Conjoint analysis – optimization and development of product offerings, market modeling. Is a method that is used primarily to study consumer preferences.

It refers to quantitative research methods and is used in the development of new products or revision/modernization of existing products when it is necessary to determine what technical, consumer, and other characteristics the product should have to be demanded in the market by end users.

The collaborative analysis is one of the most complex methods of marketing research. It is characterized by multi-stage, complex tools, and multi-step algorithms of analysis with the use of “heavy” statistical methods (in particular, regression analysis). The purpose of the method is to obtain a list of product characteristics that are most important for consumers, to form a profile of the “ideal product”.Conjoint-analysis

The advantage of conjoint analysis is that by answering the questionnaire, the respondent makes a choice as close as possible to the situation of real product choice. This is achieved because in the questionnaire the respondent is offered to choose from alternatives containing a complex description of products, and each of the alternatives contains different sets of product characteristics (so-called “product profiles”).
For example, if we are conducting a survey of tea products, the “profiles” between which the respondent chooses may look as follows:

The names of the columns in the table (sheet, package, price, weight, country) are the so-called “attributes” (product parameters), and the contents of the columns are the so-called “attribute levels” (specific values of the “attributes”). For example, for the attribute “country” the levels are China, Ceylon, Pakistan and India.

Profiles are formed as combinations of levels for each of the attributes, so the number of profiles can reach several dozens . Depending on the analysis scheme, the respondent evaluates the attractiveness of each profile on a suggested scale (for example, from 1 to 100), or chooses between several suggested profiles (between two, three, or four), or ranks the profiles in order of preference (from most attractive to least attractive).

At the output, we get numerical coefficients that show how important each of the “levels” (the so-called “utility levels”) and each of the “attributes” (the so-called “importance of attributes”) are to consumers. Knowing these coefficients, we can quantify different combinations of product characteristics: the higher the final coefficient of a product with a given combination of characteristics, the more attractive this product is to the consumer.