Analysis of Unsupervised Machine Learning Techniques for Customer Segmentation

Abstract

Customer relationship management (CRM) has played an important part in marketing tools for giving companies the model business intelligence which helps in raising, developing, and managing valuable long-term customer relationships with companies. Most organizations have understood the acceptance of customer relationship management and the application of computational capability to achieve cut-throat market aid. The current study expands the role of customer segmentation as a function of customer relationship management as well as the different framework for customer segmentation with the help of machine learning unsupervised techniques like clustering. The existing clustering methods of customer segmentation, and the vital techniques of K-means and hierarchical clustering, are considered in the current study. The righteousness and wickedness of the methods are pointed out during the study. Furthermore, as per the data collected a comparative analysis is done between hierarchical and K-means, and the results obtained from both the models were discussed. The difference arises when discussing the accuracy in resultant outcomes from the unsupervised learning technique.