Analisis Perubahan Segmentasi Konsumen secara Longitudinal Menggunakan Time-Based Clustering pada Data Produk NVIDIA: Studi Kasus Data Shopee
DOI:
https://doi.org/10.63447/jimik.v7i3.1990Keywords:
Time-Based Clustering, Longitudinal Consumer Segmentation, RFM-T, K-Means, Nvidia, Consumer BehaviorAbstract
This study analyzes longitudinal changes in consumer segmentation on Nvidia product data using time-based clustering method. With the increasing demand for Nvidia products in the global market, particularly for gaming GPUs and AI applications, understanding changes in consumer behavior over time becomes crucial for marketing strategy. The research utilizes Nvidia consumer transaction data spanning 5 years (2021-2025) with RFM-T (Recency, Frequency, Monetary, Time) approach enriched with Interpurchase Time (IPT). The clustering method employed is K-Means algorithm with validation using silhouette score, Davis-Bouldin index, and Calinski-Harabasz index. The research results indicate the formation of 4 main consumer segments experiencing dynamic changes: (1) Loyal AI Enthusiasts, (2) Gaming Power Users, (3) Potential Upgraders, and (4) Churn Risk Customers. This longitudinal segmentation reveals a pattern of shift from gaming-centric to AI-centric preferences alongside AI technology development. This study contributes to the development of more targeted CRM strategies and retention strategies for technology hardware companies.
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