TLDR; A demo video on how to use tags effectively
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 currently add tags to:
- Highlighted text in user responses
- Highlighted text in Notes
Let's call User and Note tags, parent tags going forward. Parent tags play a vital role in slicing/dicing analytics. More on this later.
Parent tags are tags that help categorize our users. Good examples of parent tag groups are team size, role, age, frequency of use etc.
Within each of these parent tag categories you can have multiple tags, for example within team size we might have:
- TS: 0-5
- TS: 6-10
- TS: 11-15
The TS in the text is simply there to facilitate quick lookup when we are in the process of tagging our users.
Highlight tags are used to categorize user responses, feedback or observations and the process of creating these tags can be both inductive or deductive.
In other words, we can either create a list of tags before hand for information we expect to find in our data, or we can create tags on the fly as we are reading through the responses or notes.
Some examples of tag categories for highlight tags are goals, frustrations, feature requests, etc.
Within each category, you will have multiple tags. For example for a food tracking app the tags within feature requests might be:
- FR: tracking water intake
- FR: reminders for data entry
- FR: track weight progress
Like with parent tags, the FR in the text is simply there to facilitate quick lookup when we are in the process of tagging our data.
When set up, our tag section might look something like this
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 we want to code
- Add parent tags to the top section
- Highlight and add tags to any text that is associated with our existing tags
- If we encounter important information that we haven't created a tag for, we can simply create one on the fly. The new tag will appear under ungrouped tags. We can organize them at anytime by going to the tags section.
Analyzing data after tagging is complete
In the analytics section, we 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 parent 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.
We can also use a combination of parent 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 if you have any questions or feedback.
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