Applied Conjoint Analysis

Several interdependent decisions are involved in the formulation of a marketing strategy for a brand (of a product or service). These include not only decisions about the product’s characteristics but also its positioning, communication, distribution, and pricing to chosen sets of targeted customers. The decisions will need to be made in the wake of uncertain competitive reactions and a changing (and often unpredictable) environment. For a business to be successful, the decision process must include a clear understanding of how customers will choose among (and react to) various competing alternatives. It is well accepted in marketing that choice alternatives can be described as profiles on multiple attributes and that individuals consider various attributes while making a choice. While choosing, consumers typically make trade-offs among the attributes of a product or service. Conjoint analysis is a set of techniques ideally suited to studying customers’ choice processes and determining tradeoffs.

The basic principles of designing a marketing research study will apply to any study that uses conjoint analysis. Differences arise in the conceptual foundations. The conceptual model of conjoint analysis is quite straightforward; it postulates that the utility of a multi-attributed item can be decomposed into specific contributions of each attribute and possibly their interactions. The approach is easy to implement if the number of attributes is small. But, problems arise in most practical problems because of the large number of possible hypothetical alternatives for a given problem. In general, only a subset of possible alternatives is chosen for the study. Experimental design methods exist for selecting such subsets.

We saw in the previous chapter various methods for designing and collecting data in conjoint studies. The data collection procedure used almost invariably dictates the type of analytical method used in conjoint analysis. In addition, analysis methods depend on two major factors: the nature of the scale used for the dependent variable (preference) and the desired level of data aggregation.

One of the major objectives in conjoint analysis has been to predict the choices made by a sample of individuals for a new item which is described in terms of a set of attributes used in a conjoint study. Ratings-based conjoint studies involve the conversion of an individual’s stated utility for an item to predict the probability of choice of an alternative under various conditions (e.g. when other alternatives available). As described in Chap. 3, such a prediction is made using preference data (ratings or rankings) collected on a set of hypothetical choice alternatives. A parallel stream of research pursues the path of choice experiments in which an individual makes a choice among a set of choice alternatives, each of which is typically described by a set of attributes; several choice sets are presented to each individual. These choice data, across all the choice sets and all individuals, are then analyzed using a choice model (usually a multinomial logit model and sometimes multinomial probit model) to obtain a function that relates the attribute levels to probability of choice. This approach has come to be known as choice-based conjoint analysis and has its roots in discrete choice analysis; these methods are also called “stated” choice methods (or stated choice experimental methods) because they represent intended choices of respondents among hypothetical choice possibilities. This chapter describes these methods.

In the previous chapters we discussed various conjoint analysis methods for ratings-based ad choice-based studies. One problem that nags applied researchers is how to deal with the issue of large numbers of attributes (and levels) to be included that arise in any practical problem. This problem may arise particularly for technologically complex products which usually have a large number of attributes. Over the years, researchers have come up with different methods to deal with this problem. While we have mentioned tangentially some of the applicable methods, this chapter will pull together various methods developed. In the next section (Sect. 5.2), we will describe the main problem when a conjoint study has to deal with a large number of attributes and then present an overview of the methods available in the literature. In Sect. 5.3, we will describe each method in some detail (data collection approach and analysis method) along with an application. Section 5.4 compares the methods on a set of relevant criteria. Finally, we will offer several directions for future research on the issue of a large number of attributes in any conjoint study and conjecture possible newer developments. Some newer data collection methods that use auctions also deal with the large number of attributes problem.

The methodology of conjoint analysis has been most frequently used to tackle the difficult marketing problem of product/service design and product line selection. The typical conjoint approach for these problems is to implement a conjoint study (as per the details discussed in Chaps. 2 and 3) and to use the results to estimate attribute partworths preferably at the individual respondent level. These partworths are then used to determine the values of attributes (or design characteristics of a product or service) so as to optimize an objective function for a firm. This process requires the knowledge of the competitive set in which the new product(s) or product lines will compete and product costs (as a function of the product attributes). Usually the firm’s objective is to maximize the long-run profit potential for the new product(s) or product lines based on stable market shares of the new product(s). If cost information is not available, the objective of long run sales is used (Table 6.1).

As we discussed in the previous chapter, there is a subtle difference between product design and product positioning. While product design deals with decisions on the “optimal” characteristics of a product, product positioning deals with issues of how best to communicate the corresponding benefits (or attributes) to the target consumers (for more details, see Kotler and Keller (2012) and Kaul and Rao (1995)). Naturally the benefits of a product arise from its characteristics and the way consumers interpret them. In applications of conjoint analysis to product positioning, an analyst describes the possible benefits and their levels in the same way as one would in the case of product design; then the problem of determining the best positioning is identical to that of product design. In some cases, the analyst may include both product benefits and characteristics.

One significant application of conjoint analysis is in helping the manager with pricing decisions. Determination of optimal price for a new product (or brand) is a typical application. One way to determine the best price is to estimate the market obtainable from the new product at different feasible prices for the new product profile. We described the use of conjoint simulators in Chap.​ 3. Additional information on cost functions can be integrated into the estimates of market share to yield estimates of profit from the new product at various prices. The price at which the computed profit is highest can be deemed to be the best price for the new product. This approach can also yield a generic estimate of price elasticity for the product category as a whole.

We have seen applications of conjoint analysis to marketing problems such as product design, market segmentation, product positioning and pricing. We have also seen that conjoint simulators have been quite helpful in dealing with these questions. In this process, we have tangentially dealt with the design of appropriate competitive strategies. The objective of this chapter is to present an overview of several other applications to demonstrate the versatility of the methodology of conjoint analysis for general research in marketing.

The previous chapters described several approaches employed for determining partworths of attributes and tradeoffs among them. The chapters dealt with various methods for both of ratings-based and choice-based conjoint methods. In addition, we described several applications of conjoint methodology to different marketing problems such as product design, product positioning, pricing, market segmentation, and several miscellaneous problems. During the last thirty plus years since these methods were introduced to marketing research, researchers have tackled various problems that are encountered in applying these methods in practice. As Hauser and Rao (2006) have noted, conjoint analysis is alive and well. In fact there have been several developments in the last 5–10 years that place this methodology as one of the most vibrant techniques in marketing research.

We identify gaps and propose several directions for future research in preference measurement. We structure our argument around a framework that views preference measurement as comprising three interrelated components: (1) the problem that the study is ultimately intended to address; (2) the design of the preference measurement task and the data collection approach; (3) the specification and estimation of a preference model, and the conversion into action. Conjoint analysis is only one special case within this framework. We summarize cutting edge research and identify fruitful directions for future investigations pertaining to the framework’s three components and to their integration.