Why User Interviews Haven't Evolved in 20 Years (And How We're Changing That)

Our exciting new feature, segments, saves time by allowing you to create and save groups of participant responses based on various filters. Think of it as your magic wand to effortlessly organize and scrutinize the wealth of data and insight you collect in your studies. Even more exciting is that the segments are available in all our quantitative study tools, including Optimal Sort, Treejack, Chalkmark, and Questions.

‍

What exactly are segments?

‍

In a nutshell, segments let you effortlessly create and save groups of participants' results based on various filters, saving you and the team time and ensuring you are all on the same page. 

A segment represents a demographic within the participants who completed your study. These segments can then be applied to your study results, allowing you to easily view and analyze the results of that specific demographic and spot the hidden trends.

‍

What filters can I use?

‍

Put simply, you've got a treasure trove of participant data, and you need to be able to slice and dice it in various ways. Segmenting your data will help you dissect and explore your results for deeper and more accurate results.

‍

Question responses: Using a screener survey or pre - or post-study questions with pre-set answers (like multi-choice), you can segment your results based on their responses.

‍

URL tag: If you identify participants using a unique identifier such as a URL tag, you can select these to create segments.

‍

Tree test tasks, card sort categories created, first click test and survey responses: Depending on your study type, you can create a segment to categorize participants based on their response in the study. 

‍

Time taken: You can select the time taken filter to view data from those who completed your study in a short space of time. This may highlight some time wasters who speed through and probably haven’t provided you with high-quality responses. On the other hand, it can provide insight into A/B tests for example, it could show you if it’s taking participants of a tree test longer to find a destination in one tree or another.

‍

With this feature, you can save and apply multiple segments to your results, using a combination of AND/OR logic when creating conditions. This means you can get super granular insights from your participants and uncover those gems that might have otherwise remained hidden.

‍

When should you use segments?

‍

This feature is your go-to when you have results from two or more participant segments. For example, imagine you're running a study involving both teachers and students. You could focus on a segment that gave a specific answer to a particular task, question, or card sort. It allows you to drill down into the nitty-gritty of your data and gain more understanding of your customers.

‍

How segments help you to unlock data magic 💫

‍

Let's explore how you can harness the power of segments:

‍

Save time: Create and save segments to ensure everyone on your team is on the same page. With segments, there's no room for costly data interpretation mishaps as everyone is singing from the same hymn book.

‍

Surface hidden trends: Identifying hidden trends or patterns within your study is much easier.  With segments,  you can zoom in on specific demographics and make insightful, data-driven decisions with confidence.

‍

Organized chaos: No more data overload! With segments, you can organize participant data into meaningful groups, unleashing clarity and efficiency.

‍

How to create a segment

‍

Ready to take segments for a spin?  To create a new segment or edit an existing one, go to  Results > Participants > Segments. Select the ‘Create segment’ button and select the filters you want to use. You can add multiple conditions, and save the segment.  To select a segment to apply to your results, click on ‘All included participants’ and select your segment from the drop-down menu.  This option will apply to all your results in your study. 

‍

We can't wait to see the exciting discoveries you'll make with this powerful tool. Get segmenting, and let us know what you think! 

‍

Help articles
‍

How to add a group tag in a study URL for participants
‍
How to integrate with a participant recruitment panel

‍

User research is key to discovering the inner workings of your users’ minds – their emotional, organizational, informative needs and desires. These are all super important to creating a user experience that is intuitive and meeting your users’ needs in a way that means they feel loved, cared for and considered. All the deep understanding stuff that keeps them coming back!

‍

Qualitative research allows you to collect verbatim data from participants that give insights into why they do or feel things. You can even get into whether ‘Dee’ understood how the website worked or why ‘Andrew’ would (or wouldn’t) revisit the app outside of testing.

‍

Gathering these awesome insights is one step. Analyzing and organizing these is a skill and talent in its own right. And armed with the right tools or methods it can be immersive, interesting and a great way to get under the skin of your users. Let’s take a look at affinity mapping as a method of analyzing this data - as a tool it can help researchers visualize and easily group and theme data.

‍

Affinity mapping is used outside of the UX world and can be done independently, however is a great analysis method to use collaboratively. For researchers, it can be a great tool to collaborate and engage the team and potentially stakeholders. Bringing people together to identify, discuss and resolve user experience issues. 

‍

Here we’ll lay out what affinity mapping is, specifically why it’s useful for user research and set out key steps to get you underway. 

‍

What is Affinity Mapping? 🗺️

‍

By definition, affinity mapping is the process of collecting, organizing, and grouping qualitative data to create an affinity diagram.

‍

Put simply it is a tool to group, map, sort and categorize information. A tool where you’ll look at the information and patterns of your qualitative user research and work to group these together to make sense of them. It helps you to find patterns, similar outcomes and insights that allow you to draw conclusions and collate results in a cohesive manner, then report to the wider team in a way that makes sense and provides a clear road to applicable and achievable outcomes.

‍

What is an Affinity Diagram? 🖼️

‍

An affinity diagram is what you have once you have gone through the affinity mapping process. It is the final ‘diagram’ of your grouping, sorting and categorizing. An ordered visual sorting of insights and information from your user research. And the place to filter or funnel observations and information into patterns and reach final outcomes. 

‍

Allowing you to see where the key outtakes are and where there may need to be improvements, changes or updates. And from here a roadmap can be decided.

‍

‍

Essentially the mapping part is the process of creating the diagram, a visual sorting of insights and information from your user research. So how do you make affinity mapping work for you?

‍

1. Start with a large space

‍

This could be a table, desk, pinboard or even a whiteboard. Somewhere that you can stick, pin or attach your insights to in a collaborative space. Becoming more common recently is the use of shared digital and online whiteboard tools.  allowing people to access and participate remotely.

‍

2. Record all notes

‍

Write observations, thoughts, research insights on individual cards or sticky notes.

‍

3. Look for patterns

‍

As a group read, comment and write notes or observations. Stick each of the notes onto the board, desk or whiteboard. Add, and shuffle into groups as you go. You can keep adding or moving as you go.

‍

4. Create a group/theme

‍

This will start to make sense as more sticky notes are added to the map. Creating groups for similar observations or insights, or for each pattern or theme.

‍

5. Give each theme or group a name

‍

As more notes are added there will be natural groups formed. Openly discuss if there are notes that are more difficult to categorize or themes to be decided. (We’ve outlined some ideas for UX research themes in another section below.)

‍

6. Determine priorities

‍

You’ve tidied everything into themes and groups, now what? How do you decide which of these are priorities for your organization? Discussion and voting can be the best way to decide what outcomes make the most sense and may have the biggest impact on your business.

‍

7. Report on your findings

‍

Pulling together and reporting on the findings through your affinity diagram process should be key to putting actionable outcomes in place.

‍

How to define research themes 🔬

‍

Commonly, user research is digested through thematic analysis. During thematic analysis, you aim to make sense of all the notes, observations, and discoveries you’ve documented across all your information sources, by creating themes to organize the information. 

‍

Depending on your role and the type of research you conduct, the themes you create for your affinity diagram can vary. Here are some examples of affinity groups that you could form from your UX research:

‍

• User sentiment and facial expressions when completing certain tasks
• Frequently used words or phrases when describing a product or experience
• Suggestions for improving your product or experience

‍

Wrap up 🌯

‍

Qualitative user testing and the resulting observations can be some of the best insights you get into your users’ minds. Filtering, organizing and ordering these disparate and very individual observations can be tricky. Especially if done in silo.

‍

So, draw a team together, bring in stakeholders from throughout your organization and work collaboratively to sort, organize and categorize through affinity mapping. This opens the doors to discussion, buy-in and ultimately a collective understanding of user research. Its importance and its role within the organization. And most importantly the real-world implications UX research and its insights have on organizational products and output.

‍

Are we exaggerating when we say that the way the researchers run and analyze user interviews hasn’t changed in 20 years? We don’t think so. When we talk to our customers to try and understand their current workflows, they look exactly the same as they did when we started this business 17 years ago: record, transcribe, analyze manually, create reports. See the problem?

‍

Despite  advances in technology across every industry, the fundamental process of conducting and analyzing user interviews has remained largely unchanged. While we've transformed how we design, develop, and deploy products, the way we understand our users is still trapped in workflows that would feel familiar to product, design and research teams from decades ago.

‍

The Same Old Interview Analysis Workflow 

‍

For most researchers, in the best case scenario, Interview analysis can take several hours over the span of multiple days. Yet in that same timeframe, in part thanks to new and emerging AI tools, an engineering team can design, build, test, and deploy new features. That just doesn't make sense.

‍

The problem isn't that researchers  lack tools. It's that they haven’t had the right ones. Most tools focus on transcription and storage, treating interviews like static documents rather than dynamic sources of intelligence. Testing with just 5 users can uncover 85% of usability problems, yet most teams struggle to complete even basic analysis in time to influence product decisions. Luckily, things are finally starting to change.

‍

When it comes to user research, three things are happening in the industry right now that are forcing a transformation:

‍

1. The rise of AI means UX research matters more than ever. With AI accelerating product development cycles, the cost of building the wrong thing has never been higher. Companies that invest in UX early cut development time by 33-50%, and with AI, that advantage compounds exponentially.
2. We're drowning in data and have fewer resources.  We’re seeing the need for UX research increase, while simultaneously UX research teams are more resource constrained than ever. Tasks like analyzing hours of video content to gather insights, just isn’t something teams have time for anymore. 
3. AI finally understands research. AI has evolved to a place where it can actually provide valuable insights. Not just transcription. Real research intelligence that recognizes patterns, emotions, and the gap between what users say and what they actually mean.

‍

A Dirty Little Research Secret + A Solution 

‍

We’re just going to say it; most user insights from interviews never make it past the recording stage. When it comes to talking to users, the vast majority of researchers in our audience talk about recruiting pain because the most commonly discussed challenge around interviews is usually finding enough participants who match their criteria. But on top of the challenge of finding the right people to talk to, there’s another challenge that’s even worse: finding time to analyze what users tell us. But, what if you had a tool where using AI, the moment you uploaded an interview video, key themes, pain points, and opportunities surfaced automatically? What if you could ask your interview footage questions and get back evidence-based answers with video citations?

‍

This isn't about replacing human expertise, it's about augmenting  it. AI-powered tools can process and categorize data within hours or days, significantly reducing workload. But more importantly, they can surface patterns and connections that human analysts might miss when rushing through analysis under deadline pressure. Thanks to AI, we're witnessing the beginning of a research renaissance and a big part of that is reimagining the way we do user interviews.

‍

Why AI for User Interviews is a Game Changer 

‍

When interview analysis accelerates from weeks to hours, everything changes.

‍

Product teams can validate ideas before building them. Design teams can test concepts in real-time. Engineering teams can prioritize features based on actual user need, not assumptions. Product, Design and Research teams who embrace AI to help with these workflows, will be surfacing insights, generating evidence-backed recommendations, and influencing product decisions at the speed of thought.

‍

We know that 32% of all customers would stop doing business with a brand they loved after one bad experience. Talking to your users more often makes it possible to prevent these experiences by acting on user feedback before problems become critical. When every user insight comes with video evidence, when every recommendation links to supporting clips, when every user story includes the actual user telling it, research stops being opinion and becomes impossible to ignore. When you can more easily gather, analyze and share the content from user interviews those real user voices start to get referenced in executive meetings. Product decisions begin to include user clips. Engineering sprints start to reference actual user needs. Marketing messages reflect real user voices and language.

The best product, design and research teams are already looking for tools that can support this transformation. They know that when interviews become intelligent, the entire organization becomes more user-centric. At Optimal, we're focused on improving the traditional user interviews workflow by incorporating revolutionary AI features into our tools. Stay tuned for exciting updates on how we're reimagining user interviews.