RFM Segmentation and K–Means Clustering of Skincare Product (Case study Scarlett)

Authors

  • Dinda Ayu Pradina Universitas Bakrie
  • Yuni Kurniawati Universitas Bakrie
  • Ahmad Syawaldi Afwan Universitas Bakrie
  • Jerry Heikal Universitas Bakrie

DOI:

https://doi.org/10.55338/saintek.v6i2.3644

Keywords:

RFM Segmentation, Skincare Product, Marketing Mix

Abstract

This analysis focuses on at-risk consumers in the skincare market using the RFM segmentation method. This method involves collecting consumer purchase data, including the recency of their last purchase, the frequency of their purchases, and the amount spent. The data is collected through a customer relationship management (CRM) system that records consumer purchase activities. Analytical tools like SPSS or Python with libraries such as pandas and scikit-learn are used for data processing and cluster analysis.The segmentation process involves several steps: collecting data from the CRM, cleaning and normalizing the data, applying the RFM algorithm to group consumers based on their RFM scores, and analyzing the segmentation results to identify the unique characteristics of each segment. The findings reveal clear preferences for skincare products among this segment, characterized by recent purchases within the last three months, a purchase frequency of about twice during that period, and an average spending of Rp. 500,000.Classified as top-tier buyers, these at-risk consumers exhibit unique characteristics—primarily females, working in the private sector, living outside Jakarta, with an average age of 31 years and an income of Rp. 15 million. In response to these insights, a comprehensive 7P strategy on TikTok is proposed to effectively engage at-risk buyers. This strategy includes customized product offerings, competitive pricing, strategic platform utilization, influencer collaborations, efficient purchasing processes, and tangible proof of product effectiveness. Regular evaluations are emphasized to ensure the adaptability and continuous effectiveness of the strategy in meeting the evolving needs of at-risk buyers, ultimately fostering brand loyalty and satisfaction in the competitive skincare market.

Downloads

Download data is not yet available.

References

R. Y. Du, O. Netzer, D. A. Schweidel, and D. Mitra, “Capturing marketing information to fuel growth,” Journal of Marketing, vol. 85, no. 1, pp. 163–183, 2021.

C. Fornell, F. V. Morgeson III, G. T. M. Hult, and D. VanAmburg, The reign of the customer: Customer-centric approaches to improving satisfaction. Springer Nature, 2020.

O. Baiyewu, “The impact of customer relationship management on organizational performance: a case study of Dangote Flour Mill, Kano State,” 2022.

R. Y. Du, O. Netzer, D. A. Schweidel, and D. Mitra, “Capturing marketing information to fuel growth,” Journal of Marketing, vol. 85, no. 1, pp. 163–183, 2021.

L. Lin, Z. Guo, and C. Zhou, “Failure to maintain customers: antecedents and consequences of service downgrades,” Journal of Service Theory and Practice, vol. 33, no. 3, pp. 387–411, 2023.

M. Sağlam and S. El Montaser, “The effect of customer relationship marketing in customer retention and customer acquisition,” International Journal of Commerce and Finance, vol. 7, no. 1, pp. 191–201, 2021.

P. Anitha and M. M. Patil, “RFM model for customer purchase behavior using K-Means algorithm,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 5, pp. 1785–1792, 2022.

S. A. Kabut and N. A. Windasari, “A Predictive CRM Analytics Framework For Merchant Retention: Applying RFM Segmentation, Customer Profiling, and Behavioral Analytics In The B2B Payment Gateway Company,” Return: Study of Management, Economic and Bussines, vol. 3, no. 6, pp. 409–428, 2024.

A. Suryakrisna and C. E. Susanti, “The Influence of Attractiveness and Expertise of Influencers towards Purchase Intention Mediated by Bran Image in the Erigo Clothing Line Brand in Indonesia,” Technium Soc. Sci. J., vol. 53, p. 259, 2024.

R. Shirole, L. Salokhe, and S. Jadhav, “Customer segmentation using rfm model and k-means clustering,” Int. J. Sci. Res. Sci. Technol, vol. 8, pp. 591–597, 2021.

J. Wu et al., “[Retracted] An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K‐Means Algorithm,” Mathematical Problems in Engineering, vol. 2020, no. 1, p. 8884227, 2020.

S. Das and J. Nayak, “Customer segmentation via data mining techniques: state-of-the-art review,” Computational Intelligence in Data Mining: Proceedings of ICCIDM 2021, pp. 489–507, 2022.

E. Ernawati, S. S. K. Baharin, and F. Kasmin, “A review of data mining methods in RFM-based customer segmentation,” in Journal of Physics: Conference Series, IOP Publishing, 2021, p. 012085.

A. J. Christy, A. Umamakeswari, L. Priyatharsini, and A. Neyaa, “RFM ranking–An effective approach to customer segmentation,” Journal of King Saud University-Computer and Information Sciences, vol. 33, no. 10, pp. 1251–1257, 2021.

J. Pallant, S. Sands, and I. Karpen, “Product customization: A profile of consumer demand,” Journal of Retailing and Consumer Services, vol. 54, p. 102030, 2020.

N. L. Rane, A. Achari, and S. P. Choudhary, “Enhancing customer loyalty through quality of service: Effective strategies to improve customer satisfaction, experience, relationship, and engagement,” International Research Journal of Modernization in Engineering Technology and Science, vol. 5, no. 5, pp. 427–452, 2023.

P. H. Kjekshus and C. Finseth, “Understanding Underlying Gratifications Behind TikTok Use: A Strategic Marketing Perspective,” Handelshøyskolen BI, 2023.

Downloads

Published

2024-07-15

How to Cite

Ayu Pradina, D. ., Kurniawati, Y., Syawaldi Afwan, A. ., & Heikal, J. . (2024). RFM Segmentation and K–Means Clustering of Skincare Product (Case study Scarlett). Jurnal Sains Dan Teknologi, 6(2), 213-216. https://doi.org/10.55338/saintek.v6i2.3644