Comment identifier les critères d'importance d'un consommateur
dans le choix d'un produit ou d’un service ?

A quel prix commercialiser cette nouvelle offre ?

Comment obtenir un classement fiable de mes produits quand ils sont très nombreux ?

Le TRADE-OFF permet de répondre à ces questions !

Le lancement de ce nouveau packaging
permettra-t-il de gagner des parts de marché ?

Quels avantages / services inclure dans mon programme de fidélité
pour améliorer la satisfaction client ?

Comment arbitrer entre des dizaines d’alternatives ?

Le TRADE-OFF permet de répondre à ces questions !

Comment évaluer le compromis entre l'efficacité d'un traitement médical
et les risques associés ?

Que faut-il changer à mon offre si mon concurrent baisse ses prix ?

Mon nouveau service va-t-il fidéliser mes clients
ou plutôt capter ceux de mon concurrent ?


What is MaxDiff? /The basics

Maximum Difference Scaling (MaxDiff) is an approach which allows us to evaluate preferences and to classify a large range of alternative proposals in relation to one another.

Unlike the CBC, this methodology uses only one attribute (the list of alternatives) and the respondent has to choose among a series of subsets.

This measurement is recorded as an individual score directly associated with each proposal and for each respondent. As a result, this score will make it possible to define the classification and the weight of importance given to each alternative.

Advantages of a MaxDiff:

  • It classifies a large number of proposals without evaluating all of them at once: only a few components in the choice tasks are repeated from the initial list
  • It obtains scores rather than a simple classification value, allowing us to also measure the distance between two alternatives
  • It simplifies the respondents answers: they choose between ‘most preferred’ to ‘least preferred’ in each subset

The experimental design

Solirem develops the experimental design according to the number of products to be assessed.

As with any trade off, the design is constructed to be orthogonal, that is to say, all alternatives have the same chance of being seen.

During this stage of the development, the sample design is tested to determine the optimum number of choice tasks and the number of its proposals/propositions.

Gathering/Collecting information

The approach presents a candidate with a repetition of proposals presented in ‘packages’ (3 to 6 in general) within all the proposals being evaluated. The respondent is then asked to choose the “best” and  “worst ”(or only “best ”) in this ‘package’.

The task is repeated on X number of screens or with X number of choice tasks, each time putting forward a distinct set of ‘packages’.

Exemple tâche de choix #1 (classement produits)

Exemple tâche de choix #2 (classement allégations)

Score estimates

Estimating utility values on completion/delivery

Similar to a CBC, the calculation of scores at the time of data processing is carried out by  using the hierarchical Bayesian algorithm, which makes it possible to obtain results (utility values) individual-by-individual

Various parameters are estimated at individual level by means of a repetitive approach which takes into account both the choice of each of the individuals as well as the overall distribution of these choices. Estimates at individual level improve the precision of the measured importance/significance.

Estimating utility values during the process

Advantage: It is possible to expand on a MaxDiff task by dynamically asking specific questions in relation to the observed classification of a given respondent. For example, you can have a list of 15 products evaluated, while asking a series of specific questions about the “preferred” product in the same questionnaire (or create a TOP 3 for example).

Disadvantage: the calculation method is simplified compared to the calculation presented previously (because the design only takes into account individual responses at time T). The precision of the calculated score is lower because there is no “correction” made according to the responses of the entire sample. We do keep the individual calculated scores on completion, because the overall estimate could contradict the individual classification defined throughout the task.

The MaxDiff analysis makes it possible to obtain utility value scores for each individual as well as for every other alternative proposal evaluated.

Complementary statistical analyses

When it is appropriate and according to your needs and objectives it is possible to carry out secondary analyses.


Solirem is able to categorize or split up the scores from MaxDiff’s basic interpretation. This allows us to obtain information on which alternatives work together, and those which do not. This analysis represents a mapping of various consumer preferences of certain ‘packages’.


Totally Unduplicated Reach and Frequency (TURF) analysis can be applied to MaxDiff’s groundwork, transforming the calculated utility values into choice probabilities. This analysis makes it possible to develop different hypotheses for optimum ‘packages’, ie. creating different references in order  to appear simultaneously (in a department, in a catalog, etc.).