Dive deeper into participant responses with segments

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.

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What exactly are segments?

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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.

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What filters can I use?

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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.

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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.

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

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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. 

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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.

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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.

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When should you use segments?

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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.

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How segments help you to unlock data magic 💫

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

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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.

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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.

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

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How to create a segment

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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. 

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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! 

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

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In the realm of financial services, complexity isn't just a challenge, it's the default state. From intricate investment products to multi-layered insurance policies to complex fee structures, financial services are inherently complicated. But your users don't want complexity; they want confidence, clarity, and control over their financial lives.

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How to keep things simple with good UX research 

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Understanding how users perceive and navigate complexity requires systematic research. Optimal's platform offers specialized tools to identify complexity pain points and validate simplification strategies:

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Uncover Navigation Challenges with Tree Testing

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Complex financial products often create equally complex navigation structures:

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How can you solve this? 

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• Test how easily users can find key information within your financial platform
• Identify terminology and organizational structures that confuse users
• Compare different information architectures to find the most intuitive organization

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Identify Confusion Points with First-Click Testing

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Understanding where users instinctively look for information reveals valuable insights about mental models:

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How can you solve this? 

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• Test where users click when trying to accomplish common financial tasks
• Compare multiple interface designs for complex financial tools
• Identify misalignments between expected and actual user behavior

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Understand User Mental Models with Card Sorting

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Financial terminology and categorization often don't align with how customers think:

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How can you solve this? 

• Use open card sorts to understand how users naturally group financial concepts
• Test comprehension of financial terminology
• Identify intuitive labels for complex financial products

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Practical Strategies for Simplifying Financial UX

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1. Progressive Information Disclosure

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Rather than bombarding users with all information at once, layer information from essential to detailed:

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• Start with core concepts and benefits
• Provide expandable sections for those who want deeper dives
• Use tooltips and contextual help for terminology
• Create information hierarchies that guide users from basic to advanced understanding

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2. Visual Representation of Numerical Concepts

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Financial services are inherently numerical, but humans don't naturally think in numbers—we think in pictures and comparisons.

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What could this look like? 

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• Use visual scales and comparisons instead of just presenting raw numbers
• Implement interactive calculators that show real-time impact of choices
• Create visual hierarchies that guide attention to most relevant figures
• Design comparative visualizations that put numbers in context

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3. Contextual Decision Support

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Users don't just need information; they need guidance relevant to their specific situation.

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How do you solve for this? 

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• Design contextual recommendations based on user data
• Provide comparison tools that highlight differences relevant to the user
• Offer scenario modeling that shows outcomes of different choices
• Implement guided decision flows for complex choices

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4. Language Simplification and Standardization

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Financial jargon is perhaps the most visible form of unnecessary complexity. So, what can you do? 

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• Develop and enforce a simplified language style guide
• Create a financial glossary integrated contextually into the experience
• Test copy with actual users, measuring comprehension, not just preference
• Replace industry terms with everyday language when possible

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Measuring Simplification Success

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To determine whether your simplification efforts are working, establish a continuous measurement program:

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1. Establish Complexity Baselines

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Use Optimal's tools to create baseline measurements:

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• Success rates for completing complex tasks
• Time required to find critical information
• Comprehension scores for key financial concepts
• User confidence ratings for financial decisions

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2. Implement Iterative Testing

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Before launching major simplification initiatives, validate improvements through:

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• A/B testing of alternative explanations and designs
• Comparative testing of current vs. simplified interfaces
• Comprehension testing of revised terminology and content

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3. Track Simplification Metrics Over Time

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Create a dashboard of key simplification indicators:

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• Task success rates for complex financial activities
• Support call volume related to confusion
• Feature adoption rates for previously underutilized tools
• User-reported confidence in financial decisions

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Where rubber hits the road: Organizational Commitment to Clarity

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True simplification goes beyond interface design. It requires organizational commitment at the most foundational level:

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• Product development: Are we creating inherently understandable products?
• Legal and compliance: Can we satisfy requirements while maintaining clarity?
• Marketing: Are we setting appropriate expectations about complexity?
• Customer service: Are we gathering intelligence about confusion points?

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When there is a deep commitment from the entire organization to simplification, it becomes part of a businesses’ UX DNA. 

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Conclusion: The Future Belongs to the Clear

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As financial services become increasingly digital and self-directed, clarity bcomes essential for business success. The financial brands that will thrive in the coming decade won't necessarily be those with the most features or the lowest fees, but those that make the complex world of finance genuinely understandable to everyday users.

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By embracing clarity as a core design principle and supporting it with systematic user research, you're not just improving user experience, you're democratizing financial success itself.

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Product, design and research teams today are drowning in user data while starving for user understanding. Never before have teams had such access to user information, analytics dashboards, heatmaps, session recordings, survey responses, social media sentiment, support tickets, and endless behavioral data points. Yet despite this volume of data, teams consistently build features users don't want and miss needs hiding in plain sight.

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It’s a true paradox for product, design and research teams: more information has made genuine understanding more elusive. 

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Because with  all this data, teams feel informed. They can say with confidence: "Users spend 3.2 minutes on this page," "42% abandon at this step," "Power users click here." But what this data doesn't tell you is Why. 

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The Difference between Data and Insight

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Data tells you what happened. Understanding tells you why it matters.

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Here’s a good example of this: Your analytics show that 60% of users abandon a new feature after first use. You know they're leaving. You can see where they click before they go. You have their demographic data and behavioral patterns.

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But you don't know:

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• Were they confused or simply uninterested?
• Did it solve their problem too slowly or not at all?
• Would they return if one thing changed, or is the entire approach wrong?
• Are they your target users or the wrong segment entirely?

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One team sees "60% abandonment" and adds onboarding tooltips. Another talks to users and discovers the feature solves the wrong problem entirely. Same data, completely different understanding.

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Modern tools make it dangerously easy to mistake observation for comprehension, but some aspects of user experience exist beyond measurement:

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• Emotional context, like the frustration of trying to complete a task while handling a crying baby, or the anxiety of making a financial decision without confidence.
• The unspoken needs of users which can only be demonstrated through real interactions. Users develop workarounds without reporting bugs. They live with friction because they don't know better solutions exist.
• Cultural nuances that numbers don't capture, like how language choice resonates differently across cultures, or how trust signals vary by context.
• Data shows what users do within your current product. It doesn't reveal what they'd do if you solved their problems differently to help you identify new opportunities. 

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Why Human Empathy is More Important than Ever 

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The teams building truly user-centered products haven't abandoned data but they've learned to combine quantitative and qualitative insights. 

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• Combine analytics (what happens), user interviews (why it happens), and observation (context in which it happens).
• Understanding builds over time. A single study provides a snapshot; continuous engagement reveals the movie.
• Use data to form theories, research to validate them, and real-world live testing to confirm understanding.
• Different team members see different aspects. Engineers notice system issues, designers spot usability gaps, PMs identify market fit, researchers uncover needs.

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Adding AI into the mix also emphasizes the need for human validation. While AI can help significantly speed up workflows and can augment human expertise, it still requires oversight and review from real people. 

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AI can spot trends humans miss, processing millions of data points instantly but it can't understand human emotion, cultural context, or unspoken needs. It can summarize what users say but humans must interpret what they mean.

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Understanding users has never been easier from a data perspective. We have tools our predecessors could only dream of.  But understanding users has never been harder from an empathy perspective. The sheer volume of data available to us creates an illusion of knowledge that's more dangerous than ignorance.

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The teams succeeding aren't choosing between data and empathy, they're investing equally in both. They use analytics to spot patterns and conversations to understand meaning. They measure behavior and observe context. They quantify outcomes and qualify experiences.

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Because at the end of the day, you can track every click, measure every metric, and analyze every behavior, but until you understand why, you're just collecting data, not creating understanding.

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