Consumer Segmentation of Emina Cosmetics Optimal and Relevant Approach of RFM+Lifetime Analysis

Authors

  • Nabil Rakha Dwitya Universitas Multimedia Nusantara, Tangerang, Indonesia
  • Wirawan Istiono Universitas Multimedia Nusantara

DOI:

https://doi.org/10.58905/saga.v1i3.171

Keywords:

consumer segmentation, K-Means algorithm, RFM Analysis, RFML Analysis

Abstract

Buyers are the most crucial entities for companies selling products, including PT Paragon Technology and Innovation. PT Paragon Technology and Innovation is a cosmetics company that oversees well-known brands such as Wardah, Emina, MakeOver, and Kahf. It is essential for this company to understand the characteristics of its buyers who purchase their products, and one way to achieve this is by conducting consumer segmentation. This consumer segmentation is carried out on customers who have purchased Emina products from March 2021 to March 2023, using three types of RFM analysis approaches: vanilla RFM analysis, RFM+Lifetime, and RFM/Lifetime, which are then grouped using the K-Means algorithm. Through the implementation of this consumer segmentation, the company can gain a deeper understanding of its buyers' behavior towards the products they offer, thereby enhancing business processes and marketing efforts. The consumer segmentation has been completed with the finding that out of the three types of RFM analysis approaches employed for consumer segmentation, the RFM+Lifetime approach is the most effective and relevant one, resulting in four categories: Make Up, Face Care, Others, and General. The Make Up category further consists of five segments, while each of the other categories contains four segments.

References

M. R. Sofi, I. Bashir, M. A. Parry, and A. Dar, “The effect of customer relationship management (CRM) dimensions on hotel customer’s satisfaction in Kashmir,” International Journal of Tourism Cities, vol. 6, no. 3, pp. 601–620, 2020, doi: 10.1108/IJTC-06-2019-0075.

A. Lubis, R. Dalimunthe, Y. Absah, and B. K. Fawzeea, “The Influence of Customer Relationship Management (CRM) Indicators on Customer Loyalty of Sharia Based Banking System,” GATR Journal of Management and Marketing Review, vol. 5, no. 1, pp. 84–92, 2020, doi: 10.35609/jmmr.2020.5.1(8).

D. Ngelyaratan and D. Soediantono, “Customer Relationship Management (CRM) and Recommendation for Implementation in the Defense Industry : A Literature Review Customer Relationship Management ( CRM ) dan Usulan Penerapannya Pada Industri Pertahanan : A Literature Review,” Journal of Industrial Engineering and Management Research, vol. 3, no. 3, pp. 17–34, 2022, doi: 10.7777/jiemar.v3i3.279.

R. Thakur and L. Workman, “Customer portfolio management (CPM) for improved customer relationship management (CRM): Are your customers platinum, gold, silver, or bronze?,” Journal of Business Research, vol. 69, no. 10, pp. 4095–4102, 2016, doi: 10.1016/j.jbusres.2016.03.042.

A. Naim, “Role of Information Systems in Customer Relationship Management,” International Journal of Intelligent Communication, Computing, and Networks, vol. 02, no. 03, pp. 34–45, 2021, doi: 10.51735/ijiccn/001/37.

A. A. Salameh, A. Hatamleh, M. S. Azim, and A. G. Kanaan, “Customer oriented determinants of e-crm success factors,” Uncertain Supply Chain Management, vol. 8, no. 4, pp. 713–720, 2020, doi: 10.5267/j.uscm.2020.8.001.

S. I. Wu and C. L. Lu, “The relationship between CRM, RM, and business performance: A study of the hotel industry in Taiwan,” International Journal of Hospitality Management, vol. 31, no. 1, pp. 276–285, 2012, doi: 10.1016/j.ijhm.2011.06.012.

S. Hansun, “FX forecasting using B-WEMA: Variant of Brown’s Double Exponential Smoothing,” 2016 International Conference on Informatics and Computing, ICIC 2016, no. Icic, pp. 262–266, 2017, doi: 10.1109/IAC.2016.7905726.

J. Wu et al., “An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K -Means Algorithm,” Mathematical Problems in Engineering, vol. 2020, no. November 2017, 2020, doi: 10.1155/2020/8884227.

O. Doğan, E. Ayçin, and Z. A. Bulut, “Customer Segmentation by Using RFM Model and Clustering Methods: A Case Study in Retail Industry,” International Journal of Contemporary Economics and Administrative Sciences, vol. 8, no. 1, pp. 1–19, 2018, [Online]. Available: www.ijceas.com

J. Wei, S. Lin, and H. Wu, “A review of the application of RFM model,” African Journal of Business Management, vol. 4, no. 19, pp. 4199–4206, 2010.

S. Hansun, A. Wicaksana, and M. B. Kristanda, “Prediction of jakarta city air quality index: Modified double exponential smoothing approaches,” International Journal of Innovative Computing, Information and Control, vol. 17, no. 4, pp. 1363–1371, 2021, doi: 10.24507/ijicic.17.04.1363.

S. Monalisa, P. Nadya, and R. Novita, “Analysis for customer lifetime value categorization with RFM model,” Procedia Computer Science, vol. 161, pp. 834–840, 2019, doi: 10.1016/j.procs.2019.11.190.

D. Chen, S. L. Sain, and K. Guo, “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, vol. 19, no. 3, pp. 197–208, 2012, doi: 10.1057/dbm.2012.17.

K. P. Sinaga and M. S. Yang, “Unsupervised K-means clustering algorithm,” IEEE Access, vol. 8, pp. 80716–80727, 2020, doi: 10.1109/ACCESS.2020.2988796.

E. Hot and V. Popović-Bugarin, “Soil data clustering by using K-means and fuzzy K-means algorithm,” Telfor Journal, vol. 8, no. 1, pp. 56–61, 2016, doi: 10.5937/telfor1601056H.

M. Ahmed, R. Seraj, and S. M. S. Islam, “The k-means algorithm: A comprehensive survey and performance evaluation,” Electronics (Switzerland), vol. 9, no. 8, pp. 1–12, 2020, doi: 10.3390/electronics9081295.

R. V. Singh and M. P. S. Bhatia, “Data clustering with modified K-means algorithm,” International Conference on Recent Trends in Information Technology, ICRTIT 2011, pp. 717–721, 2011, doi: 10.1109/ICRTIT.2011.5972376.

E. Aytaç, “Unsupervised learning approach in defining the similarity of catchments: Hydrological response unit based k-means clustering, a demonstration on Western Black Sea Region of Turkey,” International Soil and Water Conservation Research, vol. 8, no. 3, pp. 321–331, 2020, doi: 10.1016/j.iswcr.2020.05.002.

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Published

22-08-2023

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