Abstract
Customer segmentation is important because it can help businesses to reach out to all of the customers in the market. Without customer segmentation, it is difficult to develop a marketing strategy that fits for all groups of people. Many research papers exploit the general attributes in finding the customer segmentation. However, there are several issues when using general attributes which include the difficulty in obtaining customer’s general information, incorrect and missing data in the customer information dataset and people who has similar general attributes to one another does not necessarily have similar purchasing pattern. Therefore, this study proposes the use of transactional attributes to perform customer segmentation. Three clustering algorithms were employed namely, KMeans, KModes, and KMedoids. The algorithm that produced the highest Silhouette and Davies-Bouldin score was the KMeans algorithm, in which the most optimal number of clusters were found to be three.
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Jewinly, O., Zakaria, M.Z., Abdul-Rahman, S. (2022). Customer Segmentation Analysis on Retail Transaction Data. In: Alfred, R., Lim, Y. (eds) Proceedings of the 8th International Conference on Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 835. Springer, Singapore. https://doi.org/10.1007/978-981-16-8515-6_19
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DOI: https://doi.org/10.1007/978-981-16-8515-6_19
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