Day 13 - Turning User Research into Insights
Understand what your users really mean...
Hello learners!
Welcome to the 13th lesson of the series 30 Days of PM by Crework! We have talked about user research in depth and now we will talk about how to use that research to come up with some insights.
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User Research Analysis
Talking to customers is great, but most people walk away feeling overwhelmed by the sense of more information than they know what to do with. Learning how to properly analyze UX research helps turn raw data into insights and action. That’s why we do User Research Analysis!
User research analysis is the act of making sense of what was learned in user research so that informed recommendations can be made on behalf of customers or users.
Conducting user research analysis basically means spending time categorizing, classifying, and organizing the data that has been gathered to directly inform what will be shared as outcomes of the research and the key findings.
But, why do we need to do this?
People believe that they can remember things that they heard in the user interviews, but they often don’t. Then, what happens? They take decisions based on only the things that they remember. Recommendations based on a single data point can lead a team down the path of solving the wrong problem.
Also, simply going with your instincts means you are reacting to data, not trying to understand it.
Performing the necessary analysis of user research data is an act of asking “why” to data points. Analysis transforms the research from raw data into insights and meaning.
Imagine if the data showed: “6 out of 10 people had difficulty signing in to our application.” On the surface, a reasonable recommendation could be to redesign the sign-in form. However, proper research analysis and finding the meaning behind what that data represents is when the real magic happens.
Perhaps the reason people had trouble signing in was due to forgotten passwords. In this case, redesigning the sign in form wouldn’t necessarily solve this problem.
When should you do the analysis?
Great analysis starts before research even begins. This happens by creating well-defined goals for the project, research, and product. Creating clear goals allows researchers to collect data in predefined themes to answer questions about how to meet those goals. Before any research session begins, craft clear goals and questions that need to be answered by the research.
Most of the analysis happens after the user research. At this point researchers are reviewing all the notes they’ve taken to really figure out what patterns and insights exist. Most researchers will have a good idea of which tags, groups, and themes to focus on, especially if they’ve done a debrief after each session.
It then becomes a matter of determining why those patterns and themes exist in order to create new knowledge and insight about their customers.
How to do the analysis?
User research analysis in simpler words basically means - going through all the data and finding patterns in it. And how do we do that? By associating all the data with specific tags and finding similarities in data that have the same tag.
Imagine the research goals for the project are:
Increase the number of people signing up for our product free trial
Increase the number of people going from free trial to a paid account
Educate trial customers about the value of our product prior to signing up for a paid account
From there, research questions can be formed such as:
“Does the website communicate the right message to share the value of a free trial?”
“Is it easy for a new customer to sign up?”
“Are new customers easily able to start a free trial and begin using the product?”
From those questions, we can extract topics and themes. Since we’re researching the free trial, sign up process and general usability of that process, they become clear choices for tags. Some useful tags based on those questions would be:
#free-trial
#value-prop
#signup-reason
#signup-process
#onboarding
#confusion
Now, your job is to tag data from the conversations with these tags and then group them together.
Once all the research is done, it’s time to dig in to find patterns and frequency across all the data gathered.
Review those tags and codes to find relationships between them. A useful tip for this is to pay close attention to tags that have notes with multiple other tags. This often indicates a relationship between themes. Once you analyze the data well, you will be able to come up with key insights.
There are three parts to creating a key insight from user research:
Statement of what you learned
Tags that describe the insight (often used from the analysis, but can also be new tags entirely)
Supporting notes, data, and evidence that give further context to the key insight and support the statement of what was learned
A key insight from the example project might be:
“Prospective customers are worried they might not have enough time to review our product during the free trial.”
Day 13 - Completed ✅
Congratulations on completing the thirteenth lesson of the series. 🥳
Now, you know what to do. Share your learnings with the world and be accountable.
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