MaxDiff analysis helps businesses identify which features, benefits or messages customers prioritise when they must make a choice. Instead of rating every option as important, participants repeatedly select the most and least important items from small groups.
This makes the method useful when a product team has more ideas than it can deliver. A survey showing that every proposed feature is “very important” may leave the roadmap debate unresolved. A carefully designed MaxDiff survey adds evidence about relative priorities.
The result is an input to a business decision. It does not automatically reveal which feature will increase revenue, what customers will pay or what the team should build first.
What Is MaxDiff Analysis?
MaxDiff stands for maximum difference scaling. In the form commonly used for feature research, it is also called best-worst scaling.
Participants see a series of small sets drawn from a larger item list. For each set, they choose the option that matters most and the option that matters least under a clearly defined question.
Researchers analyse these repeated choices to estimate relative preference or importance. The meaning depends on the question asked: “most appealing” measures something different from “most important when choosing.”
Use one consistent criterion throughout the exercise. Combining importance, satisfaction and likelihood of purchase in a single task makes the responses difficult to interpret.
When Is MaxDiff Useful?
MaxDiff analysis is useful when a business has a defined set of comparable options and needs to understand their relative value to a target audience.
Applications include:
- Prioritising candidate product features.
- Comparing customer benefits for positioning.
- Selecting service improvements for further evaluation.
- Identifying which claims deserve more detailed message testing.
- Comparing feature priorities across relevant customer groups.
It is less suitable when the team has not yet understood the problem. Asking people to choose between poorly defined solutions creates precise-looking answers to the wrong question.
Begin with interviews or broader consumer research when important needs and vocabulary remain unclear. Use those findings to build the candidate list.
How Does a MaxDiff Survey Work?
Imagine a business developing an appointment-management tool for small service firms. It wants to compare several possible improvements.
One illustrative task could ask:
“Thinking about managing appointments in your business, which feature would be most important and which would be least important?”
The participant might see:
- Automatic reminders before an appointment.
- A customer self-service rescheduling option.
- A dashboard showing unfilled appointment slots.
- A shared calendar for multiple staff members.
The respondent selects one most important and one least important option. Subsequent tasks present other combinations from the full list.
This is an example task, not a complete research design. A production survey needs a planned arrangement of items and tasks so the comparisons support the intended analysis.
The lowest-ranked feature is not necessarily unwanted. It may be valuable but less important than the alternatives shown.
How to Design Feature Prioritisation Research
1. Define the Decision and Audience
Specify what the research must inform. Choosing benefits for a landing page is different from deciding which capability to develop next.
Define eligible participants using relevant behaviour or responsibilities. For a business scheduling product, someone who manages appointments may provide more useful answers than an employee who never uses the system.
Decide whether the study should represent existing customers, potential buyers or both. Report these groups separately if their needs differ.
2. Build a Comparable Item List
Write each item at a similar level of detail. Avoid comparing a broad promise such as “better productivity” with a narrow capability such as “export appointments to CSV.”
Describe one idea per item. “Automated reminders and payment collection” bundles two features, making it unclear which influenced the selection.
Avoid exaggerated wording. “Instant, effortless reporting” may outperform “calendar export” because of its language rather than its underlying value.
A practical review question is: could a participant understand the feature and distinguish it from every other item without an explanation from the researcher?
For more on neutral wording, see ResearchFox’s guide to market research survey questions.
3. Use an Appropriate Experimental Design
Plan how often each item appears and which items appear together. Simple randomisation alone may not provide a sufficiently balanced or connected design.
The number of tasks, items per task and survey versions should reflect the total list, respondent burden and analysis approach. A longer exercise is not automatically better.
Use a suitable design tool and review its diagnostics. Where the planned analysis is complex, involve the analyst before fieldwork so the questionnaire collects the comparisons the model needs.
4. Plan the Sample Around Reporting Needs
There is no single sample size that suits every MaxDiff survey. Requirements depend on the design, variation in preferences and level of detail needed.
A study reporting overall priorities has different needs from one comparing several industries or customer segments. A large total sample can still leave individual subgroups too small for dependable comparisons.
Agree on the important comparisons before recruitment. Assess expected precision and feasibility rather than adopting a universal minimum without considering the study.
5. Pilot Before Launch
Test whether participants understand the instructions, recognise the differences between items and can complete the tasks comfortably on their devices.
Check whether wording needs clarification or translation. If research spans languages, equivalent meaning matters more than literal translation.
Review survey logic and item exposure before launch. Fieldwork quality checks should identify suspicious or unusable responses without assuming that every unusual preference is invalid.
How to Interpret MaxDiff Results
Understand the Score Type
A basic descriptive measure compares how often an item is selected as best and worst, accounting for how often it was shown. Model-based approaches, such as logit or hierarchical Bayes estimation, produce utility estimates using additional assumptions.
These outputs are related, but they are not interchangeable. A report should explain the estimation method and the scale used.
If scores are normalised to sum to 100, they describe relative preference within the tested list under that scoring approach. A score of 25 does not mean that 25% of the market will buy the feature.
Look Beyond the Overall Ranking
Average scores may hide different needs. A multi-location business could prioritise access controls, while a single-location operator cares more about reducing manual scheduling.
Investigate relevant groups when the sample supports it. Avoid presenting small score differences as meaningful distinctions without considering uncertainty.
The tested item list also matters. Adding, removing or rewording options can change the comparison. Track changes across waves carefully rather than assuming that scores from different lists are directly comparable.
Separate Relative Priority From Absolute Need
A feature can rank last in a strong list and still be necessary. Conversely, the winning feature in a weak list may not make the product attractive enough to buy.
Use additional questions or appropriately designed anchored methods when you need a benchmark for acceptability or importance. Keep these separate from claims about actual adoption.
MaxDiff vs Conjoint: Which Method Should You Choose?
Use MaxDiff when the decision is about prioritising individual items, such as features or benefits.
Use choice-based conjoint when the decision involves choosing between complete offers that vary across several attributes, potentially including price. It can examine how those combinations affect stated choices within the study design.
For example, a team might use MaxDiff to narrow a long feature list. It could then use a separate conjoint study to explore selected features within alternative packages.
Neither method removes the need for clear definitions, suitable respondents or validation. If the question is whether users can complete a task, product and usability testing addresses a different need.
Turn Customer Preferences Into a Roadmap
MaxDiff results should inform prioritisation alongside development effort, dependencies, commercial goals and basic customer requirements.
Consider the hypothetical appointment-management tool. Suppose reminders and self-service rescheduling rank above a reporting dashboard. The team could investigate reminder delivery and rescheduling workflows first.
However, a feature with modest preference scores might still be needed for accessibility, interoperability or the product to function. Research does not make those requirements optional.
A practical decision record should state the target group, the observed priority, the proposed action and the next validation step. Prototype testing, a limited release or follow-up interviews may help resolve questions the survey could not answer.
This example illustrates a decision process; it is not a ResearchFox case study or evidence of a particular feature’s market demand.
Common MaxDiff Mistakes
Using vague items is one of the most avoidable problems. A high score for “better support” does not tell a team whether to offer longer hours, faster replies or more specialised help.
Other mistakes include mixing current satisfaction with future importance, treating least preferred as disliked, and assuming that preference scores measure willingness to pay.
Teams also risk overinterpreting results when they split a sample into many small groups after seeing the findings. Plan important comparisons early and label exploratory patterns clearly.
Frequently Asked Questions
What Does MaxDiff Stand For?
MaxDiff stands for maximum difference scaling. In a typical item-based study, participants choose the best and worst options from repeated subsets, enabling researchers to estimate relative preferences.
Is MaxDiff Better Than a Rating Scale?
It depends on the question. MaxDiff is useful for relative prioritisation. Rating scales remain useful for questions such as satisfaction with a specific experience. Choose the method that fits the decision.
Can MaxDiff Analysis Set Product Prices?
A standard feature-focused MaxDiff study does not estimate a revenue-maximising price. Pricing decisions need a suitable research design, commercial inputs and, where feasible, validation against actual purchasing behaviour.
Does the Lowest Score Mean a Feature Should Be Removed?
No. It means lower relative preference within the tested list and question. Consider whether the feature is a basic requirement, important to a specific group or necessary for the product to work.
Can MaxDiff Be Used for B2B Research?
Yes, when qualified business participants can make meaningful comparisons between the items. Account roles, organisational requirements and recruitment feasibility should shape the study design.
