Analysis of Shopee App User Behavior Patterns Using the K-Means Algorithm and RFM Model (Case Study: ZR Store)
DOI:
https://doi.org/10.69916/jkbti.v5i3.622Keywords:
K-means, RFM, ZR Store, Loyal Customer, Behavioral PatternAbstract
The rapid growth of e-commerce in Indonesia has led to an increase in transaction volumes and user activity on online shopping platforms, such as the Shopee app. Large-scale transaction data can be leveraged to understand user behavior patterns, enabling companies to formulate more effective and targeted marketing strategies. This study aims to analyze the behavior patterns of Shopee app users using the Recency, Frequency, Monetary (RFM) method and the K-Means clustering algorithm. The RFM method is employed to assess customer characteristics based on the time of the last transaction (Recency), transaction frequency (Frequency), and total customer expenditure (Monetary). Subsequently, the K-Means algorithm is applied to segment users based on similarities in their transaction behavior. The dataset comprises 200 customers, with attributes including customer code, transaction date, age, gender, quantity of items purchased, shipping type, product category, and payment method. Using the K-Means algorithm, customers were grouped into three clusters: C1 (Loyal Customers), C2 (Less Loyal Customers), and C3 (New Customers). The results indicate that 90 customers fell into category C1, 39 into category C2, and 71 into category C3. Regarding rankings, the maximum rank (RRank_max) was 50 with an obtained rank value of 12.0; for Frequency (Frank), the value was 2 with a maximum rank (Frank_max) of 12.29; and an Mrank of 48.8 was obtained.
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