Data collection is one of the very important steps involved in Conjoint Research. Survey Analytics's advance survey building techniques gets you the maximum out of your audience.
The typical sequence that one goes through to implement a conjoint study involves following steps:
- Identification of the problem, along with dimensions of the product to be studied. How many attributes are considered and what are the levels of each attribute?
- Develop the study protocol including all contact, sampling and follow-up protocols.
- Develop the questionnaire and then pretest the survey and data collection activity.
- Using one of a variety of data collection procedures to collect the data.
- Process the data to derive at the individual respondent level estimates of the part-worth's of each person's utility function.
- Segmentation Analysis: The matrix of respondent by attribute-level part-worth's may then be related to other subject background data in an effort to identify possible market segments based on similarities in part-worth functions.
- Build and Run the Choice Simulator using a set of product configurations that represent feasible competitive offerings. These product profiles are entered into a consumer choice simulator, along with the earlier computed individual utility functions. Choice simulators differ, in the simplest case each respondent.s individual part-worth function is used to compute the utility for each of the competing profiles.
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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