Analysis of Recency Frequency Monetary (RFM) Using Python and Matplotlib in Optimizing Marketing Strategies at Dhiza Petshop Retail Store in Tanjung Selor - Eric Raharjo; Tinik Sugiati (Dinasti Publisher, 2025)
Petshops have been rapidly growing due to increased public awareness of pet care and food needs. The rising trend of pet adoption has fueled the expansion of petshops in various regions. The competition in the pet retail industry demands more targeted marketing strategies to enhance customer loyalty. One effective method is RFM (Recency, Frequency, Monetary) analysis, which allows for customer segmentation based on their shopping behavior. This study aims to analyze the customer behavior patterns of Dhiza Petshop Tanjung Selor using the RFM method with Python and Matplotlib, based on customer transaction data from December 21, 2024, to January 20, 2025, using 11 segments: Champions, Loyal Customers, Potential Loyalists, Recent Buyers, Promising, Need Attention, About to Sleep, Cannot Lose Them, At Risk, Hibernating, and Lost. The results show that the customer behavior patterns at Dhiza Petshop Tanjung Selor are dominated by customers with recent purchasing activity, while the number of loyal customers remains limited, indicating the need for retention and reactivation strategies to strengthen long-term relationships. The application of RFM analysis using Python and Matplotlib allows for efficient segmentation of customers, resulting in improved marketing strategies. The percentages for the 11 customer segments at Dhiza Petshop Tanjung Selor are: Champion (0.1%), Loyal (0%), Potential Loyalist (0.9%), Recent Buyers (72.6%), Promising (4.4%), Need Attention (0.1%), About to Sleep (0.2%), Cannot Lose Them (0%), At Risk (0%), Hibernating (12.1%), and Lost (9.6%).