Selecting the correct price for a product is a crucial decision often faced by marketers. Further complicating matters are packaging issues, as price is directly related to the product attributes and the positioning of the product offering in relation to the competitors' products. The traditional method of solving the pricing and packing issues is the parametric, assumption-full conjoint analysis. The purpose of this article is to present a regression tree alternative, such as CHAID,  to the conjoint paradigm that eliminates some of the thorny practical matters of implementing the high-wrought conjoint analysis: incorporating nonlinearities and nonadditivities in the final conjoint model solution. Two cases studies are discussed comparing and contrasting the traditional conjoint method and the nonparametric, assumption-free (and model-free!) regression tree approach for determining the optimal price and packaging based upon dozens of product attribute combinations.