Comparing Supervised and Unsupervised Learning Techniques for Customer Segmentation in Marketing

The research contrasts marketing client segmentation strategies that utilize supervised and unsupervised learning techniques. The initial stage entails a comprehensive comparison and contrast of the two methodologies, with an emphasis on their respective strengths, weaknesses, and common applications in consumer segmentation. Hierarchical clustering is among the methodologies that are investigated. In the comparative research, the effectiveness of various methodologies is evaluated using a variety of metrics, including precision, scalability, interpretability, and simplicity of implementation. The research investigates the potential challenges associated with the use of supervised and unsupervised learning approaches for consumer segmentation, including data quality, feature selection, and algorithm complexity. The research offers recommendations for selecting the most effective approach in light of the constraints of a marketing endeavor. This comparative research will be a valuable resource for data scientists and marketers who are interested in enhancing client segmentation strategies through the use of machine learning.