The self-explicated model provides a simple alternative producing utility score estimates equal to or superior to that of other conjoint analysis methods. The self-explicated model is based theoretically on the multi-attribute attitude models that combine attribute importance with attribute desirability to estimate overall preference.
Survey Analytics's Self-explicated conjoint analysis offers a simple but surprisingly robust approach that is very simple to implement and does not require the development of full-profile concepts. First, factors and levels are presented to respondents for elimination if they are not acceptable in products under any condition.
The attribute levels retained in the analysis are then evaluated for desirability. Finally, the relative importance of attributes is measured using a constant sum scale to allocate 100 points between the most desirable levels of each attribute.
The attribute level desirabilities are then weighted by the attribute importances to provide utility values for each attribute level. This approach does not require regression analysis or aggregated solution required in many other conjoint approaches. This approach has been shown to provide results equal or superior to full-profile approaches, and places fewer demands on the respondent.
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.