Data mining techniques in customer relationship management classification analysis

The presence in the turbulent competition market has pushed organizations to adopt new approaches to keeping current customers, identifying and attracting new customers. Nine hundred articles were identified and reviewed for their direct relevance to applying data mining techniques to CRM. Eighty-seven articles were subsequently selected, reviewed and classified seven data mining functions the review and classification process was independently verified. Findings of this paper indicate that the research area of customer retention received most research attention. Of these, most are related to one-to- one marketing and loyalty programs respectively. On the other hand, classification and association models are the two commonly used models for data mining in CRM. Our analysis provides to guide future research and facilitate knowledge accumulation and creation concerning the application of data mining techniques in CRM.In these types of companies, due to the great variety of customers, the diversity of business environments, the variability in the complexity of services and the needs of customers and the diversity in access to their information، use of a modified method that will help us discover hidden relationships and knowledge between these topics. For this reason, it is very important to use data analytics to classify, discover, and generate relevant information from customers across a large body of data from companies and organizations. To this end, the use of data analysis techniques and methods will give decision makers the ability to know about the future of their system and develop targeted plans for their company s future. In this paper, after introducing customer relationship management, data analysis techniques have been investigated as a tool for discovering knowledge to responsiveness to business needs and customer relationship management strategies. In addition, a review of various applications in customer relationship management has been done in various industries including retail, banking and telecommunications. Data mining methods that provide the ability to discovering knowledge from massive data, they will learn how to invest on valuable customers and, of course, identify them, and vice versa, identify customers who will have less profit. They will figure out how to deal with them. Despite the importance of data mining techniques to customer relationship management (CRM), there is a lack of a comprehensive literature review and a classification scheme for it. This is the first identifiable academic literature review of the application of data mining techniques to CRM. It provides an academic database of literature between the periods of proposes a classification scheme to classify the articles. After that, data analysis methods were used for three different applications as case studies, which included Investigating the Effect of a Commercial Title on a Customer Shopping Cart in the Study of Citizen Store Data Service prediction on future customer demand in the use of the Internet Check customer retrieval from smart Internet using value of customer s lifetime. Various data analysis techniques such as Apriori s artificial neural network and Decision tree have been used in studies that resulted in the discovery of the rules governing the massive amount of data in these applications

Keywords:

Customer Relationship Management Strategies , relationship management , Literature review , Classification , Data analysis , Attracting Customers.