Each research project starts with a research goal. However, most of the time you will discover a lot of valuable insights that are not about your goal. It’s even possible that these insights have more effect on the actual user experience than the ones addressed by your research questions. But how do you capture all insights and not only the ones regarding your research questions? You can’t capture everything and if you can, you will get an overload of information. How do you manage this in order to discover patterns across study findings?
Know your own perspective and broaden your view
Defining the research goal and questions and writing down your own assumptions is a good start for every research project. Even if you know that your “assumptions” are true or are no-brainers, just write them down to be more aware of them. After doing this, take a closer look at the goal of your research project. Is something like improving the check-out flow a goal or a desired outcome? Probably the end goal is to make more profit.
This immediately helps you to think more strategic; maybe the check-out is not the problem, but the USP’s are not clear enough. When you sum up all the possible factors that could have an effect on the end goal, you instantly broaden your perspective. Keeping these factors in mind during your research will help you to be open to findings that are valuable, even though they are outside the scope of the original research plan. You unconsciously want to exclude or prove these factors during your research and that’s why you will notice more.
Don’t forget to check this with your (internal) client before you focus on the wrong things.
Discover patterns over multiple studies by using tags
More and more organizations focus on documenting insights/nuggets instead of writing reports in order to get an overview of all the research that is done. With this way of working, you’ve got a lot of insights when you’re finished with your research. You need to tag these insights to make them accessible and searchable.
When you have a fixed set of tags for your insights and use them consistently across teams and departments, you will get valuable overviews. Thereby, you’ll also create a shareable and searchable knowledge base for the entire organization. With these overviews per tag you are able to discover patterns across multiple studies. This could lead you to the bigger picture. Because all the by-catch of all studies could be more important than you think. When some issues keep showing up in different studies (without even focusing on it), it’s possible it’s a bigger issue than it seemed beforehand. And don’t forget to link your observation/insight to the study it comes from. It will be easy for you to make a strong case of the patterns you have discovered because you can refer to all studies.
So how to tag your insights?
Deductive or inductive tagging
When you do qualitative user research, there are two ways to tag your notes: deductive or inductive. Deductive tagging means determining tags before you start taking notes and inductive means determining tags afterwards. There is no good or bad way to tag your research notes as long as you do it consistently.
Deductive tagging: start with categories
To determine tags before collecting the data, you’ll need to team up with your research colleagues and preferably also with other roles that outsource research (product owners and marketers). You’ll need to agree on which tags to use in order to create valuable overviews. See these tags as a shared language to make sense of all observations across departments and teams. Deductive tagging is the most useful way of tagging for clearly defined projects, because you know what you can expect to a certain extent.
There is no such thing as a tag category list every organization needs to use, because no organization is the same. But some overviews and labels will be relevant for most of the organizations like target group. Use this list as inspiration for your tag categories & tags:
Tags for your organization
- Products or services (webshop, app, store)
- Page or section (detail page, check-out, cash desk)
- Target group (prospects, persona, consumers, companies)
- Team/department (UX, marketing, customer service)
- Discipline (copy-writing, design, development)
Tags for your user/customer
- Life events (marriage, moving out, first job)
- Journey step (orientation, browse, buy)
- Features or actions (filters, search, delivery, editing)
- Themes (Christmas, home, family)
Tags for your research
- Device (desktop, mobile)
- Context (online, offline, social media)
- Research methods (analytics, interviews, survey)
Inductive tagging: divide tags into categories
PostNL for example, started with inductive tagging their qualitative data from user research. After a while, they had a long list of tags with a lot of synonyms. They looked at which tags were used the most and which could be combined or removed. The most difficult task is to decide which labels to use, because all researchers have to understand when to use which tag. If you already have a list of tags like PostNL you can collectively choose between labels that already exist. Although, it’s more efficient if one person takes the lead by proposing labels (based on the existing list) and others can give their feedback.
Inductive tagging works better than deductive tagging if you do an exploratory research project, because you don’t know which themes will emerge. This will also help you to stay objective when taking notes.
Less is more; which tags are really necessary?
When you start thinking about useful tags, there is a high chance you will end up with a lot of them. But beware, to be complete is not what you’re aiming for. You just want to create valuable overviews of your data to gain new insights. So look at all your tags and ask yourself:
- Will I gather enough data about this segmentation that I will need an overview of it? Or is this subject too narrow or too specific?
- Do I need this variation of a certain subject? Or are the variations so small there will be no significant difference between the insights you will gather?
- Is it really necessary to get an overview about this subject? Or do I only use it as a label, (like iOS or Android) but I won’t get interesting insights about this?
Practice makes perfect
Probably, the first version of your tag list will not be perfect. Just like improving the user experience of your product or service, you’ll have to test what works for you and your colleagues. Eventually, it’s a way to communicate between departments/teams so it’s better to choose labels that everyone understands than choosing the most correct one.
Good luck with tagging 😉