Summary
As an increasing interest of the scientific community is devoted to the application of neural networks to pattern recognition, forecasting and optimization, we will demonstrate how so-called Backpropagation Networks can be used to specify market response functions from sales data. The proceeding will be demonstrated by market share analysis. Against this background it is also shown how market diagnostics (e.g., market shares and elasticities) which are well-known to marketing managers can be calculated from a calibrated neural network. Finally, results from an empirical application including a comparison of the new approach with a traditional market share model are sketched.
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References
Cooper, L.G., and Nakanishi, M. (1988): Market-Share Analysis, Kluwer Academic Publishers, Boston.
Decker, R. (1994): Analyse und Simulation des Kaufverhaltens auf Konsumgütermärkten, Lang, Frankfurt.
Decker, R., and Wartenberg, F. (1994): Neue Wege der Wettbewerbsanalyse im Einzelhandel. Markenartikel, 2, 46–51.
Hanssens, D.M., Parsons, L.J., and Schultz, R.L. (1990): Market Response Models: Econometric and Time Series Analysis, Kluwer Academic Publishers, Boston.
Hertz, J., Krogh, A., and Palmer, R.G. (1991): Introduction to the Theory of Neural Computation, Addison-Wesley, Redwood City.
Howard, J.A., and Sheth, J.N. (1969): The Theory of Buyer Behavior, Wiley, New York.
Hruschka, H. (1991): Einsatz künstlicher neuraler Netzwerke zur Datenanalyse im Marketing. Marketing ZFP, 4, 217–225.
Mcfadden, D. (1974): Conditional Logit Analysis of Qualitative Choice Behavior, in: P. Zarembka (ed.): Frontiers in Econometrics, Academic Press, New York, 105–142.
Naert, P.A., and Leeflang, P.S.H. (1978): Building Implementable Marketing Models, Leiden, Stenfert Kroese.
Ronning, G. (1991): Mikrkonometrie, Springer, Berlin.
Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986): Learning Internal Representation by Error Propagation. In: D.E. Rumelhart and J.L. Mlelland (eds.):Parallel Distributed Processing, MIT Press, Cambridge, 318–362.
Wartenberg, F., Gaul, W., and Decker, R. (1994): Alternativ Modell: Neuronale Netze in der Kaufverhaltensforschung, Absatzwirtschaft, 7, 66–69.
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© 1996 Springer-Verlag Berlin · Heidelberg
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Wartenberg, F., Decker, R. (1996). Analysis of Sales Data: A Neural Net Approach. In: Gaul, W., Pfeifer, D. (eds) From Data to Knowledge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79999-0_33
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DOI: https://doi.org/10.1007/978-3-642-79999-0_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60354-2
Online ISBN: 978-3-642-79999-0
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