Operationalizing Dynamic Pricing Models Bayesian Demand Forecasting
Dynamic Pricing of services has become the norm for many young service industries – especially in today’s volatile markets. Steffen Christ shows how theoretic optimization models can be operationalized by employing self-learning strategies to construct re
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GABLER RESEARCH
Steffen Christ
Operationalizing Dynamic Pricing Models Bayesian Demand Forecasting and Customer Choice Modeling for Low Cost Carriers With a foreword by Prof. Dr. Robert Klein
RESEARCH
Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at http://dnb.d-nb.de.
Dissertation University of Augsburg, 2009
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“It is very difficult to make an accurate prediction, especially about the future.” Niels Bohr 1
1
Niels (Henrik David) Bohr (October 7, 1885 – November 18, 1962) was a Danish physicist who made fundamental contributions to understanding atomic structure and quantum mechanics. Bohr is widely considered one of the greatest physicists of the twentieth century.
Foreword Following the success of low cost carriers, dynamic pricing has become one of the most popular fields of research at the interface of Marketing and Operations Management. However, analyzing the available literature reveals that most publications concentrate on the development of optimization models for price variation. The major challenge of forecasting demand is most often ignored. With his dissertation, Steffen Christ aims to close the corresponding gap by examining the applicability of existing optimization models for dynamic pricing on the specifics of highly volatile consumer markets using the example of low cost air carriers. The explicit objective is the operationalization of theoretically sound standing dynamic pricing models using only realistic input assumptions and retracting to factually available data. The approach chosen by Steffen Christ is rooted in the development of self learning demand models that calibrate their parameters as data becomes available yielding the option of using the returned results as input to conventional dynamic pricing models. It claims that the developed models can provide parts of the necessary input data in a merely plug-and-play fashion. Generally, the work of Steffen Christ targets the understanding of both relevant input values for dynamic pricing models
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