Dive deeper into participant responses with segments

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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A great user experience (UX) is one of the largest drivers of growth and revenue through user satisfaction. However, when budgets get tight, or there is a squeeze on timelines, user research is one of the first things to go. Often at the cost of user satisfaction.  

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This short sighted view can mean project managers are preoccupied with achieving milestones and short term goals. And UX teams get stuck researching products they weren’t actually involved with developing. As a result no one has the space and understanding to really develop a product that speaks to users needs, desires and wants. There must  be a better way to produce a product that is user-driven.  Thankfully there is.

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What is user research and why should project managers care about it? 👨🏻💻

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User research is an important part of the product development process. Primarily, user research involves using different research methods to gather information about your end users. 

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Essentially it aims to create the best possible experience for your users by listening and learning directly from those that already or potentially will use your product. You might conduct interviews to help you understand a particular problem, carry out a tree test to identify bottlenecks or problems in your navigation, or do some usability testing to directly observe your users as they perform different tasks on your website or in your app. Or a combination of these to understand what users really want.

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To a project manager and team, this likely sounds fairly familiar, that any project can’t be managed in a silo. Regular check-ins and feedback are essential to making smart decisions. The same with UX research. It can make the whole process quicker and more efficient. By taking a step back, digging into your users’ minds, and gaining a fuller understanding of what they want upfront, it can curtail short-term views and decisions.

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Bringing more user research into your development process has major benefits for the team, and the ultimately the quality of that final product. There are three key benefits:

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1. Saves your development team time and effort. Ensuring the team is working on what users want, not wasting time on features that don’t measure up.
2. Gives your users a better experience by meeting their requirements.
3. Helps your team innovate quickly by understanding what users really want.

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As a project manager, making space and planning for user research can be one of the best ways to ensure the team is creating a product that truly is user-driven.

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How to bring research into your product development process 🤔

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There are a couple of ways you can bring UX research into your product development process. 

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1. Start with a dedicated research project.
2. Integrate UX research throughout the development project.

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It can be more difficult to integrate UX research throughout the process, as it means planning the project with various stages of research built in to check the development of features. But ultimately this approach is likely to turn out the best product. One that has been considered, checked and well thought out through the whole product development process. To help you on the way we have laid out 6 key steps to help you integrate UX research into your product development process.

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6 key steps to integrate UX research 👟

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Step 1: Define your research questions

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Take a step back, look at your product and define your research questions. 

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It may be tempting just to ask, ‘do users like our latest release?’ This however does not get to why or what your users like or don’t like. Try instead:

• What do our users really want from our product?
• Where are they currently struggling while using our website?
• How can we design a better product for our users?

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These questions help to form the basis of specific questions about your product and specific areas of research to explore which in turn help shape the type of research you undertake.

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Step 2: Create your research plan

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With a few key research questions to focus on, it’s time to create your research plan.

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A great research plan covers your project’s goals, scope, timing, and deliverables. It’s essential for keeping yourself organized but also for getting key stakeholder signoff.

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Step 3: Prepare any research logistics

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Every project plan requires attention to detail including a user research project. And with any good project there are a set of steps to help make sense of it.

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1. Method: Based on your questions, what is the best user research method to use? 
2. Schedule: When will the research take place? How long will it go on for? If this is ongoing research, plan how it will be implemented and how often.
3. Location: Where will the research take place? 
4. Resources: What resources do you need? This could be technical support or team members.
5. Participants: Define who you want to research. Who is eligible to take part in this research? How will you find the right people?
6. Data: How will you capture the research data? Where will it be stored? How will you analyze the data and create insights and reports that can be used?
7. Deliverables: What is the ultimate goal for your research project?

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Step 4: Decide which method will be used

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Many user research methods benefit from an observational style of testing. Particularly if you are looking into why users undertake a specific task or struggle.

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Typically, there are two approaches to testing:

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1. Moderated testing is when a moderator is present during the test to answer questions, guide the participant, or dig deeper with further questions.
2. Unmoderated testing is when a participant is left on their own to carry out the task. Often this is done remotely and with very specific instructions.Your key questions will determine which method will works best for your research.  Find our more about the differences.

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Step 5: Run your research session

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It’s time to gather insights and data. The questions you are asking will influence how you run your research sessions and the methods you’ve chosen. 

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If you are running surveys you will be asking users through a banner or invitation to fill out your survey. Unmoderated and very specific questions. Gathering qualitative data and analyzing patterns.

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If you’re using something qualitative like interviews or heat mapping, you’ll want to implement software and gather as much information as possible.

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Step 6: Prepare a research findings report and share with stakeholders

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Analyze your findings, interrogate your data and find those insights that dive into the way your users think. How do they love your product? But how do they also struggle?

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Pull together your findings and insights into an easy to understand report. And get socializing. Bring your key stakeholders together and share your findings. Bringing everyone across the findings together can bring everyone on the journey. And for the development process can mean decisions can be user-driven. 

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Wrap Up 🥙

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Part of any project, UX research should be essential to developing a product that is user-driven. Integrating user research into your development process can be challenging. But with planning and strategy it can be hugely beneficial to saving time and money in the long run. 

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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