Business Strategies, Competitive Advantages, Data-driven K-means Algorithm, K-means Algorithm, Pre-processing Techniques, Strategic Planning

In today’s dynamic business environment, Machine Learning (ML) or algorithm-based, data-driven models are essential for competitive advantage and strategic planning. This study aims to demonstrate the effectiveness of ML models - specifically the standard K-means clustering algorithm in identifying patterns that can inform strategic business decisions. A synthetic dataset was generated to simulate real-world business data scenarios, and the K-means algorithm was applied both with and without data pre-processing techniques such as scaling. The results indicate that although K-means remains a powerful and widely applicable clustering method, its performance is significantly improved by proper data scaling and identification of the optimal number of clusters. The findings of this study offer valuable insight how to develop business strategies over complex business scenarios.

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