Hybrid Genetic Fuzzy System for Modeling Consumer Behavior

Understanding consumer behavior is beneficial to a business in various aspects such as prediction of manufacturing quantity, new product launch, and aids in lock-in customers and lock-out competitors. The task is highly complex and traditional models do not help in absence of generalized decision making logic. Further such domains handle large amount of data in unstructured format. This article presents an intelligent system for modeling consumer behavior via a hybrid genetic fuzzy system from large source of data. The paper justifies and presents a literature survey with common observations. A four phase generic architecture of genetic fuzzy system presented for the modeling of consumer behavior. Detailed discussion on the architecture is also provided with an experiment. Technical details, fuzzy membership functions used in experiment, encoding strategy, genetic operators, and evaluation of rules using fitness function are also discussed in detail along with results. At end, applications of the research work in other domains are enlisted with possible future enhancements.