The most fundamental unit of a research insight is not a report, but a "research nugget". This is a combination of a learning or observation, evidence (e.g. a user interview recording), and tags that allow us to cut through the data in any way we like. Research findings, sometimes called "Insights" are synthesized from the collection of these "nuggets" (i.e. "learning + evidence + tag"), which are stored in a single system of record, such as NEXT.

To make sure you can productively synthesize your research with your team, it's important to have a strong taxonomy that defines what and how to tag. If you make wrong decisions on your tagging taxonomy, there's a risk of losing an overview of your research, making it extremely hard to interpret it and act on your insights. So what's an effective taxonomy? Let's take a look!

Rule #1: Limit your taxonomy to 30 tags per stage of your project

Once you get into tagging, it's easy to create many tags – they're free after all! However, before you lose track of what's important and how to tag your data. Limiting the number of tags to 30 (per stage) makes the tagging process faster and much more effective. More tags will likely just increase the time it takes to tag, make mistakes in how to tag, and lead to confusion in the team. The key is to keep it simple, but contextual.

☝️ If you feel that you need more than 25 tags to cut through your data, you're like mixing too much data in a single stage. We recommend splitting data, for example a "problem research" stage and a "prototyping" stage.

Rule #2: A clear definition of each tag

It's important to agree with your team on what each of your tags represents, so it's easy to find back your highlighted data. You know how the song goes: "Potato - Potatho, Tomato - Tomahto" 🎶

Here are a few ideas on how to define your tags:

1. Experience: Any tag that describes the participant's (e.g. interviewee's) experience. These could be pains they described, needs they expressed, frustrations with the current experience, etc. Think about tags that represent emotions, observations, or actual well-articulated insights (e.g. "cannot log in").

2. Service: Tags related to service design, like the user journey. For example, what are the touchpoints in the journey users struggle with? Who are the key stakeholders in the journey?

3. Demographics: Any tag describing the user's demographics, such as age, gender, geography, the organization they work for, etc.

4. Product: tags that outline the (part of the) product a highlight is related to. For example, users might be struggling with a specific feature or flow in the product you offer them.

Being diligent about your tagging definitions is very important! Before you get started with analyzing your research data, organize a meeting with your team to create your own "tagging taxonomy".

Rule #3: Make your tags "universal enough"

Imagine you have tagged 10 interviews. At some point, you will likely start to identify patterns in your data. In this case, it's important to not use tags only once or twice (i.e. use different tags for very similar findings). Instead, think thoroughly about how you can make a tag "universal enough", so you can apply it across your interviews or user tests.

A short example: let's say your interviewee mentioned she struggles with the process of installing your app on her mobile phone. A great tag would be "Installation process" and you color it red to indicate it's a struggle. However, if you would call it "mobile app pain", it would be much more difficult to understand the tag, and consequently find back any highlight that belongs to it.

Rule #4: Color-code tags to add another dimension to them

Making your data look beautiful and colorful is great. However, that's not the only value in the ability to color tags (and with that, highlighted data). Instead, colors can be used in many smart ways and will ultimately help you organize your tags and more easily consume your data. They allow you to quickly scan through your highlights and make sense out of them without the need to read everything in detail.

Here are a few ideas on how to color-code tags:

Represent tag categories with colors: Categorizing your tags makes it easier to find back "semi-related" data on your highlights page. For example, if you're trying to synthesize user pains from a couple of interviews, creating a category "user pains" with a red tag allows you to find back all highlights that represent pains more easily. Similarly, if you tag all follow-up actions with a brown color, you and your team will be able to find follow-up actions much more easily.

Signal sentiment with colors: If you found something that seems very important, or your interviewee became very emotional, we recommend using colors that represent that emotion. For example, use red for anger, yellow for neutral, green for happiness, blue for calm, etc. Include how you use colors in your tagging taxonomy with your team to make sure you're all aligned.

Rule #5: Double tag data to provide additional context

When you're tagging a piece of data, a single tag to communicate the meaning of that data is often not enough. In that case, we recommend tagging that data with multiple tags. For example: let's say you're a Podcast App company and you're trying to find out why users don't use your app offline. In a user interview, your interviewee mentioned she struggles with findings the feature to download podcasts to her phone. You can highlight this statement with three tags: 1) "Download feature" and 2) "User pain", 3) "Offline usage". This will make it easier to find back this piece of data and understand the meaning behind it through tags, without having to read the context of the interview.

Conclusion

It's safe to say that having a well-thought-through tagging taxonomy is important to work with your data productively and maintain an overview. If you follow our 5 rules described in this article, you're much more likely to become a research superhero with NEXT! If you neglect your tagging taxonomy as a team, it will make your life much more difficult and providing your organization with reliable and evidence-based insights harder than ever. A final recommendation would be to not set these rules aside or engrave your taxonomy in stone. Instead, review it with your team regularly to make sure everyone understands how to tag, what to tag and how to organize tags.

In case you have any questions about tagging (or NEXT in general), feel free to reach out to your Customer Success representative!

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