Knit Bell Icon | Knit Video Insights Platform & AI Analysis Solutions

NEW: Unparalleled speed-to-insight with Knit’s newest release of AI Contextual Analysis™ for VoC Video & OE Responses

Tagging Taxonomy:

Coding Qualitative Data

As brands seek to keep up with their consumers’ demands, needs, and desires, they’ll need to look far beyond the scope of the occasional quantitative survey or market trend report. Brands now have the opportunity to hear real thoughts and feedback from their target customer base. With the emergence and growing popularity of video research, brands are now equipped to collect hundreds, even thousands, of real consumer videos providing feedback on their products and ideas. Qualitative data is more accessible than ever! The new challenge? Analyzing it to make it actionable. Coding qualitative data can help quantify the insights collected in video research.

The right tagging taxonomy is foundational for a more comprehensive analysis of this qualitative data. Deployed effectively, companies can understand consumers’ experiences with their products, look for gaps in the market, and ensure their customer service is not keeping potential clients away. However, developing, deploying, and maintaining a framework to analyze qualitative data can be, well, kind of a drag. It takes time to do it right, and it’s an iterative process to boot.

Which leads us to our first big question for brands:

What are your goals for collecting qualitative data? 

When we collect qualitative data from our customers and consumer base, we are looking for information more complex than can be contained in quantitative data alone. Customer surveys, complaints, and correspondence can provide important insights that help companies analyze patterns and make meaningful connections. However, this data can be much more difficult to sort than quantitative data; by quantifying this qualitative data, then, we can sort abstract, complex customer responses into easily navigated categories.

What is tagging taxonomy? 

The most efficient way to begin quantifying qualitative data is to establish a tagging taxonomy. “Tagging”  applies keywords to data with similar themes — for example, “shipping error,” “refunds,” etc. One of the most common ways companies utilize tagging taxonomies to quantify their qualitative data is to sort through marketing surveys or customer service tickets. 

For example, you may use the following tags to search for patterns in qualitative data:

  • Late delivery
  • Unhelpful customer service agent
  • Slow response time
  • Discount code
  • Charged twice
  • Long wait times
  • Coupons
  • Damaged package

A “taxonomy” is simply a method of sorting or categorizing large data samples. Applying a taxonomy to your “tagged” correspondence can help you to sort through common issues, questions, complaints, and queries from a wide range of customers. 

Tagging without establishing a taxonomy

What happens when companies tag their qualitative data without first establishing a tagging taxonomy? If you’ve ever had to sort through overlapping, repetitive tags, you already know: headaches happen. And it’s tough to perform a meaningful analysis at all, let alone efficiently. In order to avoid wasting time on creating disorganized, unmanageable tags, it is important to first decide how you will build your tagging taxonomy.

How to build an effective tagging taxonomy

To build your tagging taxonomy, you must first determine which method best meets the needs of your company. Depending on the volume of your customer correspondence, the variety of marketing data compiled, and the size of your customer support staff, you may have specialized needs for your tagging taxonomy.

The two main tagging taxonomies utilized by companies to perform qualitative data analysis are flat tag taxonomy and hierarchical tagging taxonomy. Let’s break them down:

Flat tag taxonomy

A flat tag taxonomy is the simplest form of tagging taxonomies. In a flat tag taxonomy, all tags are on the same level (or “unlayered”). In short, this results in a simple list of tags. Flat tag taxonomies may be easier to use and analyze for smaller companies with a lower rate of consumer data. 

For example, let’s look at how we may “tag” respondent data to the question: “What type of marketing would make you want to attend a Major League Soccer (MLS) game?”

The data set example here shows how a flat tag taxonomy system could be applied to customer responses like these below: (only 5 snippets of 241 respondents shown here)

  • “free drinks and food”
  • “meet a player before the game”
  • “cheap hot dogs”
  • “promotions such as discounts on tickets”
  • “a celebrity that was a really important to the team attend the game”

By applying the flat tag taxonomy system to this data set, your brand could [hypothetically] now build its strategy around this prioritized list, synthesized from broad and unorganized qual feedback.

Hierarchical tag taxonomy 

In a hierarchical tag taxonomy, tags move from general, abstract first-level tags to more specific second and third-level tags. In order to build a hierarchical tag taxonomy, you will:

  1. Map out high-level tags
  2. Break down the high-level tags into smaller categories
  3. Break these tags down into third-level tags

While this process is more time-consuming than establishing a flat tag taxonomy, the results are much more streamlined, specific, and organized. It also allows you to easily sort new tags under existing categories as your data pool widens.

Within this structure of tagging, we’ll now have more actionable grouped data about what could attract our Gen Z respondents to MLS events. We can now see how important experience is collectively when “Player Interactions” and “Behind the Scenes” access are now grouped within the hierarchical tags. 

Initially, it can be relatively simple to manually tag your qualitative data and obtain meaningful insights. With continued growth, increased customer correspondence produces larger sets of consumer research data becomes, and coding qualitative data with a hierarchical tagging taxonomy helps to analyze it at scale.  

We get it. Adding up the opportunity cost associated with creating a tagging taxonomy from scratch can feel downright masochistic, no matter how mesmerizing our sunburst visualization .GIF is (very).

So, why not let Knit do it? Tagging taxonomies that help quantify qualitative data? That’s our whole thing! Get in touch today to learn how we can streamline qualitative analysis for your company. We’ll even make a mesmerizing sunburst visualization .GIF just for you.

Subscribe for Gen Z insights delivered weekly.

Get access to our free newsletter to stay a step ahead on Gen Z's latest trends driving the industries of tomorrow.

Share This

Enter your email below to access this video

Enter your email below to access this video