To some degree, all decisions involve choice. Individuals choose among different alternatives; commuters choose among possible routes and methods of transport; shoppers choose among competing products based on attributes such as price, quality and quantity.
Unlike with traditional polls and surveys, choice model predictions can be made over large numbers of scenarios within a context, to the order of many trillions of possible scenarios. Choice modeling is the most accurate and general-purpose tool currently available for making behavioral predictions, and human decision-making is regarded as the most suitable method for estimating consumers’ willingness to pay for quality improvements in multiple dimensions.
In marketing research, the most common types of choice models are forms of conjoint analysis, such as discrete choice modeling or paired-comparison analysis.
One option for choice modeling is maximum difference (max-diff) analysis (or scaling). Max-diff is based on customer choice or trade-off instead of typical rating-scale responses. Max-diff is the multinomial extension of the traditional method of paired comparisons (Thurstone 1927, David 1988). Whereas a paired-comparison question asks a respondent to make a binary choice, max-diff has the respondent specify “best” and “worst” choices from sets of three or more objects.
Most importantly, max-diff allows the researchers to test a large number of attributes without having to resort to unwieldy, large orthogonal models or the complicated process of adaptive conjoint analysis. What can be accomplished with 10 relatively simple max-diff choice scenarios replaces up to 70 paired-comparison analyses.
A maximum-difference choice model is easily administered, has multiple levels of analysis and is a very effective tool in establishing the relative priority of such items as:
potential message for a new product;
features or benefits of a service;
which extras to include in a loyalty program;...