UX insights are the pot of gold at the end of the research rainbow. After you’ve met with stakeholders, set objectives, recruited participants, and conducted user interviews, it’s time to transform this pile of information into something actionable.
The key to good UX insights is their usability. They return the investment your company (and stakeholders) made in the research process with concrete intelligence that can direct the development of better and more successful products.
That puts a lot of pressure on the quality of these insights! The name “insight” implies cognition, suggesting that a sort of eureka moment must occur to make them viable. And they do require some space for deep thought. They’re a bit like that old saying that golf is a game played on a five-inch course – between the player’s ears. The same is true of creating great insights.
On the other hand, best practices can help streamline the production of great insights. Gleaning them isn’t as mysterious as it seems. Following a few key steps removes some of the guesswork.
What are UX insights?
UX insights are the actionable end-result of the user research process. They’re obtained by studying users' behaviors, needs, and preferences in relation to a product, service, or system, and then translating those studies into findings that can be used by designers, marketers, developers, and decision-makers. User insights can touch any portion of the user experience, including:
User needs (“What are the primary goals and motivations of our target users?”)
User behavior (“How do users currently interact with similar products?”)
Information architecture (“What content formats or types resonate best with our users?”)
Interaction design (“How intuitive and easy to use is our product?”)
Visual design (“What visual elements, colors, or styles are preferred by our users?”)
User satisfaction (“What are the unmet needs and potential areas for improvement?”)
That’s a lot of ground, so let’s set up an example we’ll return to later. Let’s say you’re helping design a new shoe-shopping app targeted toward young professionals. You’ve been brought in early in the process and many elements of the app are still fluid. The research brief asks you to help learn more about this demographic and their needs so they can be better incorporated into the finished product. The research may produce findings – which are factual observations grounded in the research – that you turn into insights, which map those findings to a broader understanding of the marketplace and business objectives and make them more actionable.
For example, one finding might be that a large quantity of young professionals didn’t like the visual design of a prototype. The insight might be that the user interface should reflect the aspirations of the audience, which they equate with cleanliness and minimalism. Another finding may be that the target audience had trouble picking shoes on the app. The insight may call for a better utilization of machine learning algorithms and user profiling, along with design flourishes that highlight more human recommendations, too.
Qualitative vs quantitative data in UX insights
Both qualitative and quantitative data can be turned into UX insights. As a reminder, quantitative data is numerical in nature – such as user data, analytics figures, and numerical surveys — while qualitative data addresses the underlying emotions and motivations behind those numbers – often through interviews and open-field surveys.
Qualitative data can help build a more in-depth understanding into users’ thoughts, behaviors, and experiences. It’s invaluable if the goal of the research is to create empathy with users by generating personas or to uncover unmet needs that wouldn’t be immediately apparent in hard numbers. Quantitative data, on the other hand, provides statistical significance of phenomena. It can help determine whether preferences are one-off instances or part of a larger-scale pattern.
Ultimately, the objectives of a given research project will determine the types of research techniques used, and ultimately the type of data produced. Frequently, the types of data can complement each other, providing both the “what” and the “why” behind user needs.
Setting yourself up to generate UX insights
If insights are the goal of the entire research process, it makes sense that their seeds should be planted early. You don’t want to get to the magical, transformative moment of synthesizing data into insights only to find new hurdles to jump over. Here are some steps you can follow.
Collect and organize your data
Make sure that you collected and organized data throughout the process in a coherent manner. If you’ve been following a small group of users, for example, ensure that each is saved in a research repository or has a clearly marked folder on a digital drive. This drive may contain their user-submitted diary while trialing a prototype of the app, as well as the automated transcription of any interviews or user testing sessions. It could also contain a brief intro to each user.
On the other hand, if you’re working with broader quantitative data, make sure that it’s all formatted and cleaned appropriately. In short, this step of the process is just a general gathering together of all of your research materials, but you can save yourself some trouble and get more quickly to the analysis if you’ve thought about this organization earlier.
Revisit your research objectives
Spend some time looking at your research objectives derived from conversations with key stakeholders. These simple, specific, clear statements should have driven the design of the entire research project, but at this point it’s worth revisiting them so they’re top of mind while doing the actual data analysis.
For example, going back to the shoe-shopping app for young professionals, possible objectives might have been:
Understand the footwear preferences and style preferences of young professionals in order to design a product catalog that aligns with their fashion sensibilities.
Identify the pain points and frustrations young professionals face during the online shoe shopping experience to prioritize design improvements and enhance user satisfaction.
Investigate the expectations and challenges young professionals face when seeking footwear suitable for both professional and casual settings to develop features and filters that facilitate their search process.
Whatever the objective is, write it down again and put it somewhere prominent. Your insights must pay off this objective.
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Explore your data to create findings
Now you’re going to begin exploring the data from the research to create your findings. Remember that these aren’t insights yet – you’re just generating raw nuggets of information from all of the data you have. The goal is to break down a large quantity of research into more manageable chunks.
Code and tag your data
One popular technique with qualitative data is to code it. This isn’t like computer coding – it’s a process of reading through the transcripts and other materials and tagging relevant insights and observations with small phrases that synopsize it.
If a user is talking about how they need footwear they can wear in multiple situations, you could tag this as “catalog selection.” If another user is talking about wanting apps that look good and run smoothly on their Android phone, that could be tagged as “user interface” or “mobile preferences.” These don’t have to be perfect, and in fact should become more (or less) specific as the process continues.
One way to turn those codes into something a little more concrete is to group them into themes. A classic way to do this is via whiteboard. Write down each code – yes, all of them – on sticky notes. Put one on the wall. Now go through the other sticky notes and place the ones that are similar to the one on the wall next to it. Once these are all done, give this cluster a theme – say, “design” or “catalog needs.” Now, go find a new sticky note, stick it on the wall, and repeat until you can identify more themes.
Note that this is an iterative process. It could inspire another return to the raw materials, which will in turn generate new, more specific codes, which can then be sorted onto the wall. Those themes could be broken apart – for example, “visuals” could be broken into “user interface” and “visual branding” – or clustered together.
Organize and structure data to identify trends
If you’re working with quantitative data, do some exploratory analysis to find means and standard deviations so you can understand central tendencies and variations.
Visualizing the data in bar and pie graphs can help make findings pop more easily. For example, if a broad survey of young professionals asked users to rank the most trustworthy brands and one competitor of the shoe app was chosen by 90% of respondents, that would be a clear finding. The next question is to ask: What is that brand doing differently? Why is it resonating in this manner?
Synthesize findings into actionable insights
Now that all of the raw research has been broken down into more concrete themes and findings, these need to be distilled into actionable insights. An insight can’t just be an observation – it needs to build on the observation in a way that incorporates knowledge of the business’s objectives, the broader marketplace, and even human psychology. It needs to say why that finding is occurring and point toward a meaningful solution.
One way to do this is to comb through the themes and pull out individual findings that can be potentially turned into insights. Working one theme at a time, try to look deeply at the relevant quotes and data from a couple of different angles.
A good insight should check the following boxes:
Desirability: It should lead to something that users actually want, addressing a pain point or key motivation.
Viability: It will make money for the business or cut costs, whether in the short term or long term.
Feasibility: It should be doable with the given team, technology, and budget.
Once you have 3–5 insights, it’s time to return to the stakeholders to close the loop on the UX research process.
Here are some methods to try when synthesizing your findings.
Utilize user statements
A user statement takes the form: “[User] needs [need] in order to accomplish [goal].” This is a helpful way to nail down some core concepts. For our shoe-shopping app, this could be: “Urban-dwelling millennial women need shoes that perform multiple functions in order to switch between professional and social functions easier.”
Apply the “how might we” framework
Distilling the research into questions that begin with the phrase “how might we” can be a powerful way to prompt actionable discussion among stakeholders. The insight doesn’t have to answer this question, necessarily – that’s for other stakeholders – but it can tee up the specific problem and provide direction to those teams. “How might we add a sense of personalization to the shoe-shopping experience?”
Create audiences or personas
User research can be turned into audience groups or fictional personas that help humanize the research process. Creating distinct personas is a great way to generate empathy for the eventual user, providing key insights into who exactly you’re designing for.
Identify pain points
If users have said they’re too busy to go shoe shopping, but the research data suggests they still go shopping for other things, perhaps there’s a key part of the shoe-shopping process that could be streamlined. Is it finding the right size? Seeing if they’re comfy? Seeing how they fit with other items in the wardrobe? These are all rich areas to generate possible insights.
Keep asking “so what?” and “why?”
Drill down until you get to core truths. This level of scrutiny can be helpful when presenting later, as it helps preempt questions. Think of it as stress-testing the insights.
Present and apply UX insights
Presenting insights is the moment when all of the research pays off for both researchers and stakeholders. Every company has a different presentation culture: some just want a quick-and-dirty brief, while others prefer a broader meeting with time built in for deeper interrogation of the insights. Still others prefer a combination of the two: a presentation for real-time conversation along with a take-away document.
Whatever the culture, play to it. It may be helpful to work downward: starting with the insight, then backing it up with specific quotes, data visualizations, and findings. Utilize those user statements and “how might we” questions to translate complex problems into plain language.
If you conducted interviews or user testing, consider showing clips of users talking about their experience with stakeholders. A video of real users helps stakeholders empathize quickly with users and provides a quick glimpse into the research process.
If the insight is feasible, desirable to users, and viable for the company, it can tip into new design and marketing initiatives. UX researchers may be called on to compose or weigh in on briefs that will tee off these projects, and even help during development to make sure the end results map to user needs. It’s possible to continue to consult the users who inspired these insights in the first place, too.
Lyssna can help generate UX insights
Lyssna’s suite of products work well with a continuous user research methodology, enabling purposeful user insights at every step. This means that rich UX insights don’t have to be a one-time thing, but rather an ongoing part of your work.
In Lyssna, you can choose from a number of qualitative and quantitative methods like user interviews, card sorting, surveys, first click tests, five second tests, preference tests, and prototype tests. You can also recruit participants from your own audience or via our research panel, which is made up of 530,000+ panelists worldwide. If you’re curious to test this out for yourself, choose your study size, type, and audience on our research panel calculator to get an estimate of the cost and turnaround time for your next study.
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Frequently asked questions about UX insights
What is a UX insight?
A UX insight is an actionable end-result of the user research process, obtained by studying users’ behaviors, needs, and preferences in relation to a product or service, and then translating those studies into findings that can be used by designers, marketers, developers, and decision-makers to enhance the user experience and improve products or services.
What’s an example of a UX insight?
Here’s an example of a UX insight for a shoe-shopping app targeted at young professionals. A finding may be that a large quantity of users didn’t like the visual design of a prototype. The corresponding insight might suggest that the user interface should reflect the aspirations of the audience, which they equate with cleanliness and minimalism, leading to a more user-centric design approach.
What’s the difference between findings and insights in UX?
Findings are factual observations derived from the data collected during user research, such as user behaviors, preferences, or pain points. Insights go beyond the findings and involve translating the data into meaningful and actionable conclusions that align with business objectives and provide a deeper understanding of users’ needs, motivations, and expectations. Insights help guide the design and decision-making process, making them more valuable and applicable to product development.
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