Catálogo de publicaciones - libros

Conjoint choice experimentation involves the design of product profiles on the basis of product attributes specified at certain levels, and requires respondents to repeatedly choose one alternative from different sets of profiles offered to them, instead of ranking or rating all profiles, as is usually done in various forms of classic metric conjoint studies. The Multinomial Logit (MNL) model has been the most frequently used model to analyze the 0/1 choice data arising from such conjoint choice experiments (e.g., Louviere and Woodworth 1983; Elrod, Louviere and Davey 1992). One of the first articles describing the potential advantages of a choice approach for conjoint analysis was by Madanski (1980). His conclusion was that conjoint analysts could adopt the random utility model approach to explain gross trends or predilections in decisions instead of each person's specific decision in each choice presented. The real breakthrough for conjoint choice came with the Louviere and Woodworth (1983) article in which they integrated the conjoint and discrete choice approaches.

Soon after the introduction of conjoint analysis into marketing by Green and Rao (1972), Srinivasan and Shocker (1973a, 1973b) introduced a conjoint analysis estimation method, Linmap, based on linear programming. Linmap has been applied successfully in many situations and has proven to be a viable alternative to statistical estimation (Jain, et. al. 1979, Wittink and Cattin 1981). Recent modification to deal with “strict pairs” has improved the estimation accuracy with the result that, on occasion, the modified Linmap predicts holdout data better than statistical estimation based on hierarchical Bayes methods (Srinivasan 1998, Hauser, et. al. 2006).

In abstract terms conjoint analysis can be seen as fitting a model to preference information elicited from a group of respondents. That is, conjoint analysis comprises two tasks,

Recent advances in flexibility and automation allow a growing number of manufacturers and service providers to ‘mass-customize’ their products and offer modules from which consumers can create their own individualized products (e.g., Gilmore and Pine 1997). Traditional production processes limit consumer choices to fixed products defined by suppliers, but new mass-customization processes allow consumers to create their own optimal combination of product components. Although mass-customization offers consumers increased flexibility and consumption utility, little is known about how consumer choices to package or bundle separate components differ (if at all) from choices among traditional fixed product options, much less what the impact of packaging product components will be on the market shares of such products or a producer’s overall share in the category.

Conjoint analysis was introduced to market researchers in the early 1970s as a means to understand the importance of product and service attributes and price as predictors of consumer preference (e.g., Green and Rao 1971; Green and Wind 1973). Since then it has received considerable attention in academic research (see Green and Srinivasan 1978, 1990 for exhaustive reviews; and Louviere 1994 for a review of the behavioral foundations of conjoint analysis). By systematically manipulating the product or service descriptions shown to a respondent with an experimental design, conjoint analysis allows decision-makers to understand consumer preferences in an enormous range of potential market situations (see Cattin and Wittink 1982; Wittink and Cattin 1989; and Wittink, Vriens, and Burhenne 1994 for surveys of industry usage of conjoint analysis).

Since the pioneering research of Wendell Smith (1956), the concept of market segmentation has been one of the most pervasive activities in both the marketing academic literature and practice. In addition to being one of the major ways of operationalizing the marketing concept, marketing segmentation provides guidelines for a firm’s marketing strategy and resource allocation among markets and products. Facing heterogeneous markets, a firm employing a market segmentation strategy can typically increase expected profitability as suggested by the classic price discrimination model which provides the major theoretical rationale for market segmentation (cf. Frank, Massey and Wind 1972).

One of the reasons conjoint analysis has been so popular as a management decision tool has been the availability of a choice simulator. These simulators often arrive in the form of a software or spreadsheet program accompanying the output of a conjoint study. These simulators enable managers to perform ‘what if’ questions about their market - estimating market shares under various assumptions about competition and their own offerings. As examples, simulators can predict the market share of a new offering; they can estimate the direct and cross elasticity of price changes within a market, or they can form the logical guide to strategic simulations that anticipate short- and long-term competitive responses (Green and Krieger 1988).

Conjoint analysis is a research tool for assessing market potential, predicting market share and forecasting sales of new or improved products and services. In general, conjoint analysis follows a two-step process, i.e., (1) estimating utilities for varying levels of product features and (2) simulating marketplace preferences for established, improved, and/or new products. Conjoint analysis was introduced in the 1970s (Green and Rao 1971) and by 1980 had logged more than 1000 commercial applications (Cattin and Wittink 1982). During the 1980s usage increased tenfold (Wittink and Cattin 1989). Today it may be the most widely used quantitative product development tool in the U.S. and Europe (Wittink, Vriens, and Burhenne 1994).