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Conjoint analysis requires research participants to make a series of trade-offs. Analysis of these trade-offs will reveal the relative importance of component attributes. To improve the predictive ability of this analysis, research participants should be grouped into similar segments based on objectives, values and/or other factors. With SurveyAnalytics.s research tool you can conduct surveys where the respondent simply chooses the most preferred alternative from a selection of competing alternatives - particularly common when simulating consumer choices. The use of hierarchical Bayesian analysis enables fairly robust statistical models of individual respondent decision behavior to be developed.
SurveyAnalytics's research techniques leads to insights that strengthen your market position.
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.
Amazon.com Gift Card - Rewards
Rewards - Amazon.com Gift Cards
How to set up Amazon.com Gift Card rewards integration?
You can set up Amazon.com Gift Cards as rewards on your survey. The first step is to set up the Qualifying Criteria.For example, we want to give a reward to all respondents who complete the survey. To do so, select the qualifying criteria as All Completed Respondents
Select a Reward - Amazon.com Gift Card
What happens when test mode is on?
When test mode is on, rewards are not fulfilled. This enables you to test the survey without using up your Amazon.com Gift cards.On the survey, when you fulfill the criteria for winning the reward, test page for reward will be displayed.
Once you are done with testing. Turn Off the test mode and save. When reward is fulfilled, Amazon.com Gift Cards will be given out.
Before making the survey live, please remember to delete test responses.
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