Predicting Consumer Behavior with Artificial Neural Networks

Abstract

Nowadays, facile access to information and advancements in processing power unfold opportunities for new decision support techniques used for financial and economic purposes. Artificial neural networks are machine learning techniques which integrate a series of features upholding their use in financial and economic applications. Backed up by flexibility in dealing with various types of data and high accuracy in making predictions, these techniques bring substantial benefits to business activities. This paper investigates how consumer behavior can be identified using artificial neural networks, based on information obtained from traditional surveys. Results highlight that neural networks have a good discriminatory power, generally providing better results compared with traditional discriminant analysis.