Uncovering Smartphone Brand Strategies through Specification-Based Clustering and Classification - Karim, Abdul; Ernawati, Andi (Buletin Ilmiah Informatika Teknologi, 2025)
In an increasingly saturated smartphone market, brand differentiation through technical specifications has become a core strategy for attracting diverse consumer segments. This study proposes a machine learning approach to uncover underlying brand strategies by leveraging smartphone specifications and market pricing across multiple regions. We utilize unsupervised clustering algorithms (K-Means, DBSCAN) to segment devices based on technical features, followed by supervised classification models (Random Forest, XGBoost) to identify and interpret brand-driven design strategies. The dataset comprises smartphones released in 2024–2025, including attributes such as RAM, camera specifications, processor type, battery capacity, and launch prices in Pakistan, India, China, USA, and Dubai. Our findings reveal distinct clusters that align with different pricing tiers and show clear brand positioning patterns. Feature importance analysis using SHAP highlights battery capacity, screen size, and processor type as critical variables influencing brand classification. This study provides valuable insights for both manufacturers and consumers in understanding competitive product strategies within the global smartphone market.