Are Your Research Insights Distorted? How to Make Them Crystal Clear

I finished a successful research project and was excited that I'd uncovered some great insights. I was so happy about this study that I sailed through the deck. There was no staring-at-a-blank-page syndrome, and the insights flowed nicely.

The shareout went smoothly. I presented the insights, and my colleagues seemed inspired by them. Then, there was some discussion of the following steps, which included fixing a few key issues and moving forward with some new ideas.

About a week later, I was in a team meeting when one of my colleagues reiterated one of my findings and the next steps for the team.

My jaw dropped. Not only had this person not understood the insight's intent, but they had also taken it as a solution rather than a problem space. I scrambled to explain the original insight and that it wasn't a solution for the problem I uncovered.

Why does this happen?

I clearly hadn't done my due diligence in helping my colleagues go from my findings to the next steps.

All I knew then was that I had to minimize the chances of this ever happening again. So I went back to the drawing board to brainstorm ways I could avoid the distortion of insights.

As researchers, our insights are apparent to us. We were there for the sessions, so we understood all the different pieces of the context within an insight. A lot of information not in our presentation sits in our minds.

We also tend to operate from the problem space. Just because a user says they want a new feature does not mean making that feature is a good idea. Instead, understanding underlying pain points, needs, and goals can help us make better decisions moving forward.

However, many colleagues are action-oriented. Therefore, they want to understand the tasks necessary to fix the problem or improve the experience (and success metrics).

For a long time, I thought my job ended with presenting awesome insights to the team, but I was missing one of the most important pieces: activation. I had to go further to connect the insights to potential next steps or recommendations.

By skipping the activation stage, we risk distortion and misinterpretation of insights. Just because we have all this knowledge in our minds about the users doesn't mean everyone else can consistently hold that when trying to make decisions.

We can't share all of our knowledge or be at every meeting to "quality control" the next steps. So, how do we avoid this situation?

How to avoid the distortion of insights

When I started brainstorming ways to avoid the encounter I had just had, I wasn't sure where to start. My mind went straight to wishing I had an identical twin researcher so I could be in more places at once. I then considered putting even more context and information into my presentations.

Neither of these felt like realistic or sustainable solutions. I didn't have a twin, and putting even more information in my decks might make colleagues' heads spin. I knew I had to experiment and iterate. It took me a long time to find valid ways to avoid my insights getting twisted.

Here are some tried and true methods I consistently use:

Writing actionable insights

The first change I made with my insights was ensuring they were actionable for colleagues.

Now, actionable is one of those (annoying) buzzwords that says a lot without meaning anything—at least, that's how I felt. So, trying to make my insights "actionable" was a mystery.

Instead of guessing as I had in the past, I did a little internal research. Here’s what I did:

1. Asked my colleagues what actionable insights meant to them, so I could craft a definition for my teams
2. Asked colleagues what they needed from insights
3. Presented a collection of insights to colleagues and asked them what to improve upon to make them more actionable
4. Iterated based on their feedback and represented to see if I was going in the right direction, and to get even more feedback

With this information, I understood better what my colleagues needed and how to present more relevant insights. I then created a basic formula to ensure my future insights were easy for colleagues to act on.

Here’s the formula:

• The key learning. This learning may be an unexpected attitude or behavior. It could also be a problem or barrier your participant experienced.
• The why. Explain why a particular behavior or attitude is coming up in the research, or why a participant is facing a specific problem.
• The consequence. What does this particular insight lead to, or what impact does it have on your product or service? Explain what will happen if you don't act on this insight.

Let's look at an example. Imagine we were researching how people budgeted money for travel, and we uncovered that people struggle with this concept. Instead of just reporting that people struggle with budgeting money for travel, we need to go a step deeper:

Participants struggled with budgeting money for travel in their traditional banking apps. Specifically, they could not separate what money they had to save for specific situations (ex: travel, bills, rent). This frustrated participants because they had to do their budgeting through a different app, which they then had to research and find.

Even with this solution, they had issues with forgetting to log specific budget details or spending money meant for different situations. Ultimately, this caused participants to look for other banking solutions or move toward a more manual way of budgeting (ex: spreadsheets).

Read more about writing compelling insights (plus examples!) here.

Making recommendations from the problem space

Once I tackled writing better insights, I knew I could go further. Making my insights more actionable was an improvement, but sometimes my colleagues still felt confused about what to do next.

I first did internal research to understand what recommendations meant to my colleagues and what they needed. I realized recommendations combined the insight plus a potential solution. Recommendations aren’t necessary for every insight, but can help if you feel comfortable writing them.

My recommendations are much more like suggestions rather than blanket statements. For example, I rarely write prescriptive recommendations like "move the button to the left side" or "make the font larger." Instead, I work with the designer for these UX issues to make the best decision.

Instead, my recommendations gently guide and suggest potential next steps. Let's take the above insight example to create a recommendation.

Insight: Participants struggled with budgeting money for travel in their traditional banking apps. Specifically, they could not separate what money they had to save for specific situations (ex: travel, bills, rent). This frustrated participants because they had to do their budgeting through a different app, which they then had to research and find.

Even with this solution, they had issues with forgetting to log specific budget details or spending money meant for different situations. Ultimately, this caused participants to look for other banking solutions or move toward a more manual way of budgeting (ex: spreadsheets).

Recommendation: Focusing on helping people separate money into specific budgets will keep people on our banking app and reduce frustration.

Additionally, recommendations can take the form of How Might We Statements. So the same recommendation above would turn into: How might we help people separate money into specific budgets?

I use How Might We Statements when I can't pinpoint a specific and more helpful solution.

Activation workshops

The beauty of using How Might We Statements for these more complex insights is that we can bring them directly into activation workshops. An activation workshop takes a critical insight (or two) and begins the brainstorming process. A widespread approach is an ideation workshop.

I quickly realized that this was the number one part missing from my research process, and was the most important to implement. Yes, I could write the most compelling insights and recommendations, but that only mitigated part of the risk.

Especially with more complex insights, I typically hold an ideation workshop with the team to work together on moving from the insight to a solution we can test. Since we're all in the room together, it heavily reduces the potential distortion of the insight.

1:1 meetings, if necessary

As people are learning more about user research, it may be necessary to have 1:1 follow-up meetings after a presentation to dive further into the next steps.

Within these meetings, you can help colleagues understand the insights and how they can act on them, sharing more context than in the presentation. This meeting is also an excellent time for your stakeholders to ask questions and clarify anything confusing about the insights.

After some time, I tried to move away from 1:1 meetings as they can take up a lot of time. However, when someone joins the organization, is unfamiliar with research, or there are very complex insights, I will still utilize this approach.

Biases and distortion of insights are expected. We are human, after all. While we can't completely stop these instances from happening, we can make an effort through the above steps to mitigate the risk. Additionally, improving insight writing and working more closely with your team through workshops is a great way to level up your career and make sure your work has an impact!