Customer Segmentation using Mini Batch K-Means Clustering Algorithm

Today, especially in this era of data, everything is becoming more mechanized. Data is the new electricity of the globe. This knowledge may help a lot of businesses in their marketing endeavors. Every business wanted to flourish and outperform its rivals, so they spent in product development and used novel ideas, but only to retain and grow their consumer base. In today’s business environment, consumers are separated. They can help them make money by analyzing the actions and buying habits of their customers. Customer segmentation is the process of grouping a company’s customers according to their shared characteristics. Client segmentation is the process of figuring out how to communicate with customers in various categories to increase the value of each client to the business. To segment customers, unsupervised machine learning is employed. The main objective of this research study is to cluster consumers into different groups based on their behavior patterns. There are several algorithms that may segment customers depending on their behavior and other criteria. Five clustering algorithms were examined in this study, and the best one was chosen for more investigation.