June 23, 2025

Turn User Feedback Into Product Breakthroughs: Smart Surveys That Drive Real Decisions

Optimal Surveys helps product, design and research teams capture the user insights that actually drive decisions—from feature validation to user journey optimization. Now it's getting even smarter.

Research shows that when surveys are customized, people give more thoughtful answers and are less likely to drop out.

That’s why we’re really excited to roll out one of our most requested survey features: Display Logic!

This new capability creates truly dynamic surveys that eliminate irrelevant questions and reduce drop-off rates. Instead of moving users through generic questionnaires, Display Logic shows each participant only what matters to them, giving you higher-quality data and more targeted insights.

Combined with our existing branching logic, you now have complete control over creating survey experiences that feel personal, not repetitive.

Why Dynamic Surveys Matters


Better data quality

When participants only see relevant questions, their answers are more thoughtful and accurate. More focused questions mean better insights.


More targeted insights

Use previous responses to drill deeper into specific topics—or skip over areas that don’t apply. You’ll uncover richer insights without extra noise.


Faster, more focused studies

Customizing the survey cuts out extra questions, keeps participants engaged, and helps them move through faster. Plus, a better experience means they’re more likely to take part in your future research.


What You Can Do with Display Logic

  • Set multiple logic conditions for one question
  • Show or hide questions or answers based on earlier responses from radio, Likert, and dropdown questions
  • Apply logic across screeners, pre- and post-study questions, and survey questions

Smarter Optimal Surveys


We've been doubling down on making Optimal surveys both user-friendly and best-in-class for delivering insights. To help you get the most out of your surveys, we’ve added AI Simplify to suggest clearer, more effective question wording to help you engage participants and get higher-quality data.

We’ve also recently launched automated Insights for open-text responses. This feature takes the grunt work out of analysis by instantly surfacing key themes from open-text and matrix responses.

These are just a few of the ways we’re shaping Optimal into one of the most thoughtful and effective survey tools out there. With powerful AI features like question writing and instant insights built right in, we’re making it easier than ever to go from idea to impact.

Whether you're running usability studies, product tests, or market research, Optimal’s display logic and other survey tools help you create cleaner, more efficient surveys from start to finish. Start tailoring your surveys today to drive data-backed decisions.


Not yet using Optimal? Start your free 7-day trial and launch your first survey now.

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Qualitative insights: Reimagined and supercharged 🚀

We're thrilled to announce the re-launch of our Qualitative Insights tool, formerly known as Reframer. This powerful upgrade brings new features designed to revolutionize your qualitative data analysis process, making it faster, easier, and more insightful than ever before.

Introducing the new Qualitative Insights 🔍

Qualitative Insights has always been your go-to tool to help you plan and organize interviews, take notes, tag, and analyze rich, unstructured data. Now, we've taken it to the next level with two game-changing additions:

  • Insights feature: A dedicated space to capture, organize, and communicate your key takeaways.
  • AI capabilities: Optional AI-powered assistance to accelerate your analysis process.

Discover insights effortlessly 💡 

The new Insights feature transforms how you work with qualitative data:

  • Centralized hub: All your analytical discoveries in one place.
  • Structured insights: Each insight includes a title, detailed description, and associated observations.
  • Flexible viewing: Toggle between overview and deep-dive modes.
  • Efficient organization: Tag and categorize insights for easy retrieval.
  • Collaboration tools: Share and discuss findings with your team.

How it works 🛠️ 

Manual insight creation

  1. Filter your data using keywords, tags, affinity map groupings, tasks, segments, and sessions.
  2. Select relevant observations.
  3. Craft your insight with a custom title and description.

AI-Powered Insight Generation (Optional)

  1. Click "Generate" to activate our AI assistant.
  2. AI analyzes existing observations to produce new insights.
  3. Automatically generates insight titles, summaries, and attaches relevant observations.
  4. AI-generated insights are marked with an AI star symbol for easy identification.
  5. All AI insights remain fully editable.

AI: Your analysis assistant 🤖

Our AI capabilities are designed to enhance your abilities, not replace them. Use AI to:

  • Speed up insight discovery
  • Reveal hidden patterns in your data
  • Jumpstart the analysis process

Remember, your expertise is crucial. Always review and refine AI-generated insights to ensure accuracy and capture nuances that only human understanding can provide.

Your data, your choice 🔒 

We prioritize your privacy and data control:

  • Your data stays within your organization
  • We don't use it to train other AI models
  • You control when to use AI for insights
  • AI features can be turned on or off anytime

Get started today 🌟 

Ready to experience the power of the new Qualitative Insights? Learn more and dive in. Upgrade your qualitative analysis workflow and uncover deeper insights faster than ever before with Qualitative Insights!

Learn more
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

AI-Powered Search Is Here and It’s Making UX More Important Than Ever

Let's talk about something that's changing the game for all of us in digital product design: AI search. It's not just a small update; it's a complete revolution in how people find information online.

Today's AI-powered search tools like Google's Gemini, ChatGPT, and Perplexity AI aren't just retrieving information they're having conversations with users. Instead of giving you ten blue links, they're providing direct answers, synthesizing information from multiple sources, and predicting what you really want to know.

This raises a huge question for those of us creating digital products: How do we design experiences that remain visible and useful when AI is deciding what users see?

AI Search Is Reshaping How Users Find and Interact with Products

Users don't browse anymore: they ask and receive. Instead of clicking through multiple websites, they're getting instant, synthesized answers in one place.

The whole interaction feels more human. People are asking complex questions in natural language, and the AI responses feel like real conversations rather than search results.

Perhaps most importantly, AI is now the gatekeeper. It's deciding what information users see based on what it determines is relevant, trustworthy, and accessible.

This shift has major implications for product teams:

  • If you're a product manager, you need to rethink how your product appears in AI search results and how to engage users who arrive via AI recommendations.
  • UX designers—you're now designing for AI-first interactions. When AI directs users to your interfaces, will they know what to do?
  • Information architects, your job is getting more complex. You need to structure content in ways that AI can easily parse and present effectively.
  • Content designers, you're writing for two audiences now: humans and AI systems. Your content needs to be AI-readable while still maintaining your brand voice.
  • And UX researchers—there's a whole new world of user behaviors to investigate as people adapt to AI-driven search.

How Product Teams Can Optimize for AI-Driven Search

So what can you actually do about all this? Let's break it down into practical steps:

Structuring Information for AI Understanding

AI systems need well-organized content to effectively understand and recommend your information. When content lacks proper structure, AI models may misinterpret or completely overlook it.

Key Strategies

  • Implement clear headings and metadata – AI models give priority to content with logical organization and descriptive labels
  • Add schema markup – This structured data helps AI systems properly contextualize and categorize your information
  • Optimize navigation for AI-directed traffic – When AI sends users to specific pages, ensure they can easily explore your broader content ecosystem

LLM.txt Implementation

The LLM.txt standard (llmstxt.org) provides a framework specifically designed to make content discoverable for AI training. This emerging standard helps content creators signal permissions and structure to AI systems, improving how your content is processed during model training.

How you can use Optimal:  Conduct Tree Testing  to evaluate and refine your site's navigation structure, ensuring AI systems can consistently surface the most relevant information for users.

Optimize for Conversational Search and AI Interactions

Since AI search is becoming more dialogue-based, your content should follow suit. 

  • Write in a conversational, FAQ-style format – AI prefers direct, structured answers to common questions.
  • Ensure content is scannable – Bullet points, short paragraphs, and clear summaries improve AI’s ability to synthesize information.
  • Design product interfaces for AI-referred users – Users arriving from AI search may lack context ensure onboarding and help features are intuitive.

How you can use Optimal: Run First Click Testing to see if users can quickly find critical information when landing on AI-surfaced pages.

Establish Credibility and Trust in an AI-Filtered World

AI systems prioritize content they consider authoritative and trustworthy. 

  • Use expert-driven content – AI models favor content from reputable sources with verifiable expertise.
  • Provide source transparency – Clearly reference original research, customer testimonials, and product documentation.
  • Test for AI-user trust factors – Ensure AI-generated responses accurately represent your brand’s information.

How you can use Optimal: Conduct Usability Testing to assess how users perceive AI-surfaced information from your product.

The Future of UX Research

As AI search becomes more dominant, UX research will be crucial in understanding these new interactions:

  • How do users decide whether to trust AI-generated content?
  • When do they accept AI's answers, and when do they seek alternatives?
  • How does AI shape their decision-making process?

Final Thoughts: AI Search Is Changing the Game—Are You Ready?

AI-powered search is reshaping how users discover and interact with products. The key takeaway? AI search isn't eliminating the need for great UX, it's actually making it more important than ever.

Product teams that embrace AI-aware design strategies, by structuring content effectively, optimizing for conversational search, and prioritizing transparency, will gain a competitive edge in this new era of discovery.

Want to ensure your product thrives in an AI-driven search landscape? Test and refine your AI-powered UX experiences with Optimal  today.

Seeing is believing

Explore our tools and see how Optimal makes gathering insights simple, powerful, and impactful.