A Short History of Conjoint Analysis

The genesis of new statistical models has rarely been within the field of marketing research. Marketing researchers have mainly borrowed from other fields. Conjoint analysis and the more recent discrete choice or choice-based conjoint methods are no exception. Conjoint methods were based on work in the sixties by mathematical psychologists and statisticians Luce and Tukey (1964), and discrete choice methods came from econometrics, building upon the work of McFadden (1974), 2000 Nobel Prize winner in economics. Marketers sometimes have thought (or been taught) that the word “conjoint” refers to respondents evaluating features of products or services “CONsidered JOINTly.” In reality, the adjective “conjoint” derives from the verb “to conjoin,” meaning “joined together.” The key characteristic of conjoint analysis is that respondents evaluate product profiles composed of multiple conjoined elements (attributes or features). Based on how respondents evaluate the combined elements (the product concepts), we deduce the preference scores that they might have assigned to individual components of the product that would have resulted in those overall evaluations. Essentially, it is a back-door, decompositional approach to estimating people’s preferences for features rather than an explicit, compositional approach of simply asking respondents to rate the various features. The fundamental premise is that people cannot reliably express how they weight separate features of the product, but we can tease these out using the more realistic approach of asking for evaluations of product concepts through conjoint analysis. Let us not deceive ourselves. Human decision making and the formation of preferences is complex, capricious, and ephemeral. Traditional conjoint analysis makes some heroic assumptions, including the proposition that the value of a product is equal to the sum of the values of its parts (i.e., simple additivity), and that complex decision making can be explained using a limited number of dimensions. Despite the leaps of faith, conjoint analysis tends to work well in practice, and gives managers, engineers, and marketers the insight they need to reduce un-