A Study of Customer Segmentation Based on RFM Analysis and K-Means
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-4071-4_27
Reference21 articles.
1. Rizki B, Ginasta NG, Tamrin MA, Rahman A (2020) Customer loyality segmentation on point of sale system using recency-frequency-monetary (RFM) and K-Means. J Online Informatika 5(2):130. https://doi.org/10.15575/join.v5i2.511.; Author F, Author S (2016) Title of a proceedings paper. In: Editor F, Editor S (eds) CONFERENCE 2016 LNCS, vol 9999, Springer, Heidelberg, pp 1–13
2. Hu Z, Li Y, Wang J (2021) The use of machine learning models in customer segmentation on airline, retail and electricity markets
3. Firdaus U, Utama DN (2021) Development of bank’s customer segmentation model based on rfm+b approach. ICIC Express Letters, Part B: Appl. 12(1):17–26. https://doi.org/10.24507/icicelb.12.01.17
4. Mensouri D, Azmani A, Azmani M (2022) K-means customers clustering by their RFMT and score satisfaction analysis. Int J Adv Comput Sci Appl (IJACSA) 13(6). https://doi.org/10.14569/IJACSA.2022.0130658
5. Kumar SJ, Oommen Philip A (2022) Achieving market segmentation from B2B insurance client data using RFM & K-Means Algorithm. In: SPICES 2022 - IEEE international conference on signal processing, informatics, communication and energy systems, pp 463–469. https://doi.org/10.1109/SPICES52834.2022.9774051
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