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 customized-pricing bid-response models 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 dataset as a test bed to measure the model fit. Finally, we test the models on the industry dataset to compare their performance and estimate the percent improvement in expected profits that may be possible from their use.
In this paper, we study pricing situations where a firm provides a price quote in the presence of uncertainty in the competitive landscape and the preferences of the buyer. We review two possible customized-pricing bid-response models 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 show how the models may be adjusted depending on the amount of historical bid information available to the user. Finally, we test the two methods on two industry datasets to compare their performance and estimate the percent improvement in expected profits that may be possible from their use.