Discrete Choice Conjoint Analysis

In Discrete Choice Model Respondents are shown different products or services. In this case, rather than rating or ranking them, they are asked to select the one they would be most likely to purchase. For example, respondents might be shown three different computer models and asked to indicate the one they would purchase. Discrete choice holds a number of advantages over traditional conjoint including:

  • It is a more realistic exercise for individuals to indicate which product they would purchase rather than rating/ranking since this is what they actually do in the marketplace.
  • In discrete choice, individuals can be given the option to select .none. of the products, thus indicating that they do not find any of the products appealing.
  • Discrete choice allows for much more complex statistical modeling to be performed, which often yields better data (e.g., interactions, alternative.specific effects, cross-effects, etc., can be accommodated). As with traditional conjoint analysis, the utilities that come from discrete choice can be used to develop market simulators and can also be used to examine whether different segments exist using either latent class analysis or Hierarchical Bayes.
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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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