Customer Segmentation Using K-Means Clustering

Customer segmentation is an important concept in today’s business world that allows companies to better understand and target different groups in their customer base. By identifying these groups based on their characteristics and behaviors, companies can improve their business, increase customer satisfaction, and increase revenue. This study uses an integrative approach to benefit consumers by using rich data from marketing campaigns. The database contains 2,240 records that include detailed customer information (age, education, income, and marital status) and purchasing behavior across a variety of products. To determine user values in this study, the K-Means cluster is used and aggregated. Preliminary measures were made to assess the data and eliminate missing values in order to ensure data quality. In order to make sure of data quality, preliminary steps were taken to remove missing values and evaluate the data. This also uses principal component analysis (PCA) to reduce the outliers and not important data from the dataset, thus increasing the efficiency of the computation and interpretation of the clustering process. The best categories are determined using the elbow technique, which is made available to a number of clients with different buying patterns and demographics. Businesses can use the data from this work to target particular clients in their marketing campaigns. This study shows how customer segmentation, customer relationship building, and marketing campaign direction may all benefit from their integration.