Discrete choice modelling, also known as choice-based conjoint or brand price choice modelling, is the recommended survey-based approach to guide pricing decisions.
Discrete choice models reflect the real world more closely than other claimed preferences based approaches for pricing research. In these models, respondents evaluate multiple sets of alternatives, and choose one alternative from each of the choice sets. This task of choosing from a group of products is what they normally do when they purchase a product.
The choice sets must satisfy a few conditions. Firstly, the alternatives in each choice set must be collectively exhaustive, so that respondents necessarily choose an alternative from the set. The inclusion of a “catch-all” alternative such as “none of the above” is usually required to make a choice set collectively exhaustive.
Secondly, the alternatives must be mutually exclusive, i.e. choosing one alternative means not choosing any other alternatives. Respondents can choose only one alternative from the set.
Thirdly, the choice sets contain a finite number of alternatives, as opposed to some other modelling techniques where the dependent variable can theoretically take an infinite number of values.
In the context of pricing research, the choice sets are sets of brand/price options such as the one shown in Exhibit 26.9. Respondents are asked to select a brand from each of the sets used in the study.
The models may take multiple forms including multinomial logit, which is covered in the appendix to this chapter, conditional logit, multinomial probit and a number of other versions. Of considerable relevance today, due of their many advantages and growing popularity, are the hierarchical Bayes choice models.
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