Can artificial neural network models be used to improve the analysis of B2B marketing research data?

Purpose: Artificial neural network (ANN) models, part of the discipline of machine learning and artificial intelligence, are becoming more popular in the marketing literature and in marketing practice. This paper aims to provide a series of tests between ANN models and competing predictive models. Design/methodology/approach: A total of 46 pairs of models were evaluated in an objective model-building environment. Either logistic regression or multiple regression models were developed and then were compared to ANN models using the same set of input variables. Three sets of B2B data were used to test the models. Emphasis also was placed on evaluating small samples. Findings: ANN models tend to generate model predictions that are more accurate or the same as logistic regression models. However, when ANN models are compared to multiple regression models, the results are mixed. For small sample sizes, the modeling results are the same as for larger samples. Research limitations/implications: Like all marketing research, this application is limited by the methods and the data used to conduct the research. The findings strongly suggest that, because of their predictive accuracy, ANN models will have an important role in the future of B2B marketing research and model-building applications. Practical implications: ANN models should be carefully considered for potential use in marketing research and model-building applications by B2B academics and practitioners alike. Originality/value: The research contributes to the B2B marketing literature by providing a more rigorous test on ANN models using B2B data than has been conducted before.