Choice-Based Conjoint (CBC) is used for discrete choice modeling, a research technique that is now the most often used conjoint-related method in the world. The main characteristic distinguishing choice-based from other types of conjoint analysis is that the respondent expresses preferences by choosing from sets of concepts, rather than by rating or ranking them. The choice-based task is similar to what buyers actually do in the marketplace. Choosing a preferred product from a group of products is a simple and natural task that everyone can understand.
CBC data can be analyzed in a number of ways. First, the relative impact of each attribute level can be assessed just by counting "wins." In randomized CBC designs, each attribute level is equally likely to occur with each level of every other attribute. Therefore, the impact of each level can be assessed by counting the proportion of times concepts including that level are chosen. This "counting" method can be used for main effects as well as for two- or three-way interactions. For a second type of analysis, CBC includes an easy-to-use module to perform multinomial logic estimation. This analysis results in a set of conjoint "utilities," but which differ from standard conjoint in that they describe preferences of a group rather than for an individual. CBC's Logic module can estimate main-effects and two-way interactions.
Survey Analytics helps you build complex data collections surveys, that you can further run through the simultor for your Conjoint Research.
Survey Analytics Conjoint Module
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.
Survey Analytics is a web based service for conducting online surveys. With Survey Analytics 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.
The process is simple using Survey Analytics's online survey software:
- Add your logo and branding
- Full custom control over the format
- Full multi-lingual support (over 75 languages)
Survey Analytics Software Advantage
- Measure psychological, real or any hidden factors in consumer behavior more accurately.
- Test your new product ideas or examine the existing one for new features with market segmentation simulator.
- The most easy-to-use and Conjoint Analysis tool in the industry.
- Estimate your consumer preference at the individual level.
- Applications like product launch, product positioning, market segmentation and many others.
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