The practical approach in Customers segmentation by using the K-Means Algorithm

Abstract

The partnership between businesses and consumers is increasingly crucial with technological growth. It is necessary to manage this relationship for the company's future growth. The communication mechanism between companies and customers is called Customer Relationship Management (CRM). The CRM plays a significant role in the business sector. Moreover businesses may classify client attitudes, characteristics, etc following the appropriate CRM method. By using this knowledge, companies can determine which consumers are the most profitable. This can be done with the method called customer segmentation. The Customer segmentation is a breakdown of the broad database of customers into subparts. The subparts members have some common features, although, in other subparts, these features are different. Most scientists consider various ways of segmenting customers with multiple clustering algorithms in different fields with the support of data mining. In the data analytical field, there are many techniques associated with the segmentation process. To order to identify the target consumer audience, companies use traditional market segmentation. It ensures that marketers will target their marketing strategies on the most likely to attract customers. Also, businesses may adopt more effective marketing strategies following the successful customer segmentation process and thereby reduce the uncertainty associated with an investment. Here, the k-mean was used as an algorithm for the segmentation process to segment the customers. In this study, determine the six clusters base on the annual income and spending score. Also, identify six clusters according to the two principal components. Following the successful clustering process, lead the organizations to make accurate decisions and organizations can deliver the new product and services, and also can do some changes for existing product services according to the customer requirements through the identification of customers correctly.