Modeling Toothpaste Brand Choice: An Empirical Comparison of Artificial Neural Networks and Multinomial Probit Model

Tolga Kaya*, Emel Aktaş, İlker Topçu, Burç Ülengin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)674-687
Number of pages14
JournalInternational Journal of Computational Intelligence Systems
Volume3
Issue number5
DOIs
Publication statusPublished - Oct 2010

Bibliographical note

Publisher Copyright:
© 2010, the authors.

Keywords

  • Brand choice modeling
  • artificial neural networks
  • household panel
  • multinomial probit
  • toothpaste

Fingerprint

Dive into the research topics of 'Modeling Toothpaste Brand Choice: An Empirical Comparison of Artificial Neural Networks and Multinomial Probit Model'. Together they form a unique fingerprint.

Cite this