MaxDiff vs Conjoint Analysis: Which Method Gets Better Results? [2025]

Understanding the Basics: What is MaxDiff vs Conjoint Analysis?

The choice between MaxDiff and conjoint analysis in market research depends on their core differences. These methods might look similar, but each serves a unique purpose in learning about consumer priorities.

Definition of MaxDiff Analysis

MaxDiff analysis, also known as Maximum Difference Scaling or Best-Worst Scaling, helps create reliable rankings based on what consumers prefer. Jordan Louviere developed this survey-based statistical technique that asks people to pick their most and least important options from different sets of items.

MaxDiff stands out from standard rating scales where people might label everything "important." The process shows participants 3-5 items on multiple screens until they've reviewed all items equally. To cite an instance, someone rating backpack colors might see different combinations of red, yellow, blue, and green options. They'd pick their favorite and least favorite colors from each set.

This method works best to rank 12 to 50 items. Product features, messaging claims, and brand attributes become easier to measure. The results show not just which items people prefer, but also how much they prefer them.

Definition of Conjoint Analysis

Conjoint analysis takes a more detailed look at how consumers value different parts of a product or service. This method breaks products into basic elements like brand, price, and features. It then shows how changes in these elements affect what people choose.

People taking part in a conjoint study see several product profiles made from different feature combinations. A smartphone study might include screen size, brand, battery life, and price. Each feature could have multiple options (like 5.5", 6.1", and 6.7" screens). Participants choose their preferred option from each set, much like real-life shopping decisions.

This approach shines when products have two or more features with multiple options. It can predict preferences for thousands of product combinations. The analysis creates "utility scores" or "part-worths" that show each feature's value.

Key Differences in Methodology

These methods differ in what they measure. MaxDiff ranks individual items, while conjoint shows how multiple features work together. This affects everything from survey design to how results get used:

Survey Structure:

• MaxDiff shows item subsets for best and worst choices
• Conjoint displays complete product profiles with different feature combinations

Complexity:

• MaxDiff uses simple survey design with straightforward analysis
• Conjoint needs more detailed setup with multiple features and options

Best Used For:

• MaxDiff: Feature priorities, benefit rankings, finding most/least important items
• Conjoint: Product design, pricing strategy, understanding feature trade-offs

Results:

• MaxDiff gives clear item rankings
• Conjoint reveals feature interactions that drive product preference

Your choice between these methods depends on your goals. MaxDiff works best for prioritizing single item lists. Conjoint helps understand how product features combine to influence buying decisions. Conjoint analysis gives better insights for price sensitivity testing than MaxDiff.

Both techniques make people choose between options. This leads to more realistic insights than traditional surveys where everything might be rated as important.

How Each Method Works in Practice

Researchers need to learn about the ground application of MaxDiff and conjoint analysis to select the right method that meets their needs. Let's see how these methodologies work in actual practice.

MaxDiff Survey Design and Flow

MaxDiff studies use a survey design that shows respondents multiple screens with item subsets to assess. Researchers show 3-5 items at once, though 4 items work best with detailed descriptions. A simple calculation determines the number of questions: 3 × (Total Items ÷ Items per Question). So, a study with 20 items showing 4 per screen needs about 15 questions for reliable results.

The design process needs prohibitions that prevent certain items from appearing together in choice sets. To cite an instance, price point testing should not show "$50 per month" and "$75 per month" together because respondents would always pick the lower price. These restrictions help create meaningful comparisons that provide useful insights.

MaxDiff surveys also rotate item combinations on multiple screens. Each item appears about 3 times per respondent. This balanced design creates stable and reliable preference scores.

Conjoint Analysis Setup and Attribute Modeling

Conjoint analysis setup decisions need more complexity. You'll need to pick 3-8 attributes (features) that have 2-7 levels (options) each. The possible combinations multiply fast - three attributes with two levels each creates 2×2×2=8 unique combinations.

Researchers must keep attributes independent during setup. To cite an instance, see how testing "shelf price," "cans per pack," and "price per can" together would cause problems since the first two determine the third. The setup might also need prohibitions for impossible combinations (like a Samsung smartphone with iOS), though you should not use much of either.

The experimental design algorithm makes each attribute level appear equally often. Each level appears with other attribute levels the same number of times, which creates a fair and balanced experiment.

Respondent Experience: MaxDiff vs Conjoint

These methods give respondents substantially different experiences:

MaxDiff surveys show participants a table of items (usually 4). They pick what's "most important" and "least important." The table updates with new item combinations after each choice, and this process continues several times.

Conjoint analysis has respondents choose between complete product profiles that combine multiple attributes. Each profile shows a possible product configuration (like a smartphone with specific brand, price, and features). People select their preferred option from these bundled packages, which mimics real shopping behavior.

Both methods use trade-offs to reveal meaningful preferences through different approaches. MaxDiff makes people choose between individual items, while conjoint needs selection among complete product concepts.

Comparing MaxDiff and Conjoint: Key Differences

MaxDiff and conjoint analysis have key differences that you need to understand before making a choice. These methods make respondents choose between options, but each serves a unique purpose and gives different insights.

Purpose and Use Cases

The main difference lies in what each method measures. MaxDiff works best to rank individual items based on consumer priorities. You can use it for feature prioritization, message testing, and needs-based segmentation. MaxDiff gives you a clear, prioritized list when you want to rank 12-50 items by importance.

Conjoint analysis helps you learn how consumers value multiple product attributes at once. This makes it perfect for product design, pricing strategy, and market competition analysis. Conjoint becomes your best option when your research deals with products that have two or more attributes with multiple levels, and you need to predict preferences across thousands of possible combinations.

Complexity and Setup Requirements

MaxDiff takes a simpler approach. The survey design and data analysis don't need much complexity. Respondents pick their most and least preferred options from small groups of items.

Setting up conjoint analysis needs more work with its multiple attributes and levels. You must plan the experimental designs carefully to balance attribute exposure. The surveys also take more mental effort from respondents who review complete product profiles instead of single items.

Cost and Time Investment

MaxDiff studies cost less and finish faster because of their simple design and analysis. This works great when you need quick insights or have budget constraints.

Conjoint analysis costs more and takes longer because it's more complex. You need greater expertise and computing power for the sophisticated design, analysis, and interpretation. The extra investment pays off with richer insights that help make complex product decisions.

Type of Insights Generated

These methods give very different outputs. MaxDiff creates a simple ranking with clear preference scores that show which features matter most to consumers. This helps you make prioritization decisions but won't tell you how features work together.

Conjoint analysis gives you deeper insights. It reveals which attributes matter most and shows how different levels within attributes affect consumer choices. The best part is that conjoint can run market simulations to predict how products will perform and their market share. This lets you test different product configurations before development.

When to Use Conjoint Analysis vs MaxDiff

The choice between MaxDiff and conjoint analysis comes down to your research goals and limitations. Let's get into the main factors that should shape your decision.

Choosing Based on Research Goals

Your research goals should point you toward the right method. MaxDiff works best when you just need to set priorities from a list—perfect for ranking features, testing messages, or evaluating concepts.

Conjoint analysis makes more sense when:

• You want to learn how different features work together to influence buying decisions
• You aim to fine-tune a complete product rather than rank single elements
• Price decisions play a central role in your research

To cite an instance, MaxDiff would help you figure out which website features to build first. But if you're creating a new smartphone with different prices, screen sizes, and battery life options, conjoint analysis would give you better answers about the best combinations.

Product Complexity and Attribute Levels

Your product's complexity plays a big role in picking the right method. MaxDiff shines with 12-20 features that you can evaluate one by one. Products with layered features, like multiple price points within the pricing category, work better with conjoint analysis.

Conjoint analysis really shows its strength with 3-8 unique features, each having 2-7 different levels. Take smartphone priorities as an example - you might look at screen size (5.5", 6.1", 6.7"), battery life (12, 24, 36 hours), and prices (USD 199, USD 399, USD 599) all at once.

Budget and Time Constraints

Available resources often guide method selection. MaxDiff costs less money and time, which makes it great when you want quick answers without spending too much. So if you're working with tight deadlines or limited funds, MaxDiff might be your best bet.

Conjoint analysis usually takes more resources but gives you deeper insights about how different elements combine to shape consumer choices.

Decision Framework for Method Selection

Ask yourself these key questions to pick the right method:

• Do you want to set priorities from a list (MaxDiff) or test complete product setups (Conjoint)?
• Does your product have multiple features with different levels to test?
• Can you spare the time and money for more detailed analysis?

Ready to use these powerful methods in your next research project? SurveySparrow offers an accessible interface for both MaxDiff and conjoint analysis. You can run sophisticated research whatever your technical background.

Real-World Applications and Case Studies

Looking at actual examples shows how MaxDiff and conjoint analysis create value. These methods have shaped product decisions and brought measurable results in many industries.

Conjoint Analysis in Product Development

The Honda Odyssey minivan story perfectly shows conjoint analysis at work. Honda's first-generation Odyssey struggled badly in the early 1990s. They sold only 25,000 units in 1995, while Chrysler sold hundreds of thousands. The reason was simple - Honda built the minivan with forward-hinged doors. Using Sawtooth Software's conjoint analysis, they found that buyers wanted dual sliding doors more than anything else. This led Honda to redesign the 1999 model with standard dual sliding doors, which boosted sales substantially.

Marriott Corporation's story adds another great example. They used conjoint analysis to create the Courtyard by Marriott concept. Their research team picked seven key hotel design elements that made guests happy. These findings helped them build an efficient yet welcoming experience that worked for both business and leisure travelers.

MaxDiff Analysis in Feature Prioritization

Google shows how well MaxDiff works to rank feature development. Reports from a Sawtooth research conference reveal that Google uses MaxDiff to assess feature requests from customers, executives, and engineers. This stops decisions from being based on executive opinions alone, since product managers and sales teams often rank requests differently.

Nectar Product Management uses MaxDiff to match priorities between departments. They send MaxDiff surveys to executive teams and development departments to spot differences in their priorities. One time, executives ranked a feature second-highest while developers put it near the bottom. These findings help teams have better roadmap discussions where everyone gets heard.

Comparing Outcomes from Both Methods

Each method brings its own benefits. Conjoint analysis excels at mimicking real buying decisions. The Portland Trail Blazers proved this with their fan strategy. Their research showed ticket prices and seat quality mattered most to bring fans back to games.

MaxDiff works better at ranking preferences between different groups or cultures because it removes scale use bias. Riot Games uses this advantage to compare what players want in the USA versus China.

SurveySparrow offers user-friendly tools for both MaxDiff and conjoint analysis. These tools help you conduct sophisticated research, whatever your technical background.

Comparison Table

Conclusion

The choice between MaxDiff and conjoint analysis ended up depending on your research goals and what resources you have. These methodologies serve different purposes but share the same foundations of forcing trade-offs in decision-making.

MaxDiff shines when you just need a simple ranking of features or benefits. To name just one example, our team helped a software company set their development roadmap priorities. MaxDiff showed unexpected user priorities that didn't match what executives thought. Teams with tight deadlines or limited budgets find this approach valuable because it's simple, costs less, and takes less time to implement.

Conjoint analysis gives deeper insights into how product features work together to shape buying decisions. The Honda Odyssey case shows how conjoint analysis can reshape the scene of product development. It reveals critical feature combinations that drive consumer choices. This method works best when designing complex products that have many interconnected features.

These methods differ nowhere near as much in complexity. MaxDiff creates a simpler survey experience for researchers and respondents. It needs less technical expertise and fewer analytical resources. Conjoint analysis needs more sophisticated design and interpretation but gives richer data about feature interactions and market simulations.