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Neural Nets and Genetic Algorithms in Marketing

  • Harald Hruschka
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 121)

Introduction

First publications on marketing applications of neural nets (NNs) and genetic algorithms (GAs) appeared in the early and mid 1990s, respectively. NNs mainly serve to estimate market response functions or compress data. Most of the relevant studies use GAs to solve optimization problems, although some of them apply GAs to estimate or select market response models.

Rational marketing decision making requires information on effects of marketing instruments, which as a rule are derived from market response functions. That is why I only discuss NNs which have been used to estimate market response functions. NNs for data compression are not treated (examples of marketing applications can be found in Hruschka and Natter 1999; Reutterer and Natter 2000; Mazanec, 2001).

Section 12.2gives an overview on multilayer perceptrons (MLPs), which are the kind of NNs the overwhelming majority of studies determining market response considers. This section deals with specification,...

Keywords

Linear Discriminant Analysis Hide Unit Output Unit Beam Search Customer Lifetime Value 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.University of RegensburgGermany

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