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Identify what truly matters to your customers.
MaxDiff (Best-Worst Scaling) is a survey-based research method used to measure the true relative importance of ideas, features, messages, or benefits.
Instead of asking respondents to rate items one by one, MaxDiff repeatedly presents small sets of options and asks people to choose the most and least important.
This forces real trade-offs and produces clear, discrimination-rich results — free from scale bias and over-rating.
MaxDiff is particularly powerful when decisions require prioritization: product features, value propositions, pricing drivers, messaging, benefits, or policy options.
The output is a robust, ratio-scaled ranking that reveals what truly matters — and by how much.
MaxDiffPro is 'AXYZ Analytics' solution for MaxDiff. is a survey-based research method used to measure the true relative importance of ideas, features, messages, or benefits.
Instead of asking respondents to rate items one by one, MaxDiff repeatedly presents small sets of options and asks people to choose the most and least important.
This forces real trade-offs and produces clear, discrimination-rich results — free from scale bias and over-rating.
MaxDiff is particularly powerful when decisions require prioritization: product features, value propositions, pricing drivers, messaging, benefits, or policy options.
The output is a robust, ratio-scaled ranking that reveals what truly matters — and by how much.
MaxDiffPro is 'AXYZ Analytics' solution for MaxDiff.
Compare messages, motivations and claims that drive preference and action.
Case Study - Dogs ▶ VIDEO
Case Study - Drinks ▶ VIDEO
Case Study - Lottery ▶ VIDEO
Case Study - Drinks ▶ VIDEO
Case Study - Lottery
Validate identity, packaging and visual assets with clear preference rankings.
Case Study - Logo ▶ VIDEO
Case Study - Packaging ▶ VIDEO ▶ VIDEO
Case Study - Packaging ▶ VIDEO
Prioritize incentives and offers that maximize conversion and profitability.
Case Study - Pricing ▶ VIDEO ▶ VIDEO
Test policy themes and audience trade-offs to guide strategic communication.
Case Study - Electoral
Rank employee benefits by preference to maximize satisfaction and retention with the best cost-benefit ratio.
Case Study - Employee Benefits
Identify the most appealing menu formulas and optimize your offering to maximize customer satisfaction and revenue.
Case Study - Burger Restaurant
Determine which services to include in your subscription vs. paid add-ons to maximize sign-ups and member satisfaction.
Case Study - Fitness Startup
Identify the most attractive features for a new coffee shop — from specialty coffee and coworking space to mobile ordering and subscriptions.
Case Study - Coffee Shop
Determine which massage types local residents want most before investing in renovations and hiring therapists — minimizing risk for new entrepreneurs.
Case Study - Massage Center
Prioritize the features that matter most for your next product launch — focus R&D on what truly drives purchase intent.
Case Study - Coffee Machine
Identify the initiatives with strongest perceived impact across visitor segments.
Case Study - Hotel Sustainability
Case Study - Ski Resort Sustainability
Case Study - Ski Resort Sustainability
MaxDiff, short for maximum difference, is a methodology designed to evaluate an individual's preferences across a range of options. Developed by Professor Jordan Louviere and originally known as Best–Worst Scaling (BWS), this approach asks respondents to compare items in order to rank them by relative preference. The analysis can then be extended to specific groups, such as youth or women, or to other targeted sub-populations.
Today, the term MaxDiff often encompasses BWS, particularly in marketing. From an academic perspective, however, it should arguably be the other way around.
Logos
Brand names
Slogans
Reasons to believe
Subscription models
Packaging
Favorite product
New features
Traditional MaxDiff (Best–Worst Scaling) offers a clear advantage in collecting precise and reliable data. It improves the survey experience by encouraging respondents to make explicit trade-offs, thereby capturing their true preferences. This method stands in sharp contrast to rating scales, which often produce ambiguous or inflated results.
Learn more about Traditional MaxDiff (Best–Worst Scaling) [LINK to the corresponding blog]
Our MaxDiffPro studies can include a "Dual Response" question to enhance the insights we gather. This option is customizable to fit the unique needs of your study.
After identifying the top choice in a MaxDiffPro task, we ask participants a "Dual Response" question to gauge their true commitment to their preference, determining if they would realistically subscribe to, buy, or interact with it.
We tailor Dual Response to fit your study, from assessing willingness to "join" a party, "subscribe" to a gym, to decisions on "buying" or "renting." The aim is to understand the impact of each option on your specific outcome.
Advantages:
Real behavior validation: Distinguishes relative preference from actual commitment to act. Distinguishes relative preference from actual commitment to act.
Stronger winner detection: Identifies which leading items remain robust when a real decision is required. Identifies which leading items remain robust when a real decision is required.
KPI alignment: Adds a behavioral layer that better connects utilities to market outcomes. Adds a behavioral layer that better connects utilities to market outcomes.
Improved simulations: Enables more realistic forecasts by incorporating likelihood of conversion or uptake. Enables more realistic forecasts by incorporating likelihood of conversion or uptake.
Bias reduction: Reveals overstatement effects and prevents the selection of fragile winners. Reveals overstatement effects and prevents the selection of fragile winners.
Sharper prioritization: Helps focus resources on options that truly drive engagement. Helps focus resources on options that truly drive engagement.
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We use an in-house algorithm to implement a 'balanced design' rather than relying on random displays, ensuring more pairs and triplets are shown for fairer assessments. This approach avoids bias and uneven item distribution, resulting in more accurate, reliable, and actionable survey data.
Advantages:
Improved Data Quality: Ensures data reliability and accuracy through consistent representation. Ensures data reliability and accuracy through consistent representation.
Reduces Systematic Bias: Minimizes bias by evenly distributing items across comparisons. Minimizes bias by evenly distributing items across comparisons.
Enhanced Discriminatory Power: Identifies item preferences more effectively, capturing logical patterns (e.g., if A > B and B > C, then A > C). Identifies item preferences more effectively, capturing logical patterns (e.g., if A > B and B > C, then A > C).
Easier Analytics: Simplifies data analysis and interpretation by structuring comparisons logically. Simplifies data analysis and interpretation by structuring comparisons logically.
Reduced Participant Fatigue: Prevents overexposure to the same items, maintaining engagement. Prevents overexposure to the same items, maintaining engagement.
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