Automatic discovery of potential causal structures in marketing databases based on Fuzzy association rules

Marketing-oriented firms are especially concerned with modeling consumer behavior in order to improve their information and aid their decision processes on markets. For this purpose, marketing experts use complex models and apply statistical methodologies to infer conclusions from data. In the recent years, the application of machine learning has been identified as a promising approach to complement these classical techniques of analysis. In this chapter, we review some of the first approaches that undertake this idea. More specifically, we review the application of Fuzzy-CSar, a machine learning technique that evolves fuzzy association rules online, to a certain consumption problem analyzed. As a differentiating sign of identity from other methods, Fuzzy-CSar does not assume any a prioristic causality (so model) within the variables forming the consumer database. Instead, the system is responsible for extracting the strongest associations among variables, and so, the structure of the problem. Fuzzy-CSar is applied to the real-world marketing problem of modeling web consumers, with the aim of identifying interesting relationships among the variables of the model. In addition, the system is compared with a supervised learning technique, which is able to extract associations between a set of input variables and a pre-fixed output variable, expressly designed for this marketing problem. The results show that Fuzzy-CSar can provide interesting information for marketing experts that was not detected by the classical approach, and that the extraction of fuzzy association rules is an appealing alternative, in general, to refine or complement the modeling results obtained with the use of traditional methods of analysis applied for these purposes; in particular, we focus on, and take as a reference, the structural equation modeling.