## Conjoint Analysis.

The choice simulator is one of the main objectives of choice-based conjoint analysis. This allows you to predict the effect of different scenarios on preference…

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There are many survey platforms that do not come with their own built-in choice-based conjoint question type. This then poses the question of how to…

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This one is a bit more complicated than the first five techniques we’ve talked about, but the idea of this technique is to find people’s…

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The value equivalence line is a useful concept for setting pricing strategies in markets where products vary in terms of their overall levels of benefits…

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This post explains the basics of computing willingness-to-pay (WTP) for product features in Displayr. Step 1: Estimate a choice model with a numeric price attribute…

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It shows how likely people are to make purchases at different price points. There are lots of different ways of estimating demand curves. In this…

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Reordering attribute levels in a conjoint analysis model can make results easier to interpret. For example, setting a standard option as the baseline in your co...

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In practical terms, the sample size is reduced in proportion to the frequency with which the “None of these” option is chosen. A way to…

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Conjoint analysis allows us to make predictions about the future. This post walks through the 7 stages involved in checking a choice model.

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In this post, I compare the ways Displayr and Sawtooth implement the Hierarchical Bayes (HB). I used both an in-sample data and holdout data for testing.

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Creating the simulator Create a choice model of the conjoint using hierarchical Bayes (HB), latent class analysis or Multinomial logit in Displayr (Insert > More…

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This post describes four methods for adjusting choice simulators from conjoint studies so that they better fit market share: change the choice rule, modify availability,…

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Choosing whether to use a numeric or categorical variable in a Conjoint Analysis is a difficult decision to make. With Displayr, there is a straightfoward way t...

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Example: choice-based conjoint analysis utilities Consider the utilities plot below, which quantifies the appeal of different aspects of home delivery. If you hover over the mouse plot…

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Step 1: Set up and estimate the choice model treating all the variables as categorical Start by setting up the choice model keeping all the…

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The difference between a numeric and categorical price attribute The chart below illustrates the the implications of treating price as being categorical versus numeric. When…

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Please read How to Use Hierarchical Bayes for Choice Modeling in Displayr prior to reading this post. There are a number of diagnostic tools that you…

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Analyses of the data from conjoint analysis should take into account the uncertainty that we have about the estimates of people's utilities.

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There are many ways you can increase the chance that the forecasts from your choice model are accurate. In this post, we take you through 12 of them.

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This post introduces the key concepts in designing experiments for choice-based conjoint analysis (also known as choice modeling or CBC).

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While choice-based conjoint analysis represents one of the more sophisticated techniques used in market research, presentation of its results commonly consists only of a simulator,…

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The main output from conjoint analysis is typically a choice simulator. However, substitution maps better show the underlying preferences.

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Indifference curves are a way of showing relative preferences for quantities of two things (e.g., preferences for price versus delivery times for fast food). This…

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Working out the sample size required for a choice-based conjoint study is a mixture of art and science. The required sample size depends on many factors.

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This post gives you ten top tips for writing your questionnaire for your choice modeling study. It's a must read for all those conducting choice-based conjoint....

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Want to know when and why you would do choice modeling? Discover all about the main applications of choice modeling and choice-based conjoint here!

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Choice modeling can be tricky. Luckily we've covered the basics so you can learn everything choice modeling and choice-based conjoint analysis.

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Alternative-specific choice model designs are used where alternatives are described by different qualities, rather than all attributes being the same.

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You can use either hierarchical Bayes or latent class analysis to do choice modelling in Displayr, making it easy to create your designs.

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Running a survey can be time-consuming and costly. Check your choice model experiment design using simulated data to save yourself the expense.

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In partial profiles designs, a specified number of attributes are held constant in each question. This reduces the cognitive effort for respondents.

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D-error is way of summarizing how good or bad a design is at extracting information in a choice model. Find out how to compute it in Displayr.

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D-error summarizes how good a design is at extracting information in a choice experiment. Find out how to compute it fo a Sawtooth Software CBC Experiment.

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Alternative-specific choice model designs are used where alternatives are described by different qualities, rather than all attributes being the same.

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In this post I explore the implications of using hierarchical Bayes versus using latent class analyis for data which contains segments.

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Using Hierarchical Bayes for Choice Modeling doesn't have to be difficult. I'll show you how to do it easily in Displayr.

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Assessing design quality in terms of balance can involve hundreds of numbers. What if I showed you a way to use a few key metrics to summarize your design quali...

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A worked example is used to show how to create an online choice simulator (conjoint simulator). Calculations are done in R & it is hosted in Displayr.

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