Profit optimization searches are often the most useful to a business. However, most consultants and firms will probably not have access to accurate cost data. Without cost data, searches often yield trivial solutions wherein the “optimal” product is the one with the best features at the lowest price. Solutions can be constrained to consider attribute combinations that are most realistic and promising, but this often doesn’t adequately address the problem.
Even though many firms do not have access to detailed costing information by attribute level, they often know the incremental prices that different attribute levels add to the base price of an offering. Automobiles, computers, custom-built homes, and manufacturing equipment (just to mention a few) have different options (e.g. add $1,200 for air conditioning, add 500GB more hard drive space for $30), and buyers consider the utility of the options and their incremental prices in choosing a final product to purchase.
In the absence of good cost information, it makes sense to add attribute-based pricing information, assuming good data are available. The total price for a searched product is summed across the attribute price tables and used to evaluate the desirability of the product concept according to the price attribute's part-worth utilities. Thus, a desirable product feature is not automatically included within a searched product unless its marginal value exceeds the marginal price for buyers.