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A game theoretic product design approach considering stochastic partworth functions

: Krausche, Daniel; Baier, Daniel


Lausen, B. ; Gesellschaft für Klassifikation:
Data science, learning by latent structures, and knowledge discovery : Selected papers presented during the European Conference on Data Analysis (ECDA 2013), Luxembourg, 10-12 July 2013
Berlin: Springer, 2015 (Studies in classification, data analysis and knowledge organization)
ISBN: 978-3-662-44982-0 (Print)
ISBN: 978-3-662-44983-7 (Online)
ISBN: 3-662-44982-X
European Conference on Data Analysis (ECDA) <1, 2013, Luxembourg>
Fraunhofer FIT ()

Developing new products is a necessary but costly and risky adventure. Therefore, the customers’ point of view and the prospective competitive environment have to be considered. Here, conjoint analysis has proven to be helpful since this preference modeling approach can be used to predict market shares [see, e.g., Baier and Gaul (J Econ 89(1–2):365–392, 1999), Baier and Gaul (Conjoint measurement: methods and applications. Springer, Berlin, pp. 47–66, 2007)]. When, additionally, competitive reactions must be considered, game theoretic approaches are a helpful extension [see, e.g., Choi and Desarbo (Market Lett 4(4):337–348, 1993), Steiner and Hruschka (OR Spektrum 22:71–95, 2000), Steiner (OR Spectrum 32:21–48, 2010)]. However, recently, new Bayesian procedures have been developed for conjoint analysis that allow to model customers’ partworth functions in a stochastic fashion. The idea is that customers have different preferences over time. In this paper we propose a new game theoretic approach that considers this new aspect. The new approach is applied to a (fictive) product design setting. A comparison to a traditional approach is presented.