Over the years, the business sector has seen major changes in business growth. Businesses are setting new goals and therefore a competitive environment is developing in the business sector. As a result, many competing business sectors will not succeed. The reason is "Companies fail to teach their customers". Any company can achieve success and growth, but the analysis and understanding of customers and the market is where it fails. The answer to this problem is to understand what customer decomposition is. Customer decomposition is the segregation of the market into multiple groups of consumers who share similar characteristics. As customer decomposition is one the most important applications of unsupervised learning. Using clustering techniques, companies can identify the several segments of customers allowing them to target the potential user base. K-Means Clustering Algorithm along with RFM analysis is used in this project to segment the customers. Companies that deploy customer decomposition are under the notion that every customer has different requirements and require a specific marketing effort to address them appropriately. Companies aim to gain a deeper approach of the customer they are targeting. Therefore, their aim has to be specific and should be tailored to address the requirements of each and every individual customer. Furthermore, through the data collected, companies can gain a deeper understanding of customer preferences as well as the requirements for discovering valuable segments that would reap them maximum profit. This way, they can strategize their marketing techniques more efficiently and minimize the possibility of risk to their investment.