Bid-response models for customised pricing

In this paper, we study pricing situations where a firm provides a price quote in the presence of uncertainty in the preferences of the buyer and the competitive landscape. We introduce two customised-pricing bid-response models (CPBRMs) used in practice, which can be developed from the historical information available to the firm based on previous bidding opportunities. We show how these models may be used to exploit the differences in the market segments to generate optimal price quotes given the characteristics of the current bid opportunity. We also describe the process of evaluating competing models using an industry data set as a test bed to measure the model fit. Finally, we test the models on the industry data set to compare their performance and estimate the per cent improvement in expected profits that may be possible from their use.

[1]  L. Friedman.  A Competitive-Bidding Strategy , 1956 .

[2]  M. Gates.  Bidding Strategies and Probabilities , 1967 .

[3]  Thomas L. Morin,et al.  OPBID: COMPETITIVE BIDDING STRATEGY MODEL , 1969 .

[4]  Robert M. Stark,et al.  Competitive Bidding: A Comprehensive Bibliography , 1971, Oper. Res..

[5]  John H. Aldrich,et al.  Linear probability, logit and probit models , 1984 .

[6]  Distributions in Competitive Bidding , 1991 .

[7]  L. Gary,et al.  ABELL F. Dereck, Defining The Business. The Starting Point of Strategic Planning . USA, Prentice Hall, Englewood Cliffs, New Jersey, 1980. , 1996 .

[8]  M. Wedel,et al.  Market Segmentation: Conceptual and Methodological Foundations , 1997 .

[9]  N. Cassaigne,et al.  Bid pricing-calculating the possibility of winning , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[10]  Vicky Papaioannou,et al.  A critical analysis of bid pricing models and support tool , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[11]  Martin Skitmore.  Predicting the probability of winning sealed bid auctions: a comparison of models , 2002, J. Oper. Res. Soc..

[12]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[13]  Richard D. Lawrence.  A Machine-Learning Approach to Optimal Bid Pricing , 2003 .

[14]  Julie L. Swann,et al.  Expert opinions: Current pricing and revenue management practice across U.S. industries , 2006 .

[15]  May,et al.  [Wiley Series in Probability and Statistics] Applied Survival Analysis (Regression Modeling of Time-to-Event Data) || Extensions of the Proportional Hazards Model , 2008 .