Data mining : a solution for customer relationship management (CRM) in retail businesses

Abstract

Many organisations today face the problem of having extremely large amounts of data as a result of their day-to-day transactions. Due to its complexity, such data can no longer be managed using traditional database techniques. The retail industry is one of the many industries which face such a data overload problem because they are in possession of a lot of customer data. Among this data, is hidden information which a company can make use of to better understand the needs of their customers. In doing so, their relationship with the customer is improved. This process is better known as Customer Relationship Management (CRM). Customer data can be better managed using the concept of data mining, which is the process of uncovering patterns and significant relationships in large sets of data. Data mining technology can be used in the retail industry to allow management to make better informed tactical decisions. This Final Year Project includes the research and analysis of currently available CRM techniques and data mining algorithms which can be applied to the retail sector to enhance a company's CRM. It also focuses on the possible problems that may be encountered when using dirty real-world data. A simple prototype which reflects the results of the research carried out was also developed. The objective is to demonstrate how the large amounts of data which retailers possess can be turned into useful information about their customers. The primary results of the experiments carried out indicate that data mining techniques are in fact useful for providing insight about a retailer's consumers. These techniques include classification, clustering and association methods. The results also show the importance of the quality of data required to achieve better results.