RFM Segmentation and K–Means Clustering of Skincare Product (Case study Scarlett) - Ayu Pradina, Dinda; Kurniawati, Yuni; Syawaldi Afwan, Ahmad; Heikal, Jerry (CV. Utility Project Solution, 2024)
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.