Devi, Rizky Feliana
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Customer Segmentation Based on RFM Analysis as the Basis of Marketing Strategy Case Study of PT Pertiwi Agung Pharmaceutical Industry (LANDSON) Devi, Rizky Feliana; Siswanto, Fajar Hartanto; Azkia, Nayla; Heikal, Jerry
BUDGETING : Journal of Business, Management and Accounting Vol 5 No 2 (2024): BUDGETING : Journal of Business, Management and Accounting
Publisher : Institut Penelitian Matematika Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/budgeting.v5i2.8994

Abstract

The Pharmaceutical Company is a company that has quite large raw material import activities and has many benefits for society and institutions such as hospitals. Pharmaceutical companies play an important role in improving the quality of life of the human population in modern times because, in the field of marketing, pharmaceutical companies face increasing sales performance and profits, as well as maintaining customer loyalty. Pharmacy retail customers usually make drug purchases influenced by the selling price and suitability factors (suggestions) for certain drug brands. Based on these conditions, drug purchasing patterns for the Indonesian people become unpredictable, and it is difficult to increase sales and profits. One effort that pharmaceutical business players can make is to carry out sales promotions based on customer segmentation. Customer segmentation in pharmaceutical companies can be done using clustered data mining analysis methods, such as modified Recency Frequency Monetary (RFM). This method allows companies to group customers based on purchasing patterns of pharmaceutical products, thereby allowing companies to prioritize energy and resources to different segments. After the scoring and data processing process, the number of customers for each RFM Score is obtained, then the Monetary group is segmented which is divided into 4 (four) parts, namely Best Customers by quantity (36), Loyal Customers by quantity (188), Potential Customers by quantity (34) and Lost Customers by quantity (61). Then we continue to map it into only 3 (three) parts, namely Best Customers, Loyal Customers, and Potential Customers using blue as a sign to see the score range. From the results of dividing the 3 (three) group segmentations, the Loyal Customer Score segmentation is greater in quantity (188) so the blue color is darker than the others, which shows that the more customers spend their money. Of the 3 (three) customer segmentation sections, we put all of them into the Best Customer category, because they have introduced new products or products they have not purchased. By using RFM analysis, you can quickly find out customer targets that will be prioritized in carrying out marketing, campaigns, promotions, and rewards using digital channels and direct customer relations. Keywords: Farmasi Company, Group Segmentation, Recency Frequency Monetary (RFM).