MaxDiffPro
Market insights like no others, for all your decision-making
MaxDiffPro is AXYZ Analytics’ solution for MaxDiff (also called Best-Worst Scaling) studies.
It provides tools to design the MaxDiff questions, run data quality checks, estimate models, and report results, so each study meets clear quality and reliability standards.
When needed, we source diverse panels and manage fieldwork to deliver clean, decision-ready outputs.
How MaxDiffPro Solves Your Biggest Challenges?
YOUR BRAND
Discover which logo and brand identity resonate most with your ideal customer profile
Identify which brand label best fits your product
BOOST SALES
Fix the optimal price of your product
Reveal which perks your
customers value most
FOSTER
RETENTION
Understand the benefits that are important to your employees
Pinpoint which features of your product are the most important to your clients
SPARK
ENGAGEMENT
Identify the most effective message to advertise your product
Select the most effective slogan for your political campain
FUEL
INNOVATION
Discover what eco-friendly elements resonate with your customers to better align your offer
HOTEL USE CASE ???
AND MORE…
Not sure if your use case can benefit from MaxDiffPro? You might be surprised by how many scenarios this top-notch solution can handle. Don’t hesitate —
just contact us.
Traditional MaxDiff (Best-Worst Scaling) in a Glance
MaxDiff, short for “maximum difference,” is a methodology aimed at evaluating an individual’s preferences across a range of options. Conceived by Professor Jordan Louviere, and originally labelled Best-Worst Scaling (BWS), this approach involves comparing various options to rank them by popularity. This analysis can then be extended to specific groups, such as ‘youth’ or ‘women’, or other targeted sub-populations.
Nowadays the term ‘MaxDiff’ englobes ‘BWS’, especially in marketing, but from an academic point of view, it should be the reverse!
- Logos
- Brand names
- Slogans
- Reasons to believe
- Subscription models
- Packaging
- Favorite product
- New features
- And more...
Traditional MaxDiff (Best-Worst Scaling) offers a distinct advantage in gathering precise and reliable data. It enhances the survey experience by prompting respondents to make specific choices, accurately reflecting their real preferences. This method stands in stark contrast to rating scales, which can often yield ambiguous results.
Input
Our Innovations
The Dual Response
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:
ADD: add.
ADD: add.
Balanced Design
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.
- Reduces Systematic Bias: Minimises 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).
- Easier Analytics: Simplifies data analysis and interpretation by structuring comparisons logically.
- Reduced Participant Fatigue: Prevents overexposure to the same items, maintaining engagement
Our Solution — The Optimal Methodology
| MaxDiffPro by Axyz | Traditional MaxDiff (Best–Worst Scaling) | Traditional Survey (Scaling) |
|
|---|---|---|---|
| Consumer Preferences |  | | |
| Enhanced Discrimination | | |  |
| Clear Preference Hierarchy | | | |
| AI Quality Control | | | |
| Balanced Design | | | |
| Flexible Dual Response | | | |
| Modeling Choice Data | | | |
| Preference Structure | | | |
| Multi Attribute MaxDiff | | | |
| Confidence Intervals | | | |
