Maximum Differrence Scaling

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SurveyAnalytics Max Diff Analysis

MaxDiff analysis can be used in new product features research or or even market segmentation research to get accurate orderings of the most important product features, SurveyAnalytics help's you discriminate among feature strengths better than derived importance methodologies. Like other trade-off analyses, the analysis derives utilities for each of the most important product features which can be used to derive optimal products, using market segmentation to put respondents into groups with similar preference structures, or to prioritize strategic product goals.

You can have your respondents perform Forced-choice nature of the tasks, where in SurveyAnalytics MaxDiff can disentangle the relative feature importance in cases where average Likert-style ratings might all have very similar ratings.

  • Unlimited Attributes
  • Random Sub-Selection and Rotation
  • Utility / Share of Preference Calculation

Maximum Differrence Scaling

Maximum Difference Scaling is an approach for obtaining preference/importance scores for multiple items (brand preferences, brand images, product features, advertising claims, etc.). Although it shares much in common with conjoint analysis, it is easier to use and applicable to a wider variety of research situations. It is also known as "best-worst scaling".

Maximum Difference Scaling is a technique that allows marketers to identify the "best" of many alternatives- preferences in product features or benefits, marketing slogans, logos, service options, or something else entirely like names for a restaurant.

Maximum Difference Scaling sounds like it might be complex, but with SurveyAnalytics, this technique is actually straightforward.


SurveyAnalytics is a web based service for conducting online surveys. The process is simple using SurveyAnalytics's online survey software:
  • Build your survey using the intuitive wizard interface.
  • Send invitations using the integrated email system.
  • View your reports online automatically.
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