Analysis of Customer Segmentation Clustering Techniques

The growing customer base from all over the interconnected world, it has become vital for a company’s marketing team to know and thus understand their target customer base in order to build an effective and appropriate customer-company relation. A practical implementation of analysis of customer data to extract valuable conclusion for companies is customer segmentation clustering techniques. Unsupervised machine learning algorithms plays a vital role in segmenting customers from all around the world. Due to various clustering techniques available today, businesses need the most effective and accessible clustering techniques for their application. This research paper tackles the comparison and evaluative problem, where an automobile company’s unlabeled data is clustered into 3 predominant groups. It covenants with the questions that are previously conferred by the scholars and it spotlights the definition, methodology of market dissection and discerns which clustering technique between K-means and Hierarchical clustering is better with different number of cluster and different data size.