An empirical comparison of neural network and logistic regression models

Akhil Kumar, Vithala R. Rao, Harsh Soni

Research output: Contribution to journalArticle

48 Citations (Scopus)

Abstract

The purpose of this paper is to critically compare a neural network technique with the established statistical technique of logistic regression for modeling decisions for several marketing situations. In our study, these two modeling techniques were compared using data collected on the decisions by supermarket buyers whether to add a new product to their shelves or not. Our analysis shows that although neural networks offer a possible alternative approach, they have both strengths and weaknesses that must be clearly understood.

Original languageEnglish (US)
Pages (from-to)251-263
Number of pages13
JournalMarketing Letters: A Journal of Research in Marketing
Volume6
Issue number4
DOIs
StatePublished - Jan 1 1995

Fingerprint

Neural networks
Logistic regression model
Marketing
Buyers
Logistic regression
Decision modeling
Supermarkets
New products
Modeling

All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Economics and Econometrics
  • Marketing

Cite this

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An empirical comparison of neural network and logistic regression models. / Kumar, Akhil; Rao, Vithala R.; Soni, Harsh.

In: Marketing Letters: A Journal of Research in Marketing, Vol. 6, No. 4, 01.01.1995, p. 251-263.

Research output: Contribution to journalArticle

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