Customer personality analysis using machine learning techniques

Abstract

To evaluate customer data and identify common patterns and qualities among a group of consumers, unsupervised learning is a machine learning technique that can be used. Companies may build customer groups with different traits by utilizing clustering algorithms to group like consumers. This enables them to target each segment with their marketing and sales initiatives. The study investigates customer segmentation and profiling using machine learning techniques, which has been referred to as customer personality analysis in this mini-thesis, adopting a quantitative research methodology using unsupervised machine learning techniques on a retail marketing dataset. Unsupervised learning algorithms are effective tools for businesses wanting to better understand and service their consumers since they can be trained on vast volumes of datasets. Customers have traditionally been analyzed manually by either marketing or sales teams; they would utilize their knowledge and experience to spot recurring trends and patterns among customers. The development of data mining and machine learning tools, however, has made it feasible to automate this procedure by utilizing algorithms that can scan massive volumes of data and find common patterns and qualities among clients. The researcher applied K-Means and Agglomerative Clustering, after pre-processing the data, to identify distinct customer segments. The researcher’s SHAP analysis showed that the customer's monetary expenditure on wine products is a significant contributor or determining factor to the campaigns they are inclined to accept. The results demonstrate the viability of these methods for targeted marketing. The results reveal segments characterized by age, education, and purchasing channels. The researcher recommends that businesses adopt these analytical techniques because these will lead to improved customer understanding