Neural networks for the analysis and forecasting of advertising and promotion impact

Hean‐Lee Poh, Jingtao Yao and Teo Jašic

Intelligent Systems in Accounting, Finance and Management, 1998, vol. 7, issue 4, 253-268

Abstract: Allocating advertising expenses and forecasting total sales levels are the key issues in retailing, especially when many products are covered and significant cross‐effects among products are likely. Various statistical and econometric methods could be applied for such analyses. We explore how well neural networks can be used in analyzing the effects of advertising and promotion on sales in this article. The results reveal that the predictive quality of neural networks depends on the different frequency of data observed, i.e. daily or weekly data models, and the specific learning algorithms used. The study also shows that neural networks are capable of capturing the nonlinear aspects of complex relationships in non‐stationary data. By performing sensitivity analysis, neural networks can potentially single out important input variables, thereby making it useful for scenario development and practical use. Copyright © 1998 John Wiley & Sons, Ltd.

Date: 1998
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://doi.org/10.1002/(SICI)1099-1174(199812)7:43.0.CO;2-X

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:wly:isacfm:v:7:y:1998:i:4:p:253-268

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1099-1174

Access Statistics for this article

More articles in Intelligent Systems in Accounting, Finance and Management from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().