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Conjoint Analysis Reports (PX)

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About Conjoint Analysis Reports

You don’t have to conduct analyses or customize your own reports when you’re conducting a conjoint. The reports you need to understand your conjoint analyses will be generated for you.

You can access your Conjoint Analysis Reports by going to the Reports tab and making sure you remain in the Conjoint Analysis section. No data or graphs will appear until you have collected the minimum number of responses required to do so.

Reports tab is selected along the top, and beneath that conjoint analysis is selected

Qtip: You can also get to reports by clicking Preview Analysis on the Overview tab.

Buttons in blue


The graph and table under the Summary section shows you the ideal package to offer, based on the preferences displayed by your respondents so far.

Feature Importance

The Feature Importance graph displays the influence a feature has when the respondent is choosing their preferred bundle. The higher the score, the more weight it carries in the decision-making process. This table compares each of your features’ importance.

In the example below, Location carries more weight than the other features, while Price seems to have the least importance to respondents.

bar graph named feature important/ location is the highest bar

Optimal Package

The Optimal Package table displays the most preferred package across respondents. Both preference and utility are taken into account when determining this.

In the example below, the preferred Location was Bermuda, the preferred Price was $1500, and the preferred Length of the vacation was 1 week.

Table called optimal package

Calculation Type

Calculation type dropdown in upper-left

In the upper-left, you can choose calculation types:

  • Preference Share
  • Utility Scores
  • Cost Analysis
  • Willingness to Pay
    Qtip: This option only appears if you’ve added pricing as a Conjoint feature.

Which calculation you select affects the graphs and data that appear after the summary. These calculations are explained in the following sections.

Preference Share

Preference share measures the preference that respondents had for each level of a feature. You will get a Preference Share graph for each of the features in your conjoint.

Blue horizontal bar graphs for each of the features - RAM, hard drive, processor

The preference share is the measurement of the probability that a level would be chosen over another (if all other feature components were held constant). The higher the percentage a level has, the higher a preference the respondents showed for that level over the others.

Utility Scores

Each feature gets its own Relative Utility Value and Average Level Utility graph. For example, since our conjoint is studying the preferred vacation package, our features are Location, Price, and Length.

In these reports, each level of a feature is compared to the others. Below, because Price is the feature, we see the different available prices being compared by utility scores.

bar graph and line graph labeled price

Relative Utility Value

The Relative Utility Value bar graph displays the measurement of preference for each level of a feature. The greater a level’s relative utility value is, the more it enhances a package by being present.

Using the example below, the price of $1500 is highly favorable. Any package priced $1500 tends to be the respondent’s first choice. However, the price of $5000 has a highly negative relative utility value. It tends to be respondents’ last choices when they’re choosing a price. This makes sense, since people generally prefer saving money with cheaper prices.

Bar graph with negative to positive values on the x axis

Average Level Utility

The Average Level Utility is the direct average calculation across respondents’ individual level utility scores. This line graph is the normalized view of the relative utility vales, and is useful in determining how significant a level is in contributing to a feature’s overall importance.

Average Utility line graph

Willingness to Pay

Qtip: This option only appears if you’ve added pricing as a Conjoint feature.

Calculation type dropdown in upper-left

When you switch your Calculation Type to Willingness to Pay, you will see a table for Price Difference. This allows you to compare how much your respondents are willing to pay for a level, and how this compares to the other levels in that same feature.

In the example below, we see the Price Difference chart for Material. Our Baseline is set to Leather, because we want to see how Recycled Plastic and Canvas compare specifically to Leather.

blue bar graph with a zero point in the middle bars are then positive or negative from

From the graph, we can tell that respondents are willing to pay $14.70 more for Recycle Plastic than Leather. This could imply the value of providing environmentally-friendly options to our customers. In contrast, respondents are willing to pay $2.30 less for Canvas than Leather.

Change the Baseline to change the level you’re comparing your other levels to. When we change the Baseline to Recycle Plastic, we see again that customers are willing to spend $14.70 less on Leather than Recycled Plastic. We also see customers are willing to spend $17 less on Canvas than Recycled Plastic.

Qtip: You can also click the corresponding bar to switch the Baseline. For example, if I clicked the Canvas bar at the bottom, Canvas would become the Baseline of the graph.

Cost Analysis

You can perform advanced cost analysis using the results from your conjoint study. With the cost analysis report, you can configure the cost associated with each conjoint level, and view a cost-benefit analysis chart. You can then view the optimal package based on your desired maximum cost along with the return on investment for your different conjoint levels.

Setting Up Cost Analysis

Before you can view the cost analysis report, you need to define the costs for your conjoint levels.

  1. Select Cost Analysis in the calculation type dropdown.
    selecting the cost analysis calculation type from the dropdown menu
  2. Click Set up cost analysis.
    the set up cost analysis button
  3. Enable Cost Analysis.
    enabling cost analysis in report settings and defining the costs
  4. Enter the Base cost, which is the baseline cost no matter which package is selected.
  5. Choose the Currency for your monetary amounts.
  6. Enter the cost for each level of your conjoint by typing in the box next to the level. You do not need to include currency signs here.
  7. When finished, click Save.

Using the Cost Analysis Report

When viewing your cost analysis report, the first thing you should do is determine your optimal package’s maximum cost. To do this, enter the desired maximum cost in the Optimal Package section. After setting your maximum cost, this section will display the levels that make up the optimal package.
the optimal package. adjust the max cost of the package using the plus and minus buttons

The Utility vs. Cost graph displays the different utility scores for your project’s levels against the cost of each level. A higher utility score means a higher amount of preference for the specific conjoint level. Generally, you will want to choose a package containing levels with a high utility while also trying to minimize the cost.

the utility vs cost graph

The ROI Measurement graph displays the Return on Investment (ROI) for the different conjoint levels. This graph measures the average utility score per unit cost for each level. Generally, you will want to choose a package that has a larger ROI for each level.
the roi measurement chart

Refreshing the Report

When you collect new data and visit the Conjoint Analysis report, a yellow banner will alert you that New responses have been collected since you last refreshed your report. This means the data you see may be from a previous batch of responses. This banner can be temporarily closed, but if you leave the page and come back without any data refreshes having taken place, it’ll pop up again.

If you would like to manually refresh and force the report to update, click Refresh in blue on the upper-right.

Qtip: The report automatically refreshes every hour. To the left of the Refresh button, it will tell you when the data was last refreshed.


Using the Export button, you can export conjoint data into a CSV format. Learn more about the process, what calculations are exported, and what the different column headers mean on the Exporting Raw Conjoint Data support page.

Qtip: Exported data will not contain non-conjoint survey data. See the Survey Platform Exporting Data support page to see how to export just your non-conjoint data.

Total Population & Segmentation

The Total Population dropdown you may see in the upper-right is related to a feature called Segmentation. If you collect survey data on demographics, segmentation is a great way to analyze how different groups of people have different package preferences, allowing you to optimize products for specific populations of customers.

To learn more about this feature, see the Segmentation support page.

Total Population dropdown expanded

Image Key

If your levels have images attached, there will be an image key explaining the level corresponding to each image. In addition, if you hover over your graph, you will see images in place of feature text.

hovering over a graph to see an image; box under graphs called image key

Changing Conjoint Report Display Names

You can change the display names of the features and levels of your Conjoint project so that your data is easier to visualize. This will not affect the feature and level names as they appear in your Conjoint survey.

  1. Click the gear icon on your report.
    the recode button in a conjoint report
  2. Enable Display Names.
    the report settings tab of a conjoint report
  3. Enter the text you want used in your report.
    Qtip: You can leave a field blank if you don’t want to change its display text.
  4. Click Save at the bottom of the editor.