Depending on the nature of your project, a simple A vs B comparison may not be suitable for your insights. When dealing with a group of three or more data sets, NOT comparisons are a useful tool for analyzing the data.

In this article:

What is a NOT comparison?

'NOT comparisons' are commonly used when a project involves more than two data sets as a way of identifying what makes a particular data set unique in comparison to all of the others grouped together.

NOT comparisons involve comparing a specific data set against a group of others combined and is constructed as (X) vs (NOT X).

For example, when considering age groups, you could compare 18 - 24 year-olds versus NOT 18 - 24 year-olds (i.e. all other age groups).

Common use cases

This approach can be useful whenever you are working with more than two data sets. Some common examples include:

  • Analyzing one brand against a group of competitors

  • Isolating a particular audience group (e.g. age group, geographic group) and comparing it against all others

  • Comparing NPS responses from promoters against detractors and neutrals

Creating NOT comparisons

  1. Navigate to the Data Library and open the relevant project folder (or create a new project and upload data).

  2. To create a NOT comparison including only a selection of the data sets, organize it into relevant folders. To do that, click 'Create new folder' from the bulk action menu, then give it a name and click 'Create'

  3. You can then use the checkboxes to select your data, click the three dots icon in the bulk action menu, select Move and choose the correct folder.

  4. Hover over the single data set you want to isolate from the others and click the two circles icon to compare against all others

  5. You will be brought to a preview of the comparison. Click 'Save' to save this comparison to the project

  6. The combined data set will be saved within your Data Library using the format 'NOT ...', and can be used in other comparisons as needed

To prevent the analysis of duplicated data when creating additional NOT comparisons, any NOT data sets created previously will be excluded.

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