Ontologies, CRM, Data Mining: How to integrate?

Customer Relationship Management (CRM) systems rely in providing better information services to a population of end users/customers. A typical service of a CRM solution is to provide to a consumer access to a FAQ system to get advice on commonly occurring product problems. The CRM solution also typically supports direct communication with users, such as email communication, allowing the users to make requests for information via emails and, in some cases, receiving automatically generated responses to those emails. Providing information to users implies in access, and integration of diverse and distributed sources of information. On the other hand, the information collected from theses sources and from user's interactions should be of great value if integrated in a data mining process to maintain the cur-rent users, prospect new ones, and reaching some competitive advantage among competitors. Ontologies have been used to provide semantic integration and reasoning in decision support systems and knowledge management activities. The ontology captures the intrinsic conceptual structure of a domain. For any given domain, its ontology forms the heart of the knowledge representation. Our goal in this paper is to analyse some of the main opportunities to build a system that leverages the semantic content of ontologies in order to improve services provided by CRM solutions, and use this rich information environment to apply knowledge discovery techniques to reach a competitive advantage. The analysis in the section 4 follows one KDD life cycle tracing in each phase the potential use of ontologies in a CRM context. This analysis is a basis to guide further investigation, discussion and development in how to sieve these three rich fields, customer relationship management, knowledge discovery in databases, and ontology engineering to support successful business applications. Our on going research aims to analyze the application of ontology engineering in different real business scenarios, making use of the available business intelligence strategies to face CRM challenges. As a machine learning powerful resource our goal is to integrate KDD, ontology engineering and software agents using a business ontology-model as a communication platform and a common framework used by software agents to integrate different business applications, including those that bring additional e-commerce and decision support systems requirements. In the current stage of our research we are investigating CRM, and Supply Chain businesses aspects and how to represent and integrate these scenarios in the context of e-business using the semantic web cyberspace.