An Artificial Neural Net Attraction Model (Annam) To Analyze Market Share Effects Of Marketing Instruments

Harald Hruschka

Schmalenbach Business Review (sbr), 2001, vol. 53, issue 1, 27-40

Abstract: Attraction models are very popular in marketing research for studying the effects of marketing instruments on market shares. However, so far the marketing literature only considers attraction models with certain functional forms that exclude threshold or saturation effects on attraction values. We can achieve greater flexibility by using the neural net based approach introduced here. This approach assesses brands’ attraction values by means of a perception with one hidden layer. The approach uses log-ratio transformed market shares as dependent variables. Stochastic gradient descent followed by a quasi-Newton method estimates parameters. For store-level data, neural net models perform better and imply a price response that is qualitatively different from the well-known multinomial logit attraction model. Price elasticities of neural net attraction models also lead to specific managerial implications in terms of optimal prices.

JEL-codes: C14 C45 M31 (search for similar items in EconPapers)
Date: 2001
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.vhb.de/sbr/pdfarchive.html (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sbr:abstra:v:53:y:2001:i:1:p:27-40

Access Statistics for this article

Schmalenbach Business Review (sbr) is currently edited by Wolfgang Ballwieser

More articles in Schmalenbach Business Review (sbr) from LMU Munich School of Management Contact information at EDIRC.
Bibliographic data for series maintained by sbr ( this e-mail address is bad, please contact ).