The AXYZ Analytics Journey — How It All Began

AXYZ Analytics traces its origins to Valerie Severin and Pierre Uldry, who completed PhDs with Professor Jordan Louviere at the University of Sydney. His work on MaxDiff and Discrete Choice Experiments helped shape their approach, among other influences.

In 2000 the duo founded a consultancy focused on choice modeling and experimental design. In 2007 they were joined by Marc Uldry, adding depth in computational statistics. In 2010 Cindy Ford, joined the team, further strengthening the technical side.

Over time the firm traded under different names and broadened its work across sectors such as healthcare, finance, and technology. The aim has been consistent – build useful models, test them carefully, and help clients make decisions with evidence.

In 2020 the company rebranded as AXYZ Analytics to reflect a wider scope in data science and cross-disciplinary projects.Today AXYZ Analytics focuses on practical, defensible analytics – bridging research and application after 25 years in the field.

Our team is growing to match the work – Iris Levy keeps our message clear, Moshe Vaizman strengthens operations and growth, and Yariv Levy, steers the data and engineering stack.

Meet our leadership

Pierre Uldry, PhD
CEO / Founder

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Valerie Severin, PhD
Co-Founder / VP of Finance

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Marc Uldry, PhD
Co-Founder / VP of Product

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Cindy Ford, PhD
VP of Customer Success

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Iris Levy
VP of Marketing

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Moshe Vaizman
VP of Operations and 

Business Development

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Yariv Levy, PhD
VP of Technology

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Case Title

Background

L’étiquette d’une bouteille de vin est essentielle, capable à elle seule d’augmenter les

ventes ou de soutenir une hausse de prix sans impacter la demande.

Problem

Un graphiste a créé 14 étiquettes potentielles, mais laquelle aura le plus desuccès?

Solution

C’est le consommateur qu’il faut questionner! L’analyse MaxDiff permet de déterminer
quantitativement l’ordre de préférence des étiquettes parmi un échantillon de 200-400
consommateurs en comparant les différentes options. Cette méthode résout

efficacement l’incertitude du vigneron

Results

Example of result, some explaination

Contact us for the full report

Yariv Levy, PhD
VP of Technology

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Moshe Vaizman
VP of Operations and 

Business Development

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Iris Levy
VP of Marketing

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Cindy Ford, PhD
VP of Customer Success

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Marc Uldry, PhD
Co-Founder / VP of Product

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Valerie Severin, PhD
Co-Founder / VP of Finance

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MaxDiff, or Best-Worst Scaling (BWS), is a remarkably insightful and productive measurement method used widely by researchers and practitioners across social science, business, and other disciplines. It is often preferred over traditional rating scales because it is effective at capturing preferences with known measurement properties.

Here is a comprehensive explanation of Best-Worst Scaling (BWS) and its origins as MaxDiff:

  1. Definition and Core Mechanism

Best-Worst Scaling (BWS) is defined as an extension of the method of paired comparison to multiple choices. The fundamental task requires participants to evaluate a collection, or “set,” containing three or more items or options. From this set, the participant must choose:

  • The best (or most attractive/most preferred) option.
  • The worst (or least attractive/least preferred) option.

The terms “best” and “worst” are considered metaphors representing the extremes of an underlying, latent, subjective continuum of interest.

By collecting both extreme choices from a set, BWS obtains extra choice information. Crucially, this method gathers valuable information about both the attractive options (via the “best” choices) and the less attractive options (via the “worst” choices), providing richer insights into the respondent’s utility function.

  1. Nomenclature and History (MaxDiff vs. BWS)

The terms MaxDiff and BWS refer to the same technique, though BWS is now the generally accepted name, particularly within academic circles.

  • Origin as MaxDiff: The approach was pioneered by Jordan J. Louviere, who developed it in 1987. He initially called it “maximum difference scaling” (or “MaxDiff”).
  • Hypothesized Process: This original name reflected Louviere’s hypothesis that the underlying decision process involved choosing the pair of options that exhibited the largest subjective difference on the latent scale of interest. The two chosen options (best and worst) would thus be maximally different.
  • Shift to BWS: The name was subsequently changed to Best-Worst Scaling because subsequent academic research demonstrated that individuals generally do not use a maximum difference choice process to perform the task. BWS is considered a more inclusive general term, as MaxDiff only represents one of several possible psychological processes a person might use to provide best-worst data.
  1. Advantages over Rating Scales

A significant benefit of using BWS is its ability to avoid many problems inherent to traditional rating scales.

  • Forced Discrimination: BWS forces respondents to discriminate between items by selecting only one best and one worst option, thereby reducing response biases (such as acquiescence bias or consistently using only mid-points or end-points of a scale).
  • Measurement Properties: BWS offers a theoretical framework to measure latent, subjective quantities. The derived measurement values possess known properties—specifically, they often produce ratio-level scales (if specific conditions regarding choice probabilities are met).
  • Simplicity and Quality: The task itself is simple and undemanding for respondents, which typically improves data quality.
  1. Theoretical and Analytical Foundation

The conceptual framework underpinning BWS is random utility theory (RUT). RUT assumes that when people choose repeatedly, their observed choice frequencies indicate how much they value the items under consideration.

Analytically, BWS often involves straightforward calculations, making it accessible even for practitioners with minimal prior knowledge. A simple way to summarize the data is by calculating best-minus-worst scores for each option, which is the count of times an item was chosen as “best” minus the count of times it was chosen as “worst”.

These best-minus-worst scores empirically correlate highly with estimates from more sophisticated models, often serving as a sufficient statistic for conditional logit regression models if the MaxDiff model holds.

  1. Categorization of Applications

BWS applications are typically divided into three main classes, differing based on the complexity of the items being evaluated:

Case

Name

Description of Items

Choice Task Focus

Case 1

The Object Case

Simple items, objects, statements, or people.

Measuring a list of items on a single underlying subjective scale (e.g., measuring priority, importance, or agreement).

Case 2

The Profile Case

Attribute levels described/displayed as a single profile (a combination of attribute levels).

Choosing the best and worst attribute levelswithin a single profile.

Case 3

The Multi-Profile Case

Sets of three or more profiles (each profile being a complex combination of attributes and levels, akin to a traditional DCE).

Choosing the best and worst whole profiles (options) among multiple choices.

In summary, BWS is an invaluable choice-based measurement method that leverages the finding that individuals are highly reliable and accurate when identifying extreme options. It moves beyond the MaxDiff hypothesis to provide a robust framework for measuring subjective preferences.

Reference: Louviere, J. J., Flynn, T. N. & Marley, A. A. J. Best–Worst Scaling:Theory, Methods, and Applications (Cambridge Univ. Press, 2015).

 

 

Pierre Uldry, PhD
CEO / Founder

Pierre, originally from Switzerland, began his journey as an economist before transitioning into marketing, statistics, and becoming a self-taught programmer.
He moved to Australia to pursue his PhD at the University of Sydney, studying under the guidance of Professor Jordan Louviere, the mastermind behind BWS, nowadays MaxDiff. In a significant contribution, Pierre introduced the dual response to this technology. With a solid foundation in mathematics, he’s dedicated over two decades to discrete choice modeling, consistently thinking outside the box.
Passionate about movies, running, and his former stint as a Swiss mogul skier, he’s deeply connected to Australia, where he
continues to reside. His expertise in Bayesian Methods has seen him craft processes for diverse industries and tailor models for numerous studies

How does it work?

1. Reach out

You: Fill out our form and tell us what you’d like to test.

We: Review your request and confirm feasibility.

2.⁠ ⁠Free Consultation

You: Join a 30-minute call to discuss your study.

We: Design and launch your study.

3.⁠ ⁠Approval

You: Approve the quote and study plan.

We: We design and launch the study.

4. Results

You: Sit back and relax — your insights are on the way.

We: Collect the data, analyze it, and share your report.