Comparing Supervised and Unsupervised Learning Technologies for Customer Segmentation in Marketing

This study delves into the marketing practice of client segmentation by use of supervised and unsupervised learning tools. We investigate the efficacy of these two diverse methods and the ramifications they have in practice. We use supervised models like Random Forest and Support Vector Machine, as well as unsupervised clustering techniques like K-Means and Hierarchical Clustering, to conduct a hypothetical study on a dataset of 1,000 consumers. The results show the benefits and drawbacks of both methods, with supervised learning's accuracy in client segmentation being highlighted, and unsupervised learning's adaptability. This contrast shows how crucial it is to tailor your research approach to achieve your unique marketing goals. Implications for precise targeting, flexibility, and resource optimization in marketing tactics are also highlighted in the debate. This study helps to bridge the gap between machine learning theory and marketing practice, paving the way for better company results and happier customers. This study lays the groundwork for using machine learning strategically in consumer segmentation, which will become more important as the marketing environment develops in the future.