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1 min read

Optimal vs Askable: Why an All-in-One Platform is a Better Choice than Niche Tools

When selecting user research and insight tools, product and design teams must decide between two distinct approaches: investing in multiple niche tools that address specific parts of the user research workflow, such as Askable for participant recruitment, or choosing a comprehensive platform like Optimal that supports the entire product development lifecycle from research planning through to insight.

Why choose Optimal instead of Askable?

Recruitment-Only Tools vs. Comprehensive User Research Platforms

Platform Scope and Capabilities

  • Askable's Limitations: Askable specializes exclusively in participant recruitment, requiring teams to integrate multiple third-party tools for testing, analysis, and insight generation. This fragmented approach creates workflow friction and increases project complexity.
  • Optimal's Advantage: Optimal delivers recruitment, testing, and analysis within a single platform. Teams can recruit participants, conduct UX tests, analyze results, and generate insights without switching between tools or managing multiple vendor relationships.

Global Reach and Participant Quality

  • Regional Limitations: Askable's participant panel concentrates heavily in Australia and New Zealand, limiting global research capabilities. For enterprises requiring international insights, this geographic constraint becomes a significant bottleneck.
  • Worldwide Coverage: Optimal partners with 100+ million verified participants across 150+ countries, enabling global research at scale. Advanced fraud prevention and screening protocols ensure participant quality regardless of location.

Pricing Structure and Cost Predictability

  • Variable Costs: Askable employs credit-based pricing that scales with session length, making long-form research sessions increasingly expensive and budget planning difficult.
  • Transparent Pricing: Optimal offers flat-rate pricing regardless of session duration, eliminating hidden fees and enabling predictable research budgets for extended studies.

Why Enterprises Choose Optimal Over Askable

  1. Operational Efficiency. Teams using Askable must coordinate between recruitment services and separate testing platforms, creating project management overhead. Optimal eliminates this complexity by providing integrated recruitment and testing capabilities.
  2. Advanced Research Capabilities. While Askable focuses on participant recruitment, Optimal includes: Built-in UX testing tools, AI-powered analysis and insights, Automated reporting and visualization, Survey and prototype testing capabilities
  3. Enterprise-Grade Support. Optimal provides dedicated account management and comprehensive fraud prevention assurance, whereas Askable offers standard support options without the specialized enterprise features global brands require.
  4. Scalability for Growing Teams. Askable's recruitment-only model doesn't scale with research program maturity. As teams need more sophisticated testing and analysis capabilities, they must invest in additional tools. Optimal grows with research programs from basic recruitment through advanced insight generation.

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

Optimal vs Qualtrics: When More Isn’t Always Better

Enterprise teams frequently encounter pressure from leadership to adopt consolidated platforms like Qualtrics that promise to handle multiple functions including PX, EX, and CX, in a single solution for all user feedback needs. While these multidisciplinary platforms may seem appealing from a procurement perspective, they often fall short for specialized use cases. UX and product teams typically find that purpose-built platforms like Optimal deliver superior results and stronger ROI. These specialized solutions offer the depth of functionality teams actually need while maintaining significantly reduced complexity and cost compared to enterprise-wide platforms that try to be everything to everyone.

Why Choose Optimal over Qualtrics? 

Specialist Research Platforms Outperform Generalist Platforms

  • Feature Overload: Enterprise platforms like Qualtrics provide hundreds of features across multiple use cases, creating complexity and inefficiency for research and product teams looking for user insight to drive their decisions. 
  • Purpose-Built Research Features: Specialized platforms eliminate feature bloat while providing deep capabilities in their area of focus, enabling teams to achieve better results.
  • Multi-Department Compromise: Enterprise platforms often represent compromises across multiple departments, resulting in tools that serve everyone to some degree but no one team really well.
  • Research Team Optimization: Purpose-built research platforms optimize specifically for product and research team workflows, participant experience, and user insight quality.

What does this look like when you compare Qualtrics to Optimal? 

  • Qualtrics' Broad Scope Challenge: Qualtrics serves customer experience (CX), employee experience (EX), and product experience (PX) across entire enterprises. This broad scope creates feature overload that overwhelms UX research teams who need focused, efficient tools. They are a “jack of all trades, master of none”. 
  • Optimal's UX Research Focus: Built specifically for UX and product research, Optimal eliminates unnecessary complexity while providing deep capabilities for user testing, prototype validation, and product insight that UX teams actually use.

High Costs and Launch Complexity 

In addition to feature complexity, platforms like Qualtrics often come with high costs for the features your team doesn’t really need.  While some of these larger, multi-department  platforms may appear cost-effective because they offer tool consolidation , the total cost of ownership often includes substantial professional services, extended training periods, and ongoing support requirements that specialized teams end up absorbing, despite utilizing only a fraction of available capabilities.

  • License Costs: Qualtrics pricing ranges from $50,000 to $300,000+ annually with complex modular licensing that forces teams to pay for CX and EX capabilities they don't need for UX research.
  • Transparent UX Research Pricing: Optimal offers straightforward, flat-rate pricing focused on UX research capabilities without forcing teams to subsidize enterprise modules irrelevant to their workflow.
  • Professional Services Requirements: Qualtrics implementations often require expensive professional services, extended onboarding periods, and ongoing consulting to achieve success.
  • Get Started in Minutes: Optimal's intuitive design enables teams to launch studies in minutes, no complex set up, no engineering support required 

For the Best User Insights Specialization Beats Generalization

While Qualtrics serves enterprise survey needs across multiple departments, UX research teams achieve better results with purpose-built platforms that eliminate unnecessary features while providing clear ROI. Optimal delivers 90% of Qualtrics’ enterprise platform value with 10% of the complexity.

User research excellence requires tools designed specifically for UX workflows. Smart research and product teams choose platforms that enhance your research impact rather than adding implementation overhead and workflow friction.

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

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.

Learn more
1 min read

Optimal vs. UserTesting: A Modern, Streamlined Platform or a Complex Enterprise Suite

The user research landscape has evolved significantly in recent years, but not all platforms have adapted at the same pace. UserTesting for example, despite being one of the largest players in the market, still operates on legacy infrastructure with outdated pricing models that no longer meet the evolving needs of mature UX, design and product teams. More and more we see enterprises choosing platforms like Optimal, because we represent the next generation of user research and insight platforms:  ones that are purpose-built for modern teams that are prioritizing agility, insight quality, and value.

What are the biggest differences between Optimal and UserTesting?

Cost

  • UserTesting is Expensive: UserTesting charges $5,000-$10,000 per user annually plus additional session-based fees, creating unpredictable costs that escalate the more research your team does. This means that teams often face budget surprises when conducting longer studies or more frequent research.
  • Optimal has Transparent Pricing: Optimal offers flat-rate pricing without per-seat fees or session units, enabling teams to scale research sustainbly. Our transparent pricing eliminates budget surprises and enables predictable research ops planning.

Return on Investment

  • Justifying the Cost of UserTesting: UserTesting's high costs and complex pricing structure make it hard to prove the ROI, particularly for teams conducting frequent research or extended studies that trigger additional session fees.
  • The Best Value in the Market: Optimal's straightforward pricing and comprehensive feature set deliver measurable ROI. We offer 90% of the features that UserTesting provides at 10% of the price.

Technology Evolution

  • UserTesting is Struggling to Modernize: UserTesting's platform shows signs of aging infrastructure, with slower performance and difficulty integrating modern research methodologies. Their technology advancement has lagged behind industry innovation.
  • Optimal is Purpose-Built for Modern Research: Optimal has invested heavily over the last few years in features for contemporary research needs, including AI-powered analysis and automation capabilities.

UserZoom Integration Challenges

  • UserZoom Integration Challenges: UserTesting's acquisition of UserZoom has created platform challenges that continue to impact user experience. UserTesting customers report confusion navigating between legacy systems and inconsistent feature availability and quality.
  • Built by Researchers for Researchers: Optimal has built from the ground up a single, cohesive platform without the complexity of merged acquisitions, ensuring consistent user experience and seamless workflow integration.

Participant Panel Quality

  • Poor Quality, In-House Panel: UserTesting's massive scale has led to participant quality issues, with researchers reporting difficulty finding high-quality participants for specialized research needs and inconsistent participant engagement.
  • Flexibility = Quality: Optimal prioritizes flexibility for our users, allowing our customers to bring their own participants for free or use our high-quality panels, with over 100+ million verified participants across 150+ countries who meet strict quality standards.

Customer Support Experience

  • Impersonal, Enterprise Support: Users report that UserTesting's large organizational structure creates slower support cycles, outsourced customer service, and reduced responsiveness to individual customer needs.
  • Agile, Personal Support: At Optimal we pride ourselves on our fast, human support with dedicated account management and direct access to product teams, ensuring fast and personalized support.

The Future of User Research Platforms

The future of user research platforms is here, and smart teams are re-evaluating their platform needs to reflect that future state. What was once a fragmented landscape of basic testing tools and legacy systems has evolved into one where comprehensive user insight platforms are now the preferred solution. Today's UX, product and design teams need platforms that have evolved to include:

  • Advanced Analytics: AI-powered analysis that transforms data into actionable insights
  • Flexible Recruitment: Options for both BYO, panel and custom participant recruitment
  • Transparent Pricing: Predictable costs that scale with your needs
  • Responsive Development: Platforms that evolve based on user feedback and industry trends

Platforms Need to Evolve for Modern Research Needs

When selecting a vendor, teams need to choose a platform with the functionality that their teams need now but also one that will also grow with the needs of your team in the future. Scalable, adaptable platforms enable research teams to:

  • Scale Efficiently: Grow research activities without exponential cost increaeses
  • Embrace Innovation: Integrate new research methodologies and analysis techniques as well as emerging tools like AI 
  • Maintain Standards: Ensure consistent participant, data and tool quality as the platform evolves
  • Stay Responsive: Adapt to changing business needs and market conditions

The key is choosing a platform that continues to evolve rather than one constrained by outdated infrastructure and complex, legacy pricing models.

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.

Learn more
1 min read

Building Trust Through Design for Financial Services

When it comes to financial services, user experience goes way beyond just making things easy to use. It’s about creating a seamless journey and establishing trust at every touchpoint. Think about it: as we rely more and more on digital banking and financial apps in our everyday lives, we need to feel absolutely confident that our personal information is safe and that the companies managing our money actually know what they're doing. Without that trust foundation, even the most competitive brands will struggle with customer adoption.

Why Trust Matters More Than Ever

The stakes are uniquely high in financial UX. Unlike other digital products where a poor experience might result in minor frustration, financial applications handle our life savings, investment portfolios, and sensitive personal data. A single misstep in design can trigger alarm bells for users, potentially leading to lost customers.

Using UX Research to Measure and Build Trust

Building high trust experiences requires deep insights into user perceptions, behaviors, and pain points. The best UX platforms can help financial companies spot trust issues and test whether their solutions actually work.

Identify Trust Issues with Tree Testing

Tree testing helps financial institutions understand how easily users can find critical information and features:

  • Test information architecture to ensure security features and privacy information are easily discoverable
  • Identify confusing terminology that may undermine user confidence
  • Compare findability metrics for trust-related content across different user segments

Optimize for Trustworthy First Impressions with First-Click Testing

First-click testing helps identify where users naturally look for visual symbols and cues that are associated with security:

  • Test where users instinctively look for security indicators like references to security certifications
  • Compare the effectiveness of different visual trust symbols (locks, shields, badges)
  • Identify the optimal placement for security messaging across key screens

Map User Journeys with Card Sorting

Card sorting helps brands understand how users organize concepts. Reducing confusion, helps your financial brand appear more trustworthy, quickly:

  • Use open card sorts to understand how users naturally categorize security and privacy features
  • Identify terminology that resonates with users' perceptions around security

Qualitative Insights Through Targeted Questions

Gathering qualitative data through strategically placed questions allows financial institutions to collect rich, timely insights about how much their customers trust their brand:

  • Ask open ended questions about trust concerns at key moments in the testing process
  • Gather specific feedback on security terminology understanding and recognition
  • Capture emotional responses to different trust indicators

What Makes a Financial Brand Look Trustworthy?

Visual Consistency and Professional Polish

When someone opens your financial app or website, they're making snap judgments about whether they can trust you with their money. It happens in milliseconds, and a lot of that decision comes down to how polished and consistent everything looks.Clean, consistent design sends that signal of stability and attention to detail that people expect when money's involved.

To achieve this, develop and rigorously apply a solid design system across all digital touchpoints including fonts, colors, button styles, and spacing, it all needs to be consistent across every page and interaction. Even small inconsistencies can make people subconsciously lose confidence.

Making Security Visible

Unlike walking into a bank where you can see the vault and security cameras, digital security happens behind the scenes. Users can't see all the protection you've built in unless you make a point of showing them.

Highlighting your security measures in ways that feel reassuring rather than overwhelming gives people that same sense of "my money is safe here" that they'd get from seeing a bank's physical security.

From a design perspective, apply this thinking to elements like:

  • Real time login notifications
  • Transaction verification steps
  • Clear encryption indicators
  • Transparent data usage explanations
  • Session timeout warnings

You can test the success of these design elements through preference testing, where you can compare different approaches to security visualization to determine which elements most effectively communicate trust without creating anxiety.

Making Complex Language Simple

Financial terminology is naturally complex, but your interface content doesn't have to be. Clear, straightforward language builds trust so it’s important to develop a content strategy that:

  • Explains unavoidable complex terms contextually
  • Replaces jargon with plain language
  • Provides proactive guidance before errors occur
  • Uses positive, confident messaging around security features

You can test your language and navigation elements by using tree testing to evaluate user understanding of different terminology, measuring success rates for finding information using different labeling options.

Create an Ongoing Trust Measurement Program

A user research platform enables financial institutions to implement ongoing trust measurement across the product lifecycle:

Establish Trust Benchmarks

Use UX research tools to establish baseline metrics for measuring user trust:

  • Findability scores for security features
  • User reported confidence ratings
  • Success rates for security related tasks
  • Terminology comprehension levels

Validate Design Updates

Before implementing changes to critical elements, use quick tests to validate designs:

  • Compare current vs. proposed designs with prototype testing
  • Measure findability improvements with tree testing
  • Evaluate usability through first-click testing

Monitor Trust Metrics Over Time

Create a dashboard of trust metrics that can be tracked regularly:

  • Task success rates for security related activities
  • Time-to-completion for verification processes
  • Confidence ratings at key security touchpoints

Cross-Functional Collaboration to Improve Trust

While UX designers can significantly impact brand credibility, remember that trust is earned across the entire customer experience:

  • Product teams ensure feature promises align with actual capabilities
  • Security teams translate complex security measures into user-friendly experiences
  • Marketing ensures brand promises align with the actual user experience
  • Customer service supports customers when trust issues arise

Trust as a Competitive Advantage

In an industry where products and services can often seem interchangeable to consumers, trust becomes a powerful differentiator. By placing trust at the center of your design philosophy and using comprehensive user research to measure and improve trust metrics, you're not just improving user experience, you're creating a foundation for lasting customer relationships in an industry where loyalty is increasingly rare.

The most successful financial institutions of the future won't necessarily be those with the most features or the slickest interfaces, but those that have earned and maintained user trust through thoughtful UX design built on a foundation of deep user research and continuous improvement.

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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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