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 paper covers the problem of: envisaged changes to the current market situation. The analytical method used to establish a micro simulation model is an advanced approach to Conjoint Measurement Analysis of survey data. The final aim of this research issue is to provide the suppliers of products with a Decision Support System representing problem solving tools that enable marketing management to simulate the consequences of different marketing strategies. Along with a written report giving the main results of the study carried out, the client will receive a simulation disc by means of which he will be able to achieve the necessary simulations by himself. The application of this new approach will be demonstrated by the case study: "Product Development, Pricing and Competitive Situation for Airline Services".