CRM Customer Value based on Constrained Sequential Pattern Mining

Abstract

The role of data mining has become increasingly important for an organization that has large databases of information on customers. Customer Relationship Management (CRM) systems are implemented to identify the most profitable customers and manage the relationship of company with them. Intelligent data mining tools and techniques are used as backbone to the whole CRM initiative taken by the companies. Data mining tools search the data warehouse maintained by the companies and predict the hidden patterns and present them in form of a model. Strategic decisions about the customers can be taken based on the outcomes of these models. The data mining researchers have presented various mining algorithms to extract patterns in data for successful CRM approach. These approaches are however facing several problems like they are not business-focused and often results in enormous size of data after applying mining approaches. They have no relevant mechanism to provide guidance for focusing on specific category of customers for business profitability. In this article, the requirements of sequential pattern mining process for CRM environment is described, and then a novel constraint guided model for knowledge discovery process is proposed. We have suggested even how the selection of appropriate constraints can be made from the perspective of customer value analysis.