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DEPARTAMENTO DE CIÊNCIAS DE GESTÃO DISCIPLINA: MARKETING INFORMATION AND DECISION SUPPORT SYSTEMS COORDENADOR: Prof. Doutor Paulo Rita ÁREA CIENTÍFICA: MARKETING LICENCIATURA EM MARKETING ANO LECTIVO 2006/2007 1. LEARNING OBJECTIVES Upon successful completion of the course, students will be able to: 1. Understand how information and decision support systems can assist marketers in their work, and recognize the conceptual foundations of decision making in marketing 2. Describe the concepts of modeling and how management support systems models interact with data and the user, and explain the importance and use of data mart for marketing management 3. Describe business intelligence/business analytics and their importance to organizations, and discuss the factors that lead to marketing decision support systems success or failure 4. Understand the concepts of groupwork, communication and collaboration, and see the relationships among enterprise information systems, data warehouses, online analytical processing and data mining 5. Describe the role of knowledge management in marketing activities, and explain the architecture, benefits and limitations of rule-based marketing expert systems 6. Understand the knowledge engineering process and the different approaches for knowledge acquisition, and learn the concepts and applications of case-based systems, artificial neural networks, genetic algorithms and fuzzy set theories in marketing management 2. PROGRAM 1. Management Support Systems in Marketing 2. Marketing Decision Making Systems, Modeling and Support 3. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analysis and Visualization 4. Marketing Decision Support Systems Development 5. Collaborative Computing Techniques: Group Support Systems 6. Enterprise Information Systems and Knowledge Management 7. Artificial Intelligence and Marketing Expert Systems: Knowledge-based Systems 8. Knowledge Acquisition, Representation and Reasoning 9. Advanced Intelligent Systems in Marketing 10. Marketing Applications: customer relationship management, database marketing, consumer choice modeling, market segmentation, market response modeling, sales forecasting 3. ASSESSMENT METHOD • Exam • Final Project 30% • Case Studies 20% 50% 4. BIBLIOGRAPHY • Turban, Efraim, Jay Aronson, Ting-Peng Ling (2005) Decision Support Systems and Intelligent Systems (7/E). Prentice Hall. • Miller, Thomas (2005) Data and Text Mining: A Business Application Approach. Prentice Hall.