Exporting as an Excel Workbook


Conjointly makes it easy to export different data from your experiments into Excel:

Export of summary analyses

Follow these steps to export the results of your Conjointly experiment to an Excel workbook.

1. Add outputs to export

Select which outputs you would like to add to export by navigating to each output and clicking on the “Add to export” button. Alternatively, click on the “Export analyses” button at the bottom of the page to open the analysis cart with respondent information, conjoint data, and data dumps automatically added.

add output to export

2. Open analysis export cart

Click on the Export analyses button at the bottom of the screen in the insights tab to open the analysis export cart. Here you will be able to see all the outputs you have added to export.

check the outputs in the analysis cart

You can also add respondent information, conjoint data, data dumps, and an interactive simulator through on the “Add more outputs” dropdown menu.

check the outputs in the analysis cart
  • Data dumps: Includes the worksheets (if available):
    • Questions
    • Item of questions
    • Stimuli in monadic block
    • Answers to questions
    • Answers to question items
    • Crosstab by segment
    • Diagnostic questions
    • Brands tested
    • Crosstab by item scores
    • Segment membership
  • Interactive simulator: Includes a worksheet to do simulations in Excel.

3. Export as Excel

Once all the desired outputs have been added to the cart, click the “Make Excel file” and Conjointly will compile the download file.

This process can take a few seconds. When the file is ready, click on the “Download it now” button on the sidebar to download the Excel workbook.

Export the PowerPoint presentation

Individual partworths for each respondent

To view individual partworths for each respondent, ensure that “Conjoint data” is included in your export cart.

In order to understand the meaning of these coefficients in a Brand-Specific Conjoint, let’s take a look at Example experiment 2 in your experiment list:

  • Column ASC: Opt-in is the reverse value of “None of the above” option. A higher opt-in value means that the respondent is less likely to select the “None of the above” option.
  • Preferences for brands are highlighted in columns B2 to B4. In this case, preferences for column B1 (Landrange Hoover) are not shown / set to 0 (because it is a dummy variable / point of reference for brands) [Note: In partworths utility form, the first variable is the baseline dummy variable / point of reference for the attribute. Utilities for other variables are based on the first variable. For example, compared to B1 (Landrange Hoover), the first respondent has -5.4 utility with B2 (Maruda Maru II), -5.3 utility with B3 (Kea Rocketta), and -9.9 utility with B4 (Ladina Kubnika). Learn more about dummy variable.]
  • Similarly, preference for levels are highlighted in subsequent columns. The columns highlighted below (B1A2L2 and B1A2L3) represent the preferences for price of Landrange Hoover, where:
    • B1A2L1 = Not shown / 0 (because it is a dummy variable / point of reference for prices)
    • B1A2L2 = Preferences for Landrange Hoover price of $23,000
    • B1A2L3 = Preferences for Landrange Hoover price of $25,000

To find the utility for “None of the above”, look for the “ASC Opt-in” column:


Answers to each question for each respondent

To download additional question answers for each respondent, make sure that “Respondent info” is included in the analysis cart when you export.

Each row represents one participant who responded to the survey. Respondents are identified through the ID in the row participant_id.

Note that this sheet includes respondents that were not included within the analysis. Respondents may be excluded for a variety of reasons, including low-quality responses or being disqualified for not matching the survey criteria.

To filter by respondents who were only included within analysis for the report, filter the column status_included_in_analysis to only show participants with a value of 1.

Filtering excel output by those included in analysis

Coding of individual Gabor-Granger results in Excel

After downloading the Excel export with “Respondent info” included, navigate to the Respondent tab in the worksheet. You can then view individual responses. Please note that:

  • The number value indicates the highest price that the respondent is willing to pay for the product.
  • If the qualifying question was not enabled and the respondent is not willing to purchase the product at any of the presented prices, the cell will show yes.
  • If the qualifying question was enabled and
    • The respondent is willing to buy the product (as per the qualuifying question), but not willing to do so at any of the presented prices, the cell will show yes;
    • The respondent indicated that they would not be willing to purchase the product at all (in the qualifying question), the cell will show no.
Interpreting the individual results from a  Gabor-Granger test in Excel

Acronyms used in the Excel report

You can find the meaning of the acronyms used in the Excel report below:

Tab: Experimental design

The experimental design is an important core component of all statistical experiments. It defines the choice tasks that respondents will see. You can think of these as the independent (or x) variables in the model, and respondents’ selections will be the dependent (or y) variables.

  • BLOCK: Design block, or version of the design. The experimental design contains several different versions. Each respondent is randomly assigned to one of these design blocks.
  • QES: Question, choice task number. Each block contains several tasks, usually around 12, each of which contains information on what respondents will see on a page.
  • ALT: Alternative, within a choice task (or question) there will be several choices shown to respondents, usually around 5. This variable indicates the number of choices in a question.
  • A1: Attribute 1, contains the level of attribute 1 that will be displayed for a particular alternative. There will be as many such variables, A2, A3, … as there are attributes in the experiment.

Tab: Design matrix

  • BLOCK: Design block, or version of the design. The experimental design contains several different versions. Each respondent is randomly assigned to one of these design blocks.
  • QES: Question, choice task number. Each block contains several tasks, usually around 12, each of which contains information on what respondents will see on a page.
  • ALT: Alternative, within a choice task (or question), there will be several choices shown to respondents, usually around 5. This variable indicates the number of choices in a question.
  • ASC: Alternative specific constant, a value between 1 and 0, where 1 indicates the row is an alternative (a choice in the task) and 0 if the row corresponds to the “None” option.
  • A1L2: Attribute 1 Level 2, if the value is 1, it means the second level of attribute 1 was shown. If it is 0, it means it wasn’t shown. If none of the levels of an attribute has a value of 1, it means that level 1 of the attribute was shown (this would be A1L1). There will be as many such variables, A1L2, A1L3, A2L2 … as attributes multiplied by levels minus the number of attributes (corresponding at the first level not shown).

Note: A1L1, A2L1, A3L1, and all first levels of each attribute aren’t added as columns, because it would cause multicollinearity with the combination of the other levels.

Tab: Raw responses

  • ID: participant_id who answered that specific row.
  • BLOCK: Design block, or version of the design. The experimental design contains several different versions. Each respondent is randomly assigned to one of these design blocks.
  • q1: Question, choice task number 1. This corresponds to the value 1 in the column QES of the Design matrix. There will be as many such variables, q2, q3 … as there are choice tasks in the experiment.

Tab: Model matrix

  • ID: participant_id who answered that specific row.
  • BLOCK: Design block, or version of the design. The experimental design contains several different versions. Each respondent is randomly assigned one of these design blocks.
  • QES: Question, choice task number. Each block contains several tasks, usually around 12, each of which contains information on what respondents will see on a page.
  • ALT: Alternative, within a choice task (or question), there will be several choices shown to respondents, usually around 5. This variable indicates the number of choices in a question.
  • RES: Response, this is the respondent’s selection, in this particular question, 0 indicates that the alternative was NOT selected, and 1 indicates that it was selected.
  • ASC: Alternative Specific Constant, a value between 1 and 0, where 1 indicates the row is an alternative (a choice in the task) and 0 if the row corresponds to the “None” option.
  • A1L2: Attribute 1 Level 2, if the value is 1, it means the second level of attribute 1 was shown. If it is 0, it means it wasn’t shown. If none of the levels of an attribute has a value of 1, it means that level 1 of the attribute was shown (this would be A1L1). There will be as many such variables, A1L2, A1L3, A2L2 … as attributes multiplied by levels minus the number of attributes (corresponding at the first level not shown).
  • STR: Unique index for the task.

Note: A1L1, A2L1, A3L1, and all first levels of each attribute aren’t added as columns, because it would cause multicollinearity with the combination of the other levels.

Conjointly Excel Plugin

Conjointly Excel Plugin

To improve your Excel charting experience, the Conjointly Excel Add-in is now available online. The add-in contains multiple functions that help with charting Conjointly outputs.

Some of these functions include:

  • The ability to automatically recolour charts from data cells, for quick and easy chart-making
  • A function that enables quick calculation of the price elasticity of demand between two price points
  • A button that transforms conditional formatting to static formatting with ease, allowing you to combine multiple forms of conditional formatting.

Download Excel Plugin