A Strategy to Identify Loyalty Using Elbow Curve Method for Customer Segmentation

Abstract

Customer segmentation is a very strong way to identify unsatisfied customers as well as loyal customers. It has become very crucial and mandatory for businesses to understand the customers and segment them according to their needs and desires. Many businesses struggle to manage cancellations and delays. The high number of cancellations is always a challenge for business houses. Every instance of cancellation can be a learning experience and an opportunity to understand the customers better. These insights can help businesses to improve their products and services. Now, as the usage of online gadgets has increased among customers and smart technologies are used in designing web applications, more data is available to understand customer behavior and predict their buying patterns. In the current era, customers are exposed to many online applications which pose tough competition among service providers. Businesses spend a lot to attract new customers. On the other side, retaining loyal customers is as crucial as identifying new customers. Identifying loyal customers helps to create a personalized approach that makes them feel valued. Understanding current customer priorities are more important than identifying the new customer. Machine learning is an effective technique to help segment loyal customers into actionable customers. This paper outlines the use of the K-means algorithm to identify loyal and prospective customers along with strategies to lower the cancellation rate. The current study uses the elbow curve method to identify the optimum number of clusters into which the customers could be segmented. This study will help businesses to seize new opportunities and gain customers for life.