Our Choice-based conjoint uses one of the most robust and sophisticated research methodologies (choice-based conjoint) to find the most desired combination of features for your product, service, or package. It takes into account all possible attributes and options within each attribute, calculates the number of unique combinations, and asks respondents to choose one package out of several presented side by side on screen. After several such tasks, the model gets enough information to learn about each attribute and option in comparison to the others. Once the survey receives enough responses for the model to be satisfied, you will be able to see the importance of each attribute, the strength of each possible package, and the incremental value that each option adds or subtracts from it, allowing you to build and fine-tune your perfect product or service.
For best results, we recommend a minimum of 750 to 1500 completes.
Conjoint Express vs. Segmentation
Use Conjoint Express when you need to minimize your costs by asking respondents to respond to the minimum number of screens. Conjoint Express provides real-time analysis, and it will learn about the preferences of your entire sample group (or a subset of it if you apply filters), but it won’t be able to tell you anything at the individual level of each respondent.
Conjoint Segmentation is a more expensive option, since it will ask respondents to go through approximately twice as many screens to better learn their individual preferences. When a survey is finished fielding, it will take 5-15 minutes to crunch the numbers using the gold standard of the market research industry, Hierarchical Bayesian Modeling. Conjoint Segmentation will arrive at similar results as Conjoint Express, but with a much higher confidence. It will also look into clusters of respondents for you and automagically identify different personas, if such personas emerge from the data.
Setup is identical for both types of conjoint tests, so you can choose either Conjoint Express or Conjoint Segmentation any time, right up until you’re ready to launch your survey.
In the Survey Editor
As you add Attributes and Attribute options, and change the number of packages per screen the number of screens presented to respondents (and thus, the number of questions used per research test) will update accordingly.
If you would like to look at the design sheet that our platform will produce for you, you are more than welcome to process and download it to analyze on your end. If you happen to have a design sheet created on another platform, you can upload it to your research test here.
Learn more about programming Conjoint Tests here.
Respondent View
When respondents come to the Conjoint question in your survey, they will be asked to complete a series of comparison tasks. The number of tasks per respondent will be dependent upon the number of attributes, attribute options, and packages per screen. See how it looks for yourself!
Results page
Conjoint Express and Conjoint Segmentation come with a live simulator visualization built into the Results page. Express will populate it once there is enough information to build a model. Please pay attention to the warnings that will alert you if the sample size is too small for drawing conclusions. Conjoint Express will update the findings every time new responses are available on the page.
Since Conjoint Segmentation data analysis takes a few minutes to process, it will be initiated automatically only when the survey is fully completed and out of field. During fielding, once at least 400 responses are collected, you’ll have an option to manually initiate the analysis cycle and see interim results; however, we recommend waiting until the full data set is available and analyzed.
By default you will see an average package identified by the model. Click the Best button to roll all columns up to show the best possible combination of the considered options, or the Worst button to roll all columns down to the worst possible combination. If some of the options are truncated you can temporarily hide a few neighboring columns to read the full name of the option.
The importance level of every attribute identified by the model is visible in the table, and is expressed through the relative height of the columns in the visualization. The higher a column, the greater impact on the desirability of the package its options will have. Sorting the table by importance will update the visualization.
Additional Conjoint Segmentation Information
In Conjoint Segmentation, we automatically conduct sophisticated cluster analysis of your data, and our algorithm will connect emerging subsets of the sample with other respondent information (such as traits) as well as their answers to other questions in the survey. This is a live customer persona generation engine, which will label personas with a hypothetical name and a photo, to make it easier to distinguish and navigate among them. Our engine approximates the most prominent personas in your sample group and shows the package that is perfect for each of them.
Please bear in mind that we’re not operating in terms of clear-cut filters. When we list the gender, age, and other traits under a persona, it doesn't mean that everyone in this cluster falls within this description; it tells us that these traits were more prevalent in this cluster, statistically speaking, and were best-suited to describe the group. Another explanation or description of the persona may exist outside of the dataset, unavailable to our algorithm, so you may want to consider bringing everything you have into the survey. We’re happy to assist if you’re surveying your existing customers, for example, and would like to add your existing transactional background information into the experiment.
The numbered icons across the top of the graph let you toggle among each persona and the cumulative sample. Hide and expand the persona description section to manage your screen space; and of course, you can export the findings for each persona separately.
Toggle between Preference likelihood (the likelihood/probability that a particular profile (combination of attribute levels) would be selected over the theoretical "average profile") and Utility Scores (raw coefficients), which are used to calculate the market share, probability impact, package strength, etc. See an example of results here.