Journal article
Mining sales data using a neural network model of market response
SIGKDD explorations, Vol.1(1), pp.39-43
06/1999
DOI: 10.1145/846170.846174
Abstract
Modeling aggregate market response is a core issue in marketing research. In this research, we extend previous forecasting comparative research by comparing the forecasting accuracy of feed-forward neural network models to the premier market modeling technique, Multiplicative Competitive Interaction (MCI) models. Forecasts are compared in two separate studies: (1) the Information Resources Inc. (IRI) coffee dataset from Marion, IN and (2) the A. C. Nielsen catsup dataset from Sioux Falls, SD. Our results suggest neural networks are a useful substitute for MCI models when there are too few observations available to estimate a fully-extended MCI model. Implications are discussed.
Details
- Title: Subtitle
- Mining sales data using a neural network model of market response
- Creators
- Thomas S. Gruca - University of IowaBruce R. Klemz - University of Nebraska at KearneyE. Ann Furr Petersen - University of Iowa
- Resource Type
- Journal article
- Publication Details
- SIGKDD explorations, Vol.1(1), pp.39-43
- DOI
- 10.1145/846170.846174
- ISSN
- 1931-0145
- eISSN
- 1931-0153
- Language
- English
- Date published
- 06/1999
- Academic Unit
- Marketing
- Record Identifier
- 9984380585402771
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