Conjoint analysis is used to study the factors that influence customers, purchasing decisions. Products possess attributes such as price, color, ingredients, guarantee, environmental impact, predicted reliability and so on. Conjoint analysis is based on a main effects analysis-of-variance model. Subjects provide data about their preferences for hypothetical products defined by attribute combinations. Conjoint analysis decomposes the judgment data into components, based on qualitative attributes of the products. A numerical part-worth utility value is computed for each level of each attribute. Large part-worth utilities are assigned to the most preferred levels, and small part-worth utilities are assigned to the least preferred levels. The attributes with the largest part-worth utility range are considered the most important in predicting preference. Conjoint analysis is a statistical model with an error term and a loss function.
With SurveyAnalytics's Conjoint module you can collect the data and simulate it through our conjoint simulator. Where in you may ask the respondent to arrange a list of combinatios of product attributes in decreasing order of preference. Once this ranking is obtained, you can use our advance simulator to simulate the data that will give you graphical representatio of your data. This method is efficient in the sense that the survey does not need to be conducted using every possible combination of attributes. The utilities can be determined using a subset of possible attribute combinations. From these results one can predict the desirability of the combinations that were not tested.
