TLDR; A demo video on how to use tags effectively

A video walkthrough of working with tags on UserBit

There are a number of ways to analyze interview data. However a lot of the methods like affinity diagrams and empathy mapping don't scale as our data grows into 100s of interviews. In this post we will go through how we can use tags to leverage a scalable analysis of user responses.

Analysis with tags

Tags are used to classify and code text data. On UserBit you can add tags to:

  • Users
  • Notes
  • Highlighted text in user responses
  • Highlighted text in video, audio and general notes

Let's call User and Note tags, segment tags going forward - they play a vital role in slicing/dicing analytics. Let's look at them in a bit more detail.

User Segment Tags

User 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?

Then 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

Analyzing data after tagging is complete

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:

Analytics screen on UserBit platform

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 tag - TS: 11-15.

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.

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

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