Comparison of some AI and statistical classification methods for a marketing case |
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| Institution: | 1. School of Information Systems, Singapore Management University, Singapore;2. Graduate School of Business, Seoul National University, Republic of Korea;1. ESSEC Business School, Cergy, France;2. Northwestern University, Evanston, IL, United States of America;3. The University of Auckland, New Zealand;4. Xiamen University Malaysia, Malaysia;5. Fujitsu Global, Tokyo, Japan;6. Department of Management, Technology & Economics, ETH Zurich, Zurich, Switzerland;1. Department of Management Information Systems, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, Saudi Arabia;2. Al Balqa’ Applied University, Amman College for Financial & Managerial Science, Jordan;3. Qatar University, College of Business and Economics, Department of Management and Marketing, Doha, Qatar;4. School of Management, University of Bristol, UK;5. Symbiosis Institute of Business Management Pune, Symbiosis International (Deemed) University, Pune, India;6. Al Balqa’ Applied University, Prince Abdullah Ben Ghazi Faculty of Information and Communication Technology, Jordan |
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| Abstract: | Recent progress in data processing technology has made the accumulation and systematic organization of large volumes of data a routine activity. As a result of these developments, there is an increasing need for data-based or data-driven methods of model development. This paper describes data-driven classification methods and shows that the automatic development and refinement of decision support models is now possible when the machine is given a large (or sometimes even a small) amount of observations that express instances of a certain task domain. The classifier obtained may be used to build a decision support system, to refine or update an existing system and to understand or improve a decision-making process. The described AI classification methods are compared with statistical classification methods for a marketing application. They can act as a basis for data-driven decision support systems that have two basic components: an automated knowledge module and an advice module or, in different terms, an automated knowledge acquisition/retrieval module and a knowledge processing module. When these modules are integrated or linked, a decision support system can be created which enables an organization to make better-quality decisions, with reduced variance, probably using fewer people. |
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