A fundamental problem with mainstream methods (affinity / empathy mapping) of synthesizing research data is that they don't scale. As our data grows into 100s of interviews and posts, affinity diagrams and maps often become incomprehensible. In this post, we will go through how we can use an alternate method - tags for a scalable analysis of our research data on UserBit.


Tags are used to categorize and code text data. On UserBit, we have two main category of tags:

  • Segment Tags — tags that can be applied to:
    • stakeholders
    • users
    • survey participants
    • Notes
  • Highlight Tags — tags that can be applied to:
    • Highlighted text in user/stakeholder responses
    • Highlighted text in notes and media section
    • Survey responses

Segment Tags

Segment tags are simply tags that help categorize our users. They could be any set of relevant attributes that help classify our user-base - age group buckets, team sizes, role types, etc.

Example of segment tags in a project

A way to come up with segment tags, is to first think about what kind of questions you might want to ask of your data. If your question is something like:

What are the biggest pain points of our users that are over the age of 50?

Here, you would want to have segment tags with age range buckets and tag your users/notes accordingly.

Highlight Tags

Highlight tags are used to mark relevant parts of user responses, feedback or observations. In order to figure out what tags should be created in a project, you should once again think about the kind of questions you want answered. Let's consider the same question as above:

What are the biggest pain points of our users that are over the age of 50?

If you want a ranking of pain points, each pain point should be its own tag so you can keep track of them individually.

You can either create a list of tags before hand for information you expect to find in your data, or can create tags on the fly when you are going through the data.

When set up, our tag section might look something like this

An example of tag organization on UserBit

Tagging interview data

Going through and tagging our interview data or notes on UserBit is as easy as it gets.

  • Navigate to the user interview or research note that you want to code
  • Add segment tags to the top section
Adding user tags is important for filtering analytics and getting specific answers
  • Highlight and add tags to any relevant text in responses and notes.
  • If you encounter important information that you haven't already created a tag for, you can simply create one on the fly. The new tag will appear under ungrouped tags. You can organize them at anytime by going to the tags section.

Leverage full-text search for tagging

Another powerful feature on UserBit that you should leverage when synthesizing your data, is full-text search. The search allows you to quickly view relevant search terms in context across interview responses, notes or feedback.

You can then tag search results in context!

Full-text search on UserBit

Auto-tagging based on keywords or phrases

One of the most powerful features on UserBit when it comes to coding data, is the auto-tag feature. Both interview responses and research notes can be bulk-tagged based on keywords or phrases.

Want to save even more time? Business tier users can run auto-tag across the entire project.

Analyzing data after tagging is complete

Guide to analyzing qualitative data on UserBit

In the analytics section, you can now quickly see patterns and priorities. On wakeup the analytics section shows the frequency analysis in each highlight tag group across the entire project. For example, you can immediately see the features that users are most requesting within your project:

This is where segment tags also play a vital role. What if I wanted to see:

the top feature requests of users who're in a team size of 10-15?

All I have to do, is filter the analytics by the relevant segment tag.

Analytics screen on UserBit platform

You can also use a combination of segment tags to fine-tune the results even further. For example, we can get results for all users who're in a team size of 11-15 with a management role.

Correlation Analysis

Equally important is to be able to analyze tag correlations. For this you can use the correlation tab in the analytics dashboard. There are two categories of tag correlation that you can leverage on UserBit:

  1. Highlight tag correlation with user segments - How many times were the tags used within a given user segment.
  2. Correlation between two highlight tag categories - How many times were the tags used together in an interview response, survey response or in notes.
Analyze tag correlation on UserBit
Analyze tag correlation on UserBit

UserBit makes analyzing qualitative text data across research projects a breeze. Give it a try and feel free to reach out to support@userbitapp.com if you have any questions or feedback.

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