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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for example, despite being one of the largest players in the market, still operates on legacy infrastructure with outdated pricing models that no longer meet the evolving needs of mature UX, design and product teams.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.

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Efficient Research: Maximizing the ROI of Understanding Your Customers

Introduction

User research is invaluable, but in fast-paced environments, researchers often struggle with tight deadlines, limited resources, and the need to prove their impact. In our recent UX Insider webinar, Weidan Li, Senior UX Researcher at Seek, shared insights on Efficient Research—an approach that optimizes Speed, Quality, and Impact to maximize the return on investment (ROI) of understanding customers.

At the heart of this approach is the Efficient Research Framework, which balances these three critical factors:

  • Speed – Conducting research quickly without sacrificing key insights.
  • Quality – Ensuring rigor and reliability in findings.
  • Impact – Making sure research leads to meaningful business and product changes.

Within this framework, Weidan outlined nine tactics that help UX researchers work more effectively. Let’s dive in.

1. Time Allocation: Invest in What Matters Most

Not all research requires the same level of depth. Efficient researchers prioritize their time by categorizing projects based on urgency and impact:

  • High-stakes decisions (e.g., launching a new product) require deep research.
  • Routine optimizations (e.g., tweaking UI elements) can rely on quick testing methods.
  • Low-impact changes may not need research at all.

By allocating time wisely, researchers can avoid spending weeks on minor issues while ensuring critical decisions are well-informed.

2. Assistance of AI: Let Technology Handle the Heavy Lifting

AI is transforming UX research, enabling faster and more scalable insights. Weidan suggests using AI to:

  • Automate data analysis – AI can quickly analyze survey responses, transcripts, and usability test results.
  • Generate research summaries – Tools like ChatGPT can help synthesize findings into digestible insights.
  • Speed up recruitment – AI-powered platforms can help find and screen participants efficiently.

While AI can’t replace human judgment, it can free up researchers to focus on higher-value tasks like interpreting results and influencing strategy.

3. Collaboration: Make Research a Team Sport

Research has a greater impact when it’s embedded into the product development process. Weidan emphasizes:

  • Co-creating research plans with designers, PMs, and engineers to align on priorities.
  • Involving stakeholders in synthesis sessions so insights don’t sit in a report.
  • Encouraging non-researchers to run lightweight studies, such as A/B tests or quick usability checks.

When research is shared and collaborative, it leads to faster adoption of insights and stronger decision-making.

4. Prioritization: Focus on the Right Questions

With limited resources, researchers must choose their battles wisely. Weidan recommends using a prioritization framework to assess:

  • Business impact – Will this research influence a high-stakes decision?
  • User impact – Does it address a major pain point?
  • Feasibility – Can we conduct this research quickly and effectively?

By filtering out low-priority projects, researchers can avoid research for research’s sake and focus on what truly drives change.

5. Depth of Understanding: Go Beyond Surface-Level Insights

Speed is important, but efficient research isn’t about cutting corners. Weidan stresses that even quick studies should provide a deep understanding of users by:

  • Asking why, not just what – Observing behavior is useful, but uncovering motivations is key.
  • Using triangulation – Combining methods (e.g., usability tests + surveys) to validate findings.
  • Revisiting past research – Leveraging existing insights instead of starting from scratch.

Balancing speed with depth ensures research is not just fast, but meaningful.

6. Anticipation: Stay Ahead of Research Needs

Proactive researchers don’t wait for stakeholders to request studies—they anticipate needs and set up research ahead of time. This means:

  • Building a research roadmap that aligns with upcoming product decisions.
  • Running continuous discovery research so teams have a backlog of insights to pull from.
  • Creating self-serve research repositories where teams can find relevant past studies.

By anticipating research needs, UX teams can reduce last-minute requests and deliver insights exactly when they’re needed.

7. Justification of Methodology: Explain Why Your Approach Works

Stakeholders may question research methods, especially when they seem time-consuming or expensive. Weidan highlights the importance of educating teams on why specific methods are used:

  • Clearly explain why qualitative research is needed when stakeholders push for just numbers.
  • Show real-world examples of how past research has led to business success.
  • Provide a trade-off analysis (e.g., “This method is faster but provides less depth”) to help teams make informed choices.

A well-justified approach ensures research is respected and acted upon.

8. Individual Engagement: Tailor Research Communication to Your Audience

Not all stakeholders consume research the same way. Weidan recommends adapting insights to fit different audiences:

  • Executives – Focus on high-level impact and key takeaways.
  • Product teams – Provide actionable recommendations tied to specific features.
  • Designers & Engineers – Share usability findings with video clips or screenshots.

By delivering insights in the right format, researchers increase the likelihood of stakeholder buy-in and action.

9. Business Actions: Ensure Research Leads to Real Change

The ultimate goal of research is not just understanding users—but driving business decisions. To ensure research leads to action:

  • Follow up on implementation – Track whether teams apply the insights.
  • Tie findings to key metrics – Show how research affects conversion rates, retention, or engagement.
  • Advocate for iterative research – Encourage teams to re-test and refine based on new data.

Research is most valuable when it translates into real business outcomes.

Final Thoughts: Research That Moves the Needle

Efficient research is not just about doing more, faster—it’s about balancing speed, quality, and impact to maximize its influence. Weidan’s nine tactics help UX researchers work smarter by:


✔️  Prioritizing high-impact work
✔️  Leveraging AI and collaboration
✔️  Communicating research in a way that drives action

By adopting these strategies, UX teams can ensure their research is not just insightful, but transformational.

Watch the full webinar here

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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

Clara Kliman-Silver: AI & design: imagining the future of UX

In the last few years, the influence of AI has steadily been expanding into various aspects of design. In early 2023, that expansion exploded. AI tools and features are now everywhere, and there are two ways designers commonly react to it:

  • With enthusiasm for how they can use it to make their jobs easier
  • With skepticism over how reliable it is, or even fear that it could replace their jobs

Google UX researcher Clara Kliman-Silver is at the forefront of researching and understanding the potential impact of AI on design into the future. This is a hot topic that’s on the radar of many designers as they grapple with what the new normal is, and how it will change things in the coming years.

Clara’s background 

Clara Kliman-Silver spends her time studying design teams and systems, UX tools and designer-developer collaboration. She’s a specialist in participatory design and uses generative methods to investigate workflows, understand designer-developer experiences, and imagine ways to create UIs. In this work, Clara looks at how technology can be leveraged to help people make things, and do it more efficiently than they currently are.

In today’s context, that puts generative AI and machine learning right in her line of sight. The way this technology has boomed in recent times has many people scrambling to catch up - to identify the biggest opportunities and to understand the risks that come with it. Clara is a leader in assessing the implications of AI. She analyzes both the technology itself and the way people feel about it to forecast what it will mean into the future.

Contact Details:

You can find Clara in LinkedIn or on Twitter @cklimansilver

What role should artificial intelligence play in UX design process? 🤔

Clara’s expertise in understanding the role of AI in design comes from significant research and analysis of how the technology is being used currently and how industry experts feel about it. AI is everywhere in today’s world, from home devices to tech platforms and specific tools for various industries. In many cases, AI automation is used for productivity, where it can speed up processes with subtle, easy to use applications.

As mentioned above, the transformational capabilities of AI are met with equal parts of enthusiasm and skepticism. The way people use AI, and how they feel about it is important, because users need to be comfortable implementing the technology in order for it to make a difference. The question of what value AI brings to the design process is ongoing. On one hand, AI can help increase efficiency for systems and processes. On the other hand, it can exacerbate problems if the user's intentions are misunderstood.

Access for all 🦾

There’s no doubt that AI tools enable novices to perform tasks that, in years gone by, required a high level of expertise. For example, film editing was previously a manual task, where people would literally cut rolls of film and splice them together on a reel. It was something only a trained editor could do. Now, anyone with a smartphone has access to iMovie or a similar app, and they can edit film in seconds.

For film experts, digital technology allows them to speed up tedious tasks and focus on more sophisticated aspects of their work. Clara hypothesizes that AI is particularly valuable when it automates mundane tasks. AI enables more individuals to leverage digital technologies without requiring specialist training. Thus, AI has shifted the landscape of what it means to be an “expert” in a field. Expertise is about more than being able to simply do something - it includes having the knowledge and experience to do it for an informed reason. 

Research and testing 🔬

Clara performs a lot of concept testing, which involves recognizing the perceived value of an approach or method. Concept testing helps in scenarios where a solution may not address a problem or where the real problem is difficult to identify. In a recent survey, Clara describes two predominant benefits designers experienced from AI:

  1. Efficiency. Not only does AI expedite the problem solving process, it can also help efficiently identify problems. 
  2. Innovation. Generative AI can innovate on its own, developing ideas that designers themselves may not have thought of.

The design partnership 🤝🏽

Overall, Clara says UX designers tend to see AI as a creative partner. However, most users don’t yet trust AI enough to give it complete agency over the work it’s used for. The level of trust designers have exists on a continuum, where it depends on the nature of the work and the context of what they’re aiming to accomplish. Other factors such as where the tech comes from, who curated it and who’s training the model also influences trust. For now, AI is largely seen as a valued tool, and there is cautious optimism and tentative acceptance for its application. 

Why it matters 💡

AI presents as potentially one of the biggest game-changers to how people work in our generation. Although AI has widespread applications across sectors and systems, there are still many questions about it. In the design world, systems like DALL-E allow people to create AI-generated imagery, and auto layout in various tools allows designers to iterate more quickly and efficiently.

Like many other industries, designers are wondering where AI might go in the future and what it might look like. The answer to these questions has very real implications for the future of design jobs and whether they will exist. In practice, Clara describes the current mood towards AI as existing on a continuum between adherence and innovation:

  • Adherence is about how AI helps designers follow best practice
  • Innovation is at the other end of the spectrum, and involves using AI to figure out what’s possible

The current environment is extremely subjective, and there’s no agreed best practice. This makes it difficult to recommend a certain approach to adopting AI and creating permanent systems around it. Both the technology and the sentiment around it will evolve through time, and it’s something designers, like all people, will need to maintain good awareness of.

Seeing is believing

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