Efficient Customer Segmentation Using Silhouette Based K-Means Algorithm

Customer segmentation is now essential for firms seeking to maintain and optimize revenue from consumers. The present research investigates the potential of utilizing behavioral variables such as expenses and income to successfully segment clients, beyond conventional demographic methods. The k-means clustering procedure, which is an unsupervised machine learning methodology, is utilized using the Elbow method and Silhouette score to ascertain the most suitable number of clusters. A Python application, with basic scaling, examines a dataset from a retail mall to visually represent similarities among customers and categorize them into clusters. The project aims to segment customers based on their purchase behavior using the silhouette-based K-means algorithm. The seaborn library will be used for implementation. The objective is to create a resilient consumer segmentation approach that assists organizations in comprehending target markets and enhancing marketing strategies suitably. By employing this algorithm, organizations acquire knowledge about client behavior, facilitating customized marketing campaigns and enhanced income generating.