October 1, 2025
4 minutes

Why Understanding Users Has Never Been Easier...or Harder

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.

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

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. 

The Difference between Data and Insight

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

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.

But you don't know:

  • 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?

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.

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

  • 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. 

Why Human Empathy is More Important than Ever 

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

  • 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.

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. 

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.

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.

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.

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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Optimal vs. Maze: Deep User Insights or Surface-Level Design Feedback

Product teams face an important decision when selecting the right user research platform: do they prioritize speed and simplicity, or invest in a more comprehensive platform that offers real research depth and insights? This choice becomes even more critical as user research scales and those insights directly influence major product decisions.

Maze has gained popularity in recent years among design and product teams for its focus on rapid prototype testing and design workflow integration. However, as teams scale their research programs and require more sophisticated insights, many discover that Maze's limitations outweigh its simplicity. Platform stability issues, restricted tools and functionality, and a lack of enterprise features creates friction that end up compromising insight speed, quality and overall business impact.

Why Choose Optimal instead of Maze?

Platform Depth

Test Design Limitations

  • Maze has Rigid Question Types: Maze's focus on speed comes with design inflexibility, including rigid question structures and limited customization options that reduce overall test effectiveness.
  • Optimal Offers Comprehensive Test Flexibility: Optimal has a Figma integration, image import capabilities, and fully customizable test flows designed for agile product teams.

Prototype Testing Capabilities

  • Maze has Limited Prototype Support: Users report difficulties with Maze's prototype testing capabilities, particularly with complex interactions and advanced design systems that modern products require.
  • Optimal has Advanced Prototype Testing: Optimal supports sophisticated prototype testing with full Figma integration, comprehensive interaction capture, and flexible testing methods that accommodate modern product design and development workflows.

Analysis and Reporting Quality

  • Maze Only Offers Surface-Level Reporting: Maze provides basic metrics and surface-level analysis without the depth required for strategic decision-making or comprehensive user insight.
  • Optimal has Rich, Actionable Insights: Optimal delivers AI-powered analysis with layered insights, export-ready reports, and sophisticated visualizations that transform data into actionable business intelligence.

Enterprise Features

  • Maze has a Reactive Support Model: Maze provides responsive support primarily for critical issues but lacks the proactive, dedicated support enterprise product teams require.
  • Optimal Provides Dedicated Enterprise Support: Optimal offers fast, personalized support with dedicated account teams and comprehensive training resources built by user experience experts that ensure your team is set up for success.

Enterprise Readiness

  • Maze is Buit for Individuals: Maze was built primarily for individual designers and small teams, lacking the enterprise features, compliance capabilities, and scalability that large organizations need.
  • Optimal is an Enterprise-Built Platform: Optimal was designed for enterprise use with comprehensive security protocols, compliance certifications, and scalability features that support large research programs across multiple teams and business units. Optimal is currently trusted by some of the world’s biggest brands including Netflix, Lego and Nike. 

Enterprises Need Reliable, Scalable User Insights

While Maze's focus on speed appeals to design teams seeking rapid iteration, enterprise product teams need the stability and reliability that only mature platforms provide. Optimal delivers both speed and dependability, enabling teams to iterate quickly without compromising research quality or business impact.Platform reliability isn't just about uptime, it's about helping product teams make high quality strategic decisions and to build organizational confidence in user insights. Mature product, design and UX teams need to choose platforms that enhance rather than undermine their research credibility.

Don't let platform limitations compromise your research potential.

Ready to see how leading brands including Lego, Netflix and Nike achieve better research outcomes? Experience how Optimal's platform delivers user insights that adapt to your team's growing needs.

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1 min read

Making the Complex Simple: Clarity as a UX Superpower in Financial Services

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.

How to keep things simple with good UX research 

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:

Uncover Navigation Challenges with Tree Testing

Complex financial products often create equally complex navigation structures:

How can you solve this? 

  • 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

Identify Confusion Points with First-Click Testing

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

How can you solve this? 

  • 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

Understand User Mental Models with Card Sorting

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

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

Practical Strategies for Simplifying Financial UX

1. Progressive Information Disclosure

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

  • 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

2. Visual Representation of Numerical Concepts

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

What could this look like? 

  • 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

3. Contextual Decision Support

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

How do you solve for this? 

  • 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

4. Language Simplification and Standardization

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

  • 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

Measuring Simplification Success

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

1. Establish Complexity Baselines

Use Optimal's tools to create baseline measurements:

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

2. Implement Iterative Testing

Before launching major simplification initiatives, validate improvements through:

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

3. Track Simplification Metrics Over Time

Create a dashboard of key simplification indicators:

  • 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

Where rubber hits the road: Organizational Commitment to Clarity

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

  • 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?

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

Conclusion: The Future Belongs to the Clear

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.

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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1 min read

How to convince others of the importance of UX research

There’s not much a parent won’t do to ensure their child has the best chance of succeeding in life. Unsurprisingly, things are much the same in product development. Whether it’s a designer, manager, developer or copywriter, everyone wants to see the product reach its full potential.

Key to a product’s success (even though it’s still not widely practiced) is UX research. Without research focused on learning user pain points and behaviors, development basically happens in the dark. Feeding direct insights from customers and users into the development of a product means teams can flick the light on and make more informed design decisions.

While the benefits of user research are obvious to anyone working in the field, it can be a real challenge to convince others of just how important and useful it is. We thought we’d help.

Define user research

If you want to sell the importance of UX research within your organization, you’ve got to ensure stakeholders have a clear understanding of what user research is and what they stand to gain from backing it.

In general, there are a few key things worth focusing on when you’re trying to explain the benefits of research:

  • More informed design decisions: Companies make major design decisions far too often without considering users. User research provides the data needed to make informed decisions.
  • Less uncertainty and risk: Similarly, research reduces risk and uncertainty simply by giving companies more clarity around how a particular product or service is used.
  • Retention and conversion benefits: Research means you’ll be more aligned with the needs of your customers and prospective customers.

Use the language of the people you’re trying to convince. A capable UX research practice will almost always improve key business metrics, namely sales and retention.

The early stages

When embarking on a project, book in some time early in the process to answer questions, explain your research approach and what you hope to gain from it. Here are some of the key things to go over:

  • Your objectives: What are you trying to achieve? This is a good time to cover your research questions.
  • Your research methods: Which methods will you be using to carry out your research? Cover the advantages of these methods and the information you’re likely to get from using them.
  • Constraints: Do you see any major obstacles? Any issues with resources?
  • Provide examples: Nothing shows the value of doing research quite like a case study. If you can’t find an example of research within your own organization, see what you can find online.

Involve others in your research

When trying to convince someone of the validity of what you’re doing, it’s often best to just show them. There are a couple of effective ways you can do this – at a team or individual level and at an organizational level.

We’ll explain the best way to approach this below, but there’s another important reason to bring others into your research. UX research can’t exist in a vacuum – it thrives on integration and collaboration with other teams. Importantly, this also means working with other teams to define the problems they’re trying to solve and the scope of their projects. Once you’ve got an understanding of what they’re trying to achieve, you’ll be in a better position to help them through research.

Educate others on what research is

Education sessions (lunch-and-learns) are one of the best ways to get a particular team or group together and run through the what and why of user research. You can work with them to work out what they’d like to see from you, and how you can help each other.

Tailor what you’re saying to different teams, especially if you’re talking to people with vastly different skill sets. For example, developers and designers are likely to see entirely different value in research.

Collect user insights across the organization

Putting together a comprehensive internal repository focused specifically on user research is another excellent way to grow awareness. It can also help to quantify things that may otherwise fall by the wayside. For example, you can measure the magnitude of certain pain points or observe patterns in feature requests. Using a platform like Notion or Confluence (or even Google Drive if you don’t want a dedicated platform), log all of your study notes, insights and research information that you find useful.

Whenever someone wants to learn more about research within the organization, they’ll be able to find everything easily.

Bring stakeholders along to research sessions

Getting a stakeholder along to a research session (usability tests and user interviews are great starting points) will help to show them the value that face-to-face sessions with users can provide.

To really involve an observer in your UX research, assign them a specific role. Note taker, for example. With a short briefing on best-practices for note taking, they can get a feel for what’s like to do some of the work you do.

You may also want to consider bringing anyone who’s interested along to a research session, even if they’re just there to observe.

Share your findings – consistently

Research is about more than just testing a hypothesis, it’s important to actually take your research back to the people who can action the data.

By sharing your research findings with teams and stakeholders regularly, your organization will start to build up an understanding of the value that ongoing research can provide, meaning getting approval to pursue research in future becomes easier. This is a bit of a chicken and egg situation, but it’s a practice that all researchers need to get into – especially those embedded in large teams or organizations.

Anything else you think is worth mentioning? Let us know in the comments.

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