Discrete Choice Model versus conventional Conjoint Analysis

Conjoint analysis (CA) and discrete choice models (DCM) are preference structured models that are widely used in market research and analytics. They often cover common ground, yet important distinctions that exist between the two make them better suited for different types of research programmes.

The respondents in a conjoint analysis, evaluate product profiles independently of each other. Conversely, in DCM respondents simultaneously consider a set of profiles and select the one they are most likely to purchase (if any). The latter approach is similar to what they would actually do in the marketplace, which is important in pricing research, where authenticity is an important criteria.

DCM is also better at modelling the interaction effects between different product characteristics such as brand and price. This is of great relevance in pricing research where the purpose is to measure the distinct price elasticities of demand of brands.

On the other hand, since respondents are rating all product profiles, conjoint analysis extracts more information about the relative importance of the profiles, attributes and levels.

Like conjoint analysis, advanced hierarchical Bayes versions of DCM produce utilities at the individual level, and permit what-if simulations, where respondents are assumed to maximize utility.

Due to the differences cited above, conjoint analysis is better suited for product development studies where trade-offs are to be made. The analysis reveals consumer’s preferences of product features, and in the context of pricing, it can tell us what price may be charged for certain features.

DCM is the current gold standard in pricing research (ad hoc). It more accurately reveals the relationship between share and price.

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