In concept testing and U&A research, the goal is to capture accurate perceptions, preferences, and motivations tied to specific products, services, or categories. Attributes – the descriptive words or phrases used to evaluate those offerings – function as the building blocks of this feedback. Common examples include words like “affordable,” “refreshing,” or “premium-quality.” But while individual attributes may seem simple, organizing them into a clear and intentional framework requires strategic thinking.

So why does the attribute framework matter so much?

Attributes Shape Consumer Feedback

Consumers react based on the options they're given. If your attributes are vague, overlapping, or irrelevant, the data you collect will miss the mark. That risk multiplies when running multiple research projects across teams or timelines. By standardizing how options are presented, attribute frameworks ensure that comparisons are clean and insights remain meaningful.

They Drive Better Analytics and Profiling

Frameworks also make analysis more powerful. A consistent set of well-defined attributes allows you to benchmark performance across concepts or track how perceptions shift over time. In profiling research, it helps differentiate segments or uncover white space with greater precision.

They Support Smarter Decision-Making

When attributes are aligned with business goals and customer needs, your research becomes a clearer decision-making tool – not just a data dump. Teams can confidently answer questions like: "Which claims are most motivating?" or "Which features align with our brand equity?"

They Enable Scaling and Efficiency

As brands expand their use of research tools like Dynata, having a consistent attribute framework allows for faster execution, less rework, and smoother collaboration across internal teams or research partners. It also opens the door to automation, AI integration, and more sophisticated long-term tracking.

Common Pitfalls Without Strong Frameworks

• Inconsistent attribute definitions across teams or studies
• Overlapping attributes that confuse respondents
• Missing crucial emotional or functional attributes that drive decisions
• Difficulty benchmarking or comparing across concepts

Ultimately, good research starts with the right questions – and attribute frameworks are how those questions take shape. And as more organizations embrace DIY platforms to conduct concept testing and U&A studies, the importance of having expert-led attribute development becomes even more pronounced. Experienced On Demand Talent from SIVO can help insight teams avoid common missteps and build custom attribute lists that reflect real consumer language and business relevance.

An effective attribute framework isn't just a list of positive-sounding terms – it's a carefully structured system rooted in your research objectives, target users, and category context. Whether you're profiling a product concept or segmenting users in a U&A study, the right framework will give you clarity, comparability, and actionable direction.

Key Traits of a Strong Attribute Framework

Here are a few hallmarks of effective attribute list design for consumer research:

• Clear and Distinct Wording: Each attribute should have a specific meaning, avoiding overlap. For instance, “convenient” and “easy to use” might seem different, but in consumers’ minds, they may signal the same thing unless clearly distinguished.
• Balanced Coverage: Include both emotional and functional attributes. Many concept tests overlook emotional drivers like “comforting” or “fun,” which can be key motivators.
• Relevant to the Category: Attributes should reflect what consumers actually care about in your product’s category. What works for skincare won’t necessarily apply to beverages.
• Consistent Across Studies: Use a core set of attributes to allow longitudinal tracking and benchmarking, while maintaining adaptability for specific use cases.
• Phrased from the Consumer’s Perspective: Avoid corporate jargon or internal language. Instead of “value proposition,” you might use “worth the price.”

Why Building Attribute Frameworks Is a Team Sport

Creating a solid attribute structure often requires cross-functional input. Marketers bring brand objectives, insights teams bring research rigor, and external experts bring experience across verticals. SIVO’s On Demand Talent professionals, for example, often help facilitate these discussions to ensure alignment on what really matters – before the research hits the field.

How Many Attributes Should You Use?

There’s no one-size-fits-all answer, but quality beats quantity. In most Dynata studies or U&A research, 8–15 well-defined attributes is a common sweet spot. Too few, and you may miss important distinctions; too many, and you risk respondent fatigue or diluted insights.

Making Your Framework Work Harder

Effective attribute frameworks also pay off long after the survey ends. With a consistent structure in place, you can:

• Build dashboards with clean, comparable data
• Enable AI tools to parse feedback and perform text analysis more effectively
• Train internal teams to design better self-service studies

When done right, this upfront investment creates lasting business value. But getting there takes experience – not just tool fluency. That’s where SIVO’s On Demand Talent solution proves especially valuable. Our fractional insights experts can guide your team to create custom attribute lists aligned to your strategic goals. Whether you’re launching a one-off Dynata concept test or refreshing an entire tracker, having the right framework in place ensures your research delivers real impact, not just data points.

Dynata is a powerful platform for quantitative data collection, especially in consumer research. However, to get high-quality results from concept testing, usage & attitude (U&A) studies, and profiling research, you need more than just access to the tool – you need a well-designed attribute framework. Choosing the right attributes and organizing them effectively ensures your survey captures what truly matters to your audience and delivers actionable insights.

Start with Clear Attribute Definitions

Every attribute in your list should be easy to understand by both internal teams and research participants. That means simple, specific wording with as little ambiguity as possible. For example, instead of using the word “affordable,” which can be subjective, consider “priced lower than similar brands” for better clarity.

Group Attributes Logically

Categorizing attributes into dimensions – like product performance, emotional benefits, brand perception, or price/value – helps keep survey design consistent and makes data analysis easier. Grouped attributes also help respondents compare similar traits, improving data quality.

Tips for Structuring Attribute Lists in Dynata:

• Keep scales consistent, especially across waves or markets (e.g., 5-point agreement, likelihood to consider)
• Use neutral, non-leading language to avoid bias
• Limit each attribute to one concept (avoid “tastes great and is low calorie”)
• Ensure attribute lists are not overly long – too many items can lead to respondent fatigue

Tailoring for Concept Testing vs. U&A Research

Attribute needs will vary based on study goals. For concept testing, focus on product features, benefits, and purchase drivers. In U&A research, you’ll need broader dimensions that reflect psychographics, habits, and values.

Here’s a quick comparison of how attributes may shift:

• Concept Testing: innovative, convenient, high performance, premium quality
• U&A Studies: lifestyle fit, trusted brand, used by peers, sustainable

By taking the time to organize a thoughtful, structured attribute list, researchers can unlock more precise analytics and make meaningful comparisons across demographic groups or time periods.

Developing an effective attribute framework sounds straightforward, but in practice, it presents several common hurdles that can impact research quality if left unaddressed. Many insights professionals, particularly those newer to study design, encounter inconsistencies or inefficiencies when crafting attributes for concept testing or U&A research.

Ambiguity in Attribute Wording

One of the biggest issues is unclear or vague attributes. Phrases like “modern” or “fun” might seem easy to include, but they can mean different things to different respondents. This impacts how data is interpreted and reduces consistency across studies. Strive for wording that the average consumer would interpret the same way, across age groups, geographies, or segments.

Overlapping or Redundant Attributes

Including too many attributes that essentially say the same thing – such as “natural” and “organic” – may confuse respondents and complicate your analysis. Redundancy muddies what attributes are truly driving preference or attitudes.

Lack of Alignment Across Research Teams

In larger organizations, different brand or product teams often develop their own attribute lists. Without centralized coordination or guidelines, this results in inconsistent frameworks across studies, making it difficult to compare or track data longitudinally. Shared frameworks or templates can help solve this.

Tool Limitations or Overload

DIY tools like Dynata empower businesses to run research quickly and affordably, but when attribute frameworks are underdeveloped, the outputs risk being shallow. Even more, trying to include every possible attribute in one wave can lead to respondent fatigue and less reliable data.

Watch Out for These Pitfalls:

• Using passive voice or marketing-centric terms that don’t translate well into survey language
• Forgetting to validate new or unfamiliar attributes before full fielding
• Not revisiting or refining attribute lists over time to reflect changing market trends

A thoughtfully crafted attribute framework can make all the difference – ensuring your research sees past the noise and into real consumer drivers. When clarity, consistency, and consumer understanding are missing, even the most well-funded study may fall flat.

While tools like Dynata provide the infrastructure for collecting data, it’s experienced human researchers who shape the quality of that data through smart design. On Demand Talent from SIVO gives your team immediate access to seasoned consumer insights experts who specialize in research study design – particularly the critical task of crafting well-structured, consistent attribute frameworks.

Flexible Expertise On Your Terms

Whether you need to build a new attribute framework from scratch or refine lists that have grown chaotic over time, our On Demand Talent works flexibly within your systems and timelines. These professionals hit the ground running, collaborating with your internal teams, aligning stakeholder needs, and optimizing both speed and quality.

Unlike freelancers or one-size-fits-all consultants, SIVO’s talent is embedded with purpose: to build long-term research capability and enhance the impact of your insights strategy.

How On Demand Talent Supports Attribute Development:

• Guiding brainstorming sessions to define relevant, consumer-friendly attributes
• Auditing existing attribute frameworks for clarity, overlap, and consistency
• Aligning attribute lists across concept testing, U&A, or segmentation studies
• Collaborating with analytics and marketing teams to ensure attributes yield actionable outcomes
• Training in-house teams on how to evolve and reuse frameworks effectively over time

Picture this fictional scenario: A mid-size snack brand begins testing new flavor concepts using Dynata. Initially, their attribute list includes terms like “exciting,” “unique,” and “bold.” But the team struggles to interpret the results – what does “exciting” actually mean to their audience? By bringing in On Demand Talent, they rewrote their list using precise language like “contains unexpected ingredient” or “spicy flavor profile,” improving data clarity and accelerating product decision-making.

In today’s fast-paced, leaner research environments, it’s not enough to run surveys quickly – the input design must be just as sharp as the speed. On Demand Talent ensures your investment in DIY tools like Dynata pays off by driving more targeted, high-impact outcomes.