Segmenting Your Survey Analysis
Segmentation is a powerful tool that allows you to break down survey results by specific respondent groups, such as demographics, user types, or behaviors. This makes your data easier to interpret and significantly more actionable.
When analyzing large data sets, segmentation helps identify:
Patterns and trends across different audience groups
Outliers and anomalies in feedback
Opportunities for targeted product or service improvements
Without segmentation, raw survey data can quickly become overwhelming and difficult to contextualize, especially when trying to understand why different respondents feel differently.
Enabling Segmentation with Questions
To enable segmentation, your survey must include at least one single choice type question.
These types of questions are automatically analyzed on the Survey Results page, without requiring a deeper view. They also serve a dual purpose: they can be used to segment your respondent base.
Example:
If a multiple choice question asks about industry or location, BoundaryAI will automatically group responses into segments based on the answers selected. These segments will then be available as filters in the results dashboard and for comparative analysis across questions.
Segmentation During Data Import
Segmentation is equally critical when importing survey data from external platforms. When segmentation variables (like demographic info, user type, or prior behavior) are included as columns in the data file, BoundaryAI will:
Recognize these fields as segmentation variables
Allow you to filter and compare responses across these fields
Generate side-by-side visual comparisons in the dashboard
Available Analysis Views
Quantitative Questions (e.g., multiple choice, checkbox, linear scale): Displayed directly on the main results page with bar charts or distribution graphs. Segments can be applied to compare how different groups answered.
Qualitative Questions (e.g., long answers, NPS): Have dedicated in-depth analysis pages. Segmentation can be applied here as well to surface trends in open-text responses or satisfaction scores across different groups.
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