Data defines the model by dint of genetic programming, producing the best decile table.

A Regression-tree Approach for Optimizing Price and Package Offerings
Bruce Ratner, Ph.D.

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.

For more information about this article, call me at 516.791.3544, or e-mail, br@dmstat1.com.
My publisher owns the copyright of the article, about which this abstract addresses. The article will appear in my forthcoming book.
My publisher has granted me permission to discuss orally the article's content, but by no means provide an outline, a draft or proof-ready of the article.


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