Modeling toothpaste brand choice: an empirical comparison of artificial neural networks and multinomial probit model
International Journal of Computational Intelligence Systems, 2010•Taylor & Francis
The purpose of this study is to compare the performances of Artificial Neural Networks (ANN)
and Multinomial Probit (MNP) approaches in modeling the choice decision within fast
moving consumer goods sector. To do this, based on 2597 toothpaste purchases of a panel
sample of 404 households, choice models are built and their performances are compared
on the 861 purchases of a test sample of 135 households. Results show that ANN's
predictions are better while MNP is useful in providing marketing insight.
and Multinomial Probit (MNP) approaches in modeling the choice decision within fast
moving consumer goods sector. To do this, based on 2597 toothpaste purchases of a panel
sample of 404 households, choice models are built and their performances are compared
on the 861 purchases of a test sample of 135 households. Results show that ANN's
predictions are better while MNP is useful in providing marketing insight.
The purpose of this study is to compare the performances of Artificial Neural Networks (ANN) and Multinomial Probit (MNP) approaches in modeling the choice decision within fast moving consumer goods sector. To do this, based on 2597 toothpaste purchases of a panel sample of 404 households, choice models are built and their performances are compared on the 861 purchases of a test sample of 135 households. Results show that ANN's predictions are better while MNP is useful in providing marketing insight.
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