Abstract:
Customer satisfaction analyses often suffer from the fact that it is difficult to compress the large amount of gathered data material into relevant, concrete action recommendations for decision makers, whereby the practical significance and operative convertibility of the results is particularly disputed. The suggested solution basis describes how a model of factors influencing customer satisfaction, from which initial measures can be derived, can be produced using a special kind of neural network based on empirically gathered data. Relevant, precise action recommendations are derived by testing the measures using a neural network as a simulation tool. This reduces the volume of information in a customer satisfaction survey to a list of measures to be implemented by the decision maker.
This could also be of interest:
Research Papers
Using the Internet to measure customer satisfaction
Catalogue: The Worldwide Internet Seminar 1998
Author: John Chisholm
 
June 15, 1998
Videos
Predicting TV viewership with neural networks
Catalogue: Big Data World 2017: Smart Data Integration
Author: Alex Ruiz
 
January 15, 2017
Research Papers
Neural marketing
Catalogue: ESOMAR Congress 1992: The Race Against Expectations
Author: Bruce Grey Tedesco
 
September 1, 1992
