TL;DR: MaxDiff (Maximum Difference) scaling is a survey research method used to measure the relative importance of a large set of items, such as features, products, or messages, by forcing respondents to prioritize between options, helping you uncover true priorities.
When you ask customers in a survey whether specific features or benefits matter to them, the answers aren’t always helpful. In a typical rating-scale survey, people might rate nearly every feature, benefit, or message as “important.” That makes it difficult to see real priorities. If everything scores high, what should you focus on first?
MaxDiff Analysis is a research method designed to solve this problem.
Instead of asking people to rate a long list of items, it asks them to repeatedly choose the most important and least important option from a small group. By forcing trade-offs, MaxDiff reveals what people truly value when they have to make a decision.
This approach is widely used by:
In this article, we’ll cover why MaxDiff is needed, how it works, and a list of recommended tools to help you get started.
Let’s say you’re part of a product team that wants to improve your budgeting and personal finance app. Your goal is simple. You want to know what your customers care about so you can decide what to fix, improve, or promote next.
So you send out a survey asking users to rate the importance of the following from 1 to 10:
When the results come back, almost everything is rated an 8, 9, or 10. If speed is a 9.2 and ease of use is a 9.0, that doesn’t tell you what to focus on first. This is a common challenge in survey data analysis, where traditional rating scales often lead to this kind of skewed data, where everything seems important, and it’s difficult to know what will have the biggest impact.
MaxDiff takes a different approach, forcing customers to choose between options instead of rating everything on its own. As a result, teams move beyond surface-level ratings and gain a clearer way to prioritize product features, messaging, or customer experience improvements based on what truly matters most to their audience.
MaxDiff Analysis produces a relative importance score for each item being tested based on how often it is selected in trade-off exercises.
Instead of treating preferences as standalone ratings, MaxDiff models each selection as a comparison between competing options. Over multiple rounds, these comparisons accumulate to estimate how strongly each feature is preferred relative to the others.
Which of the following app features would you say are your highest and lowest priority when managing your personal finances?
Let’s go back to the app team that wants to improve their personal finance product but isn’t sure what matters most to users.
Instead of asking customers to rate things like app speed, price, personalization, or customer support on a scale from 1 to 10, they use MaxDiff to show respondents a small group of options at a time and ask two simple questions:
Which of these matters MOST to you?
Which matters LEAST?
The respondent selects one option as the most important and one as the least important. Then, they’re shown a new set of features, such as:
Again, they choose the most and least important option. After repeating this process across several rounds, the team can clearly see which features consistently rise to the top and which ones matter less to users.
Using the budgeting app example from earlier, here’s what the MaxDiff process looks like.
To determine which features should be included in a MaxDiff study, teams typically start with a broad list of potential features to evaluate. This list is often informed by exploratory, open-ended research that captures how users describe their needs, frustrations, and expectations in their own words.
Open-ended survey questions are especially useful at this stage because they reveal what truly matters to users without biasing responses through predefined options.
These qualitative open-ended responses produce rich insight, but they are unstructured and difficult to analyze at scale. Automated thematic analysis tools like Blix help researchers quickly process large volumes of text, identify recurring themes, and group similar ideas together.
Those themes can be translated into a clear, structured set of features or attributes that become the input for the MaxDiff analysis, ensuring the study reflects real user priorities and language, rather than internal assumptions.
Instead of showing users that full list of features all at once, MaxDiff presents a small subset of those features in each question.
For example, one user might see:
Then in the next question, they might see a different combination, such as:
Each respondent typically answers anywhere from 8 to 15 of these sets, depending on the size of the feature list. This allows every feature to appear multiple times in different groupings, giving users repeated opportunities to make trade-offs between what matters most and least.
Each “most” and “least” choice creates a comparison between the items shown in that set. As respondents move through the survey, these trade-off decisions build across multiple questions and participants, gradually forming a pattern of preferences.
These repeated choices are then analyzed to estimate how important each feature is relative to the others. In simple terms, the model looks at how often a feature was selected as most or least important across all tasks.
This results in a ranked list of features based on what users consistently prioritize, helping the app team decide what to improve, build next, or highlight within the user experience.
There are dedicated tools available to run this type of analysis, which we’ll cover later in this article.
MaxDiff helps teams move beyond unclear survey results and gain a more accurate understanding of what their audience truly values. Here are some of the key benefits:
While MaxDiff can provide clearer preference data, there are a few challenges to keep in mind:
Here are the teams and industries that benefit from MaxDiff Analysis.
Market research teams often need to understand what drives customer decisions before launching a product or campaign. For example, a food delivery company may want to know whether users care more about fast delivery, lower fees, restaurant variety, or real-time tracking. Using MaxDiff helps them see which features customers consistently prioritize when forced to choose, making it easier to focus their messaging or development efforts.
Customer experience teams can use MaxDiff to understand which parts of the user journey matter most. For instance, an airline CX team may compare the importance of faster check-in, in-flight Wi-Fi, loyalty rewards, or baggage handling. This gives them a clearer view of what drives satisfaction so they can focus on the most impactful improvements.
HR teams can use MaxDiff to understand which benefits or workplace initiatives matter most to employees. For example, a company may want to compare the importance of flexible work hours, professional development programs, or remote work options. These trade-offs can help inform retention strategies based on what employees truly value.
Product management teams often need to prioritize features during development cycles. For instance, a SaaS company might compare the importance of automation tools, reporting dashboards, or mobile access. MaxDiff helps identify which capabilities deliver the greatest perceived value to users before release decisions are made.
Brand and marketing teams can use MaxDiff to test messaging or positioning strategies. For example, a retail brand might compare tagline options such as “affordable quality,” “sustainably made,” or “fast delivery.” This strategy helps reveal which differentiators stand out most in competitive markets before launching a campaign.
A range of statistical tools can be used to support MaxDiff survey design and modeling. Once survey responses are collected, these tools help analyze repeated “most” and “least” choices to estimate how important each item is relative to the others.
Some teams choose to run MaxDiff analysis using data science platforms like R or Python. In these cases, researchers typically need a strong technical background to manually structure the survey design, prepare the choice data, and apply statistical models such as Hierarchical Bayes or multinomial logit. This approach allows for more customization but often requires coding knowledge and experience working with modeling frameworks.
Below are some popular tools used to support MaxDiff analysis:
| Tool | Best for | Description |
|---|---|---|
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Advanced modeling | Sawtooth Software Offers dedicated MaxDiff survey design and analysis features with built-in Hierarchical Bayes modeling. |
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Small to mid-sized research teams | Conjointly Provides guided workflows for designing and analyzing MaxDiff studies without heavy coding. |
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Decision-making studies | 1000minds Focuses on preference-based decision modeling using MaxDiff-style methods. |
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Smaller research projects | OpinionX Includes best/worst survey design tools that allow teams to create and analyze MaxDiff-style studies without coding. |
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Enterprise research teams | Qualtrics Supports MaxDiff survey design using expert-built templates and automated analysis workflows. |
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Complex research projects | Forsta Offers built-in MaxDiff question types to create trade-off exercises directly within surveys. |
MaxDiff Analysis helps teams move beyond rating scales by requiring respondents to make trade-offs between competing options. This leads to clearer prioritization across product features, customer experience improvements, internal initiatives, or marketing strategies.
However, MaxDiff results are only as useful as the attributes included in the study. So, how do you know which features to test in the first place? Analyzing open-ended feedback from reviews, surveys, or support tickets allows your team to:
With Blix, you can surface recurring themes from open-ended responses in minutes. These themes can then guide the attributes included in your MaxDiff study so you can prioritize the features your customers truly care about.
The four main types are:
Most survey analysis focuses on descriptive analysis, with diagnostic analysis used to explain key drivers.
Common survey methods include:
Online surveys are the most popular types used today due to speed, reach, and ease of analysis.
Manual verbatim coding becomes inefficient and inconsistent as response volume grows. Software-based analysis platforms, such as Blix, support scalable qualitative analysis by automatically organizing, categorizing, and summarizing text responses across large datasets.
Save hours of manual work with AI powered open ends coding, with human-level quality and zero manual work.
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