in Foreign Banks in Indonesia. Indonesia Accounting Research Journal, 11(4), 230–241. Retrieved from http://journals.iarn.or.id/index.php/Accounting/article/view/289
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