Marketing Data Analysis Using Simulated Breeding and Inductive Learning Techniques

Abstract

Marketing decision making tasks require the acquisition of efficient decision rules from noisy questionnaire data. Unlike popular learning-from-example methods, in such tasks, we must interpret the characteristics of the data without clear features of the data nor predetermined evaluation criteria. The problem is how domain experts get simple, easy-to-understand, and accurate knowledge from noisy data. This paper describes a novel method to acquire efficient decision rules from questionnaire data using both simulated breeding and machine learning techniques. The basic ideas of the method are that simulated breeding is used to get the effective features from the questionnaire data and that machine learning is used to acquire simple decision rules from the data. The simulated breeding is one of the Genetic Algorithm based techniques to subjectively or interactively evaluate the qualities of offspring generated by genetic operations in a human-in-a-loop manner. The proposed method has been qualitatively and quantitatively validated by a case study on consumer product questionnaire data: the acquired rules are simpler than the results from the direct application of inductive learning; a domain expert admits that they are adequate and easy to understand; and they are at the same level on the accuracy compared with the other methods. The prerequisites of the method are so simple that it can be used to various decision making problems.