Abstract
A significant application of conjoint analysis is in pricing decisions for new products. Conceptually, profit maximization is an important criterion for selecting the price of a product. However, the maximization of profit necessitates estimation of fixed and variable costs, which are difficult to estimate reliably for the large number of products available for evaluation in conjoint analysis. Consequently, users of conjoint models have begun to use a share simulation to screen a small set of attractive products. For each screened product, fixed and variable costs are estimated separately and used to simulate its profits at different price levels. The limitation of this approach is that the profit simulations are based on the assumption that the conjoint data, and hence the predicted profits, are error free. Also, though the purpose of examining alternative prices is to determine the best price at which to offer a new product, current conjoint simulators do not focus explicitly on optimal pricing decisions. The authors describe and illustrate a model for optimal pricing of screened products in conjoint analysis, incorporating the effect of measurement and estimation error on predicted profits.
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