The authors (Orme and Heft) provide evidence that, under proper conditions, conjoint analysis can accurately predict what buyers do in the real world. Their results are based on CBC interviews conducted in grocery stores, where the CBC results were used to predict actual sales for three product categories of packaged goods from those same stores with good success.
A second purpose of the paper is to show that capturing heterogeneity (reflecting differences in preference between groups or individuals) with Latent Class or ICE can improve predictions. Many complex effects (substitution, cross-effects and interactions) can be accounted for with disaggregate Main Effect models. The authors note that complex terms can be built into large aggregate logit models, but that such models risk overfitting. Moreover, that approach places a great deal of responsibility on the analyst to choose the right combination of terms. This paper was originally presented at the 1999 Sawtooth Software Conference.