Analyzing customer sentiments using K-means algorithm

  • Affiliations:

    Faculty of Economics and Business Administration, Hanoi University of Mining and Geology, Vietnam

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  • Received: 18th-Aug-2020
  • Revised: 24th-Sept-2020
  • Accepted: 31st-Oct-2020
  • Online: 31st-Oct-2020
Pages: 145 - 150
Views: 14786
Downloads: 2342
Rating: 1.0, Total rating: 229
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Abstract:

Customer segmentation is the process of dividing customers based on common characteristics such as their behavior, buying habits and service usage,... so that companies can market for each group customers more effectively and appropriately. The paper analyzes customer cluster segmentation via the K-Means clustering methods of a business sector. The research was conducted on 272 customers with characteristics of age, income and expense score. The research results are divided into 2 target customer clusters, promising to help care and marketing customers more effectively; Help business units to have appropriate marketing strategies to reduce costs and increase efficiency.

How to Cite
Pham, T.Kien, Nguyen, T.Duc, Le, C.Van and Nguyen, T.Van 2020. Analyzing customer sentiments using K-means algorithm (in Vietnamese). Journal of Mining and Earth Sciences. 61, 5 (Oct, 2020), 145-150. DOI:https://doi.org/10.46326/JMES.KTQTKD2020.19.
References

Chapman, C., and Feit, E. M, (2019). R for marketing research and analytics. New York, NY: Springer.

Chen, D., Sain, S. L., and Guo, K, (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing and Customer Strategy Management, 19(3), 197-208.

Khajvand, M., and Tarokh, M. J, (2011). Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327-1332.

Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., and Lichtendahl Jr, K. C, (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley and Sons.

Zakrzewska, D., and Murlewski, J, (2005). Clustering algorithms for bank customer segmentation. In 5th International Conference on Intelligent Systems Design and Applications (ISDA’05) pp. 197-202. IEEE.

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