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Chapter 29
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1. | Application Areas of Marketing Expert Systems | ||
2. | Problem Types and AI Techniques of Marketing Expert Systems | ||
3. | From Decision Aid to True Marketing | ||
4. | Expert System for Customized Purchasing Support | ||
5. | Architecture of Salesman Expert System | ||
6. | Menswear Case: Variables | ||
7. | Menswear Case: Constraints | ||
7.1. | Importance of Constraints | ||
7.2. | Types of Constraints | ||
7.2.1. | Value Compatibility Constraints | ||
7.2.2. | Value Incompatibility Constraints | ||
7.2.3. | Functional Assignment Constraints | ||
7.2.4. | Algebraic Inequality Constraints | ||
8. | Rules in Menswear Case | ||
8.1. | Customer-Product Rules | ||
8.2. | Sales Strategic Rules for Variable and Value Ordering | ||
8.2.1. | Variable Ordering Rules | ||
8.2.2. | Value Ordering Rules | ||
9. | Reasoning Procedure in Salesman Expert System | ||
10. | Trends of AI Research for Marketing | ||
Acknowledgments | |||
References |
Marketing has been one of the most active areas of expert system research and applications (Murdoch, 1990). The functional areas of marketing to which an expert systems approach has been applied include:
The research can be classified according to problem type as follows:
As we can see from the above, two major problem types are consulting and data analysis and mining. This reflects the need of many marketing activities to be supported by consulting activities, and the motivation that the existence of enormous amounts of scanner data provided to research and development of expert systems that analyze and mine them for marketing success. For marketing research, expert system concepts, paradigms, techniques, and tools provide new ways to build models that apply marketing knowledge. Besides the rule-based approach, which is the most popular, case-based approaches (Mcintyre, Achabal, and Miller, 1993; Burke, 1991), model-based approaches (McCann and Gallagher, 1990; McCann, Lahti, and Hill, 1991), and neural networks (Poh and Jasic, 1995) have also been used as AI techniques for marketing decision-making.
Mitchell et al. (Mitchell, Russo, and Wittink, 1991) compared the use of human judgment, expert systems, and statistical/optimization models for marketing decisions and claimed that the construction of expert systems in marketing poses problems that are either quantitatively or qualitatively more difficult than those faced by expert systems builders in non-human domains. Some systems, for instance, had to use hybrid techniques such as knowledge-based techniques with algorithmic techniques (Singh, Cook, and Corstjens, 1988), knowledge-based techniques with statistical methods (Abraham and Lodish, 1993; Rangaswamy, Harlam, and Lodish, 1991), and constraint-and rule-based techniques (Lee, Lee, and Lee, 1996).
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