Yabble is an advanced AI-powered research platform designed to help businesses generate and analyze consumer insights more efficiently. Positioned among the newest generation of DIY research tools, it uses artificial intelligence to speed up tasks like survey creation, qualitative data interpretation, and language analysis. Its appeal lies in allowing teams to run quick-turn research and extract sentiment or themes without a lengthy full-service research engagement.

One of the most popular uses for Yabble is in claims testing – a type of research designed to understand how well marketing statements, product claims, or positioning messages resonate with target audiences. The goal of claims testing is to determine which messages people find the clearest, most believable, and emotionally compelling. This process is also referred to as proposition diagnostics. By using Yabble, teams can test multiple statements at once and use AI-generated analysis to interpret responses faster.

Ways companies commonly use Yabble for claims testing:

• Assessing clarity: Identifying if a claim is understood the way it’s intended.
• Evaluating believability: Gauging whether people trust or find the claim credible.
• Understanding emotional resonance: Discovering how people feel about the message.
• Comparing message versions: Testing different claims to see which performs best.

For example, a snack brand might use Yabble to test different claims about product benefits such as “Made with Real Ingredients” versus “Guilt-Free Snacking.” The platform helps them collect consumer feedback through surveys or open-ended responses, and then uses natural language processing to generate insights from the input.

Why teams choose DIY research tools like Yabble

Companies of all sizes – from startups to Fortune 500 brands – are increasingly adopting DIY platforms to move faster and handle more research in-house. Tools like Yabble help teams stay nimble, especially when budgets or timelines make traditional market research less feasible. Plus, with built-in AI models, they offer a tempting level of automation for subjective tasks like emotional analysis or language comparisons.

However, despite these advantages, using platforms like Yabble for claims testing isn’t without challenges. The results you get are only as good as the design, input, and interpretation – which is where expert guidance becomes critical. Let’s take a closer look at the common issues that can arise.

Yabble’s speed and automation are major advantages – but they can also introduce risk if not used strategically. Without an experienced researcher to guide the approach and interpret findings, teams may unknowingly fall into several common traps during claims testing. Let’s explore where things can go wrong when using AI research platforms for proposition diagnostics.

1. Misinterpreting participant language

One of the most powerful features of Yabble is its ability to analyze open-ended feedback using AI. But without someone trained in research interpretation, teams may take AI-generated summaries or sentiment scores at face value. Natural language processing is improving but still struggles with nuance, sarcasm, and cultural context.

For example, if participants say a claim is “interesting,” does that mean they like it, they’re confused by it, or something else entirely? Only a skilled insights professional can dig into the context and probe for meaning – which matters significantly when aligning research to business decisions.

2. Overlooking emotional resonance

AI can identify themes and tone, but human emotion isn’t always neatly categorized. Teams relying solely on Yabble's automated emotional analysis might miss subtle but important indicators of how a message truly lands. Emotional resonance is critical in marketing – it drives motivation, brand affinity, and ultimately purchase decisions.

When On Demand Talent joins a project, they help shape questions that tap into emotional response more accurately and know how to interpret those cues beyond “positive” or “negative” labels. This leads to richer insight and better guidance for creative strategy.

3. Lack of clarity in claim design

Clear claims make for useful diagnostics. But in the rush to test quickly, some teams input claims that are too long, too vague, or packed with jargon. Even a powerful platform like Yabble can’t give you good answers if the questions aren’t clear.

Experts in proposition diagnostics understand how to write response-friendly language that consumers interpret consistently. They also know how to structure the research flow to avoid bias and confusion, both of which can skew Yabble’s AI summaries.

4. Data overload without story

DIY research tools produce data – lots of it. But transforming that data into a clear story takes more than charts and dashboards. One challenge teams often face is knowing what to prioritize and how to connect the insights back to the original research objective or the broader business strategy.

This is where bringing in On Demand Talent adds real value. These professionals help synthesize the findings, highlight what matters most, and connect insights to outcomes. That way, research doesn’t just get done – it gets used.

Want to avoid these pitfalls?

• Involve an experienced insights expert early in your planning.
• Use human interpretation to validate AI-generated findings.
• Focus on clarity, emotional nuance, and actionable storytelling.

With the right support, your team can make the most of Yabble and other DIY research tools – unlocking smarter, faster, and more meaningful claims testing results.

DIY research tools like Yabble are powerful, fast, and cost-effective. But when it comes to something as nuanced as claims testing or proposition diagnostics, tools alone aren't enough. Strategic research guidance – the human side of insights – plays a critical role in ensuring your results are accurate, actionable, and aligned with your business objectives.

AI Can’t Replace Human Context

Yabble's AI research platform excels at automating tasks like clarity testing, emotional resonance analysis, and summarizing participant language. But even the most advanced platform can't interpret tone, intent, or subtle contradictions in survey responses the way an experienced researcher can.

For instance, a phrase like “It sounds nice, but I’m not sure I believe it” could be categorized as positive sentiment by an algorithm, but a strategic researcher would flag it as a red flag for believability. Without someone to interpret the “why” behind responses, results can be misleading – and lead you to pursue the wrong message or miss potential risk areas entirely.

Staying Objective and Business-Aligned

Another issue with unsupervised DIY claims testing lies in objectivity. When teams write, test, and interpret their own messaging, bias can creep in. What sounds clear internally may not resonate with customers – and that insight can easily be overlooked without strategic guidance to challenge assumptions.

Strategic research professionals bring:

• Objective perspectives to eliminate internal bias
• Experience mapping research questions to business goals
• The ability to spot weak claims early and suggest alternatives

Think of it this way: running Yabble without expert guidance is like flying a plane with advanced autopilot, but without a trained pilot onboard. It may seem smooth, until something doesn’t go to plan.

Avoiding the Pitfalls of Misinterpretation

Strategic guidance also prevents overconfidence in dashboards and charts. AI-generated results can appear definitive, but often require deeper questioning:

• Are participants reacting to the claim itself – or to ambiguous wording?

• Is emotional resonance stemming from the product feature, or how it’s framed?

Experts know how to dig below the surface and validate whether results are truly serving your objectives. That’s why strategic research guidance isn’t an option in DIY tools – it’s a necessity.

Yabble and other DIY research tools empower teams to move quickly – but success depends on getting the details right. That’s where SIVO’s On Demand Talent can elevate your claims testing from useful to business-changing.

Filling the Gaps in DIY Research Execution

While Yabble can handle data collection and preliminary analysis, many teams lack the in-house expertise to:

• Craft unbiased, outcome-oriented research questions
• Write claims that test the right hypotheses
• Analyze results with insight, not just interpretation
• Translate feedback into messaging strategies

On Demand Talent jump in quickly to support specific stages of your testing process – whether that’s validating your study design, reviewing emotional resonance outputs, or helping turn data into business-ready storytelling.

Speed, Experience, and Flexibility – Without Sacrifice

The challenge for many teams is resourcing. You need someone now, but aren’t hiring full-time. Freelancers can be hit-or-miss. Traditional agencies take time to mobilize. SIVO’s On Demand Talent fills that gap with seasoned professionals who can embed with your team in days – not months – across industries and initiative types.

This is especially helpful when dealing with time-sensitive proposition diagnostics – say, refining a launch message, repositioning a product, or evaluating multiple claims under tight timelines. Instead of scrambling with limited internal bandwidth, On Demand Talent offers you the ability to do it right, faster.

Support at Any Stage – Not Just After the Fact

Whether you’re comparing clarity of multiple propositions or trying to improve believability in claims, it’s often difficult to course-correct after the research is complete. That’s why our experts can help before, during, or after your Yabble research:

• Pre-field guidance ensures your claims and surveys are clear and unbiased

• In-field monitoring allows real-time adjustments where necessary

• Post-field debriefs turn raw outputs into strategic insights

By integrating On Demand Talent in your DIY research process, you maximize your platform investment while avoiding costly do-overs or missed insights. It’s not about replacing tools – it’s about enhancing how you use them.

Using a platform like Yabble for claims testing isn’t just about speeding up research – it’s about evolving the capabilities of your team. And the best way to do that? Combine the efficiency of AI tools with the expertise of real humans who can guide, teach, and elevate your approach.

Why Skills Development Can’t Be an Afterthought

As more insights teams adopt DIY and AI-led testing tools, one trend is clear: the teams who succeed long-term are the ones who invest in learning, not just execution. While Yabble makes it easy for anyone to launch a clarity or emotional resonance test, interpreting results and applying them effectively still requires a deep understanding of research principles.

On Demand Talent acts as an on-the-ground mentor. They not only direct projects, but also coach internal teams through best practices, proper setup, and how to spot – and fix – common mistakes in proposition diagnostics.

Examples of Lasting Team Growth

Hiring fractional research professionals isn’t just a short-term fix. It’s a way to accelerate the learning curve of your full-time staff. From fine-tuning claim wording to identifying emotional response patterns, our experts leave teams stronger and more capable – even after the project ends.

For example, a (fictional) CPG brand brought in On Demand Talent to guide their first round of DIY claims testing in Yabble. Beyond shaping a strong research plan, the expert also developed templates, trained junior staff on AI interpretation, and built onboarding materials the client now uses team-wide. That single project became a launchpad for their future success with AI tools.

Creating Scalable Research Maturity

As businesses rely more on self-serve market research tools, internal capability becomes critical. On Demand Talent helps bridge that gap – working hands-on and side-by-side until your team is confident, skilled, and ready to operate autonomously with platforms like Yabble.

This investment pays off in higher quality outputs, better stakeholder confidence, and faster go-to-market cycles. It’s not about replacing headcount – it’s about developing research maturity inside your team, one relevant project at a time.

Put simply, pairing talented people with smart AI doesn't just streamline research – it builds lasting insight leadership from within.