MaxDiff, or Maximum Difference Scaling, is a valuable statistical technique for identifying what matters most to your target audience. Rather than asking people to rate items on a scale (which often leads to vague or inflated results), MaxDiff forces a prioritization: respondents must pick the best and worst options from a set. Over time, their patterns reveal clear preference rankings. It’s a favorite tool among insights teams for prioritization research – whether for product features, marketing claims, or service benefits.

So, what’s different about multi-level MaxDiff? In a standard MaxDiff study, you’re limited by how many items you can ask people to compare at once – usually 10 to 20. Trying to test more than that becomes overwhelming and decreases data quality. Multi-level MaxDiff solves this by separating the question into stages or 'levels' – typically a hierarchical design – that allows broader attribute exploration without cognitive fatigue.

Key reasons to use multi-level MaxDiff:

• Handle longer lists – Break down large sets of features or benefits into structured tiers for easier evaluation
• Prioritize within themes – Understand preferences across multiple categories or product types
• Reduce respondent fatigue – Keep surveys shorter and more engaging by narrowing focus at each level
• Enable segmentation analysis – Capture clear patterns across audience groups and apply them in decision-making

For example, imagine you’re evaluating 30 potential features for a new health app. Rather than expecting participants to rank all 30 at once, a multi-level MaxDiff structure lets you group them into three logical categories (e.g. tracking, motivation, partnership), test them separately, then re-test the top features across categories at a second level. The result? A well-organized priority ranking based on real user input, not guesswork.

Whether you’re comparing conjoint vs MaxDiff methods or exploring hierarchical MaxDiff survey tips for the first time, it’s clear this structured approach offers clarity and flexibility. For business leaders and insights teams alike, multi-level MaxDiff offers a practical path to richer data – especially when enhanced with expert support, like the On Demand Talent network at SIVO. These are experienced professionals ready to step in and help you build studies that are not only executable, but also strategically aligned with your business goals.

AYTM (Ask Your Target Market) offers intuitive tools that make survey creation easier, especially for researchers looking to DIY their own MaxDiff studies. When it comes to multi-level MaxDiff, AYTM gives users some powerful options to structure complex studies – provided you know how to set things up the right way.

Below is a simple overview for how to run a MaxDiff study in AYTM with a multi-level design:

Step 1: Define Your Attribute List

Begin by gathering all the items you want to test – these are called attributes. For example, if you’re evaluating product features, these might include things like 'custom alerts', 'offline access', or 'integration with wearables'. Keep your initial list broad, then organize it into meaningful categories. This step is critical in choosing MaxDiff attributes that make sense for both the respondent and your final analysis.

Step 2: Group Attributes into Levels

Once your attribute list is finalized, separate it into levels. Typically, the first level will include groupings of related items (e.g. features, benefits, pricing elements), and the second level will combine top-performing items across those groups into a final comparative set. This approach supports a multi-level MaxDiff structure where you're narrowing down preferences by layer.

Step 3: Create Individual MaxDiff Questions in AYTM

AYTM allows you to easily build multiple MaxDiff elements using its 'Advanced Question Type' feature. Start with separate MaxDiff blocks for each first-level category, customizing the presentation and ensuring a balanced design. Then, based on those results, build your next level – typically only with a small set of finalist attributes.

Step 4: Segment Responses

Use AYTM’s built-in segmentation tools to analyze results by demographic or behavioral segments. This gives deeper insights into how different customer profiles prioritize features or messages. Running a segmentation analysis in a MaxDiff setup can highlight valuable business opportunities or product gaps.

Step 5: Review and Interpret Final Rankings

After launching the study and capturing responses, AYTM’s reporting dashboard provides clear priority rankings along with statistical outputs. Pay particular attention to what stands out for each segment – and where overlaps occur. For example, executives may prefer cost-saving features, while frontline employees value usability. Insights like this are what make MaxDiff prioritization research so actionable.

Tips to make the most of AYTM MaxDiff:

• Use clear, easily understood language for attributes
• Limit each MaxDiff question to 4–6 items to avoid fatigue
• Plan ahead for level 2 questions based on hypothetical top picks
• Don’t overlook the value of human input – a second opinion from an experienced researcher can refine your setup significantly

While AYTM is among the most user-friendly DIY survey tools on the market, guidance still matters when navigating complex techniques like multi-level MaxDiff. That’s where SIVO’s On Demand Talent is invaluable: our seasoned consumer insights professionals can partner with your team to get the technical setup right, align study goals with business outcomes, and help internal teams build capability for future research. Our professionals don’t just run the tools – they ensure the insights are actionable, strategic, and high-quality.

Choosing the right attributes is one of the most critical steps when setting up a MaxDiff study, especially in a multi-level structure. Whether you're prioritizing product features, marketing claims, service benefits, or design elements, your attributes must be clear, distinct, and relevant to the respondents. Poorly chosen or overlapping attributes can lead to confusing results or misrepresented priorities, which defeats the purpose of using a tool as precise as MaxDiff.

What Makes a Good MaxDiff Attribute?

Think of each attribute as a unique item you're asking your audience to rank against others. In an AYTM multi-level MaxDiff study, the goal is to organize these in a way that mirrors decision-making in the real world. Each attribute should:

• Represent one single idea (avoid compound or vague statements)
• Be easy to understand at a glance
• Avoid excessive buzzwords or technical terms unless your audience knows them
• Reflect real differentiators – not “table stakes” that all brands must offer

Grouping Attributes into Hierarchical Buckets

Multi-level MaxDiff works best when attributes are grouped by themes or categories. For example, let’s say you’re researching a new meal kit service. Groupings might include Price and Value, Health and Nutrition, and Convenience and Customization. Each lower layer then lets you dig into deeper, specific preferences – such as keto meals under Health, or delivery flexibility under Convenience.

This layered design avoids overwhelming respondents by showing them just a few comparisons at a time, while still capturing nuanced preferences. To determine your grouping structure, consider these steps:

1. List all possible attributes through brainstorming or early qualitative research
2. Identify natural themes or related ideas across the list
3. Organize these into 2–3 high-level groups for your top layer MaxDiff
4. Build more specific sub-groups for deeper MaxDiff layers

Remember, there is no single “best way to structure a MaxDiff survey,” but clarity and consistency are key. Many first-time users of DIY survey tools like AYTM benefit from mapping out the full structure visually before programming the survey.

When in doubt, start simple and scale. Testing a smaller set of well-chosen attributes within distinct groups often yields more actionable insights than overloading your survey.

Once your multi-level MaxDiff survey is complete and data collected, the next step is making sense of it in a way that drives decisions. In AYTM, you’ll receive utility scores (also called part-worths) that represent the relative importance or desirability of each attribute. But how do you go deeper from there – especially when your study includes segmentation and multiple hierarchical layers?

Understanding Utility Scores and Priority Ranking

At its core, MaxDiff provides a priority ranking of items based on tradeoff decisions, not just likeability. For each respondent – or segment of respondents – AYTM calculates how strongly each attribute was preferred over others in its set. Segmenting these results by demographics, behaviors, or attitudinal profiles can uncover meaningful differences in what matters most across groups.

For example, in a fictional MaxDiff study testing 15 product features among two customer segments – budget-conscious vs. premium-seekers – you may find that “lowest price” outperforms all others for budget shoppers, while “premium ingredients” tops the list for the premium group.

Making Sense of Multiple MaxDiff Layers

In a multi-level MaxDiff, each layer reveals insights about decisions made at different levels of abstraction. Suppose Layer 1 grouped features into three themes: Value, Quality, and Flexibility. You can determine that Flexibility is most important overall – but then use Layer 2 and 3 results to see which aspects of Flexibility (e.g., cancel-anytime, delivery customization, pause options) are driving that interest.

This layered narrative helps teams:

• Connect higher-level brand strategy decisions to executional details
• Tailor offerings and messaging by segment priorities
• Focus development budgets on what truly drives preferences

Visualization tools inside AYTM can support this segmentation analysis, but expert guidance can be especially valuable when comparing across complex subgroups. Look for patterns where attribute preferences shift based on behavior (e.g., frequent users vs. light users), life stage, or even geography. The added nuance is often where your deepest insights live.

DIY platforms like AYTM have opened the door for more teams to run MaxDiff studies quickly and affordably. But when it comes to more complex structures like multi-level MaxDiff or interpreting highly segmented data, expert input can make the difference between “just data” and truly actionable insights.

Situations That Benefit from Expert Guidance

MaxDiff is more than a plug-and-play method – especially when your research stakes are high or when the number of attributes and sample segments grows. Consider bringing in an experienced consumer insights expert if:

• You’re unsure how to group or structure attributes into logical levels
• You're managing a large list of attributes that need refinement or pruning
• Your study includes multiple customer segments with different needs
• You need help interpreting utility scores in a business context
• You’re translating insights into specific product, marketing, or CX decisions

Experts – like those in SIVO's On Demand Talent network – regularly assist with MaxDiff design, survey implementation, and advanced analysis. With deep experience across categories and business models, they help tailor the approach to your specific objectives and ensure your insights have impact.

Why Choose On Demand Talent Over Other Options?

Unlike freelancers or generalist consultants, On Demand Talent professionals are embedded insights experts who can jump in quickly without the learning curve. They’re not just survey programmers – they’re strategic partners who understand the “why” behind the design and can help interpret the “what now” from results.

Whether you’re building internal capabilities or need temporary support for high-stakes research, On Demand Talent offers flexible access to senior expertise that complements your team. And since they’re matched specifically to your needs, you gain targeted support – not just bandwidth.

As DIY survey tools and AI-enabled platforms gain traction, success will increasingly rely not just on access to data, but on the ability to make sense of it intelligently. Experienced hands ensure your research stays on purpose – and on impact.