Maximum Difference Scaling (MaxDiff) Analysis
What do consumers want is often analyzed by assessing the importance consumers place on various attributes. Most often marketers/researchers use rating scale to address this research objective. However, many a times simply rating attributes or features on a scale of 1-5, 1-7 etc does not provide lucid differentiation of preference among various attributes/features.
AbsolutData uses an advanced research technique to pierce this fog, which is called as “Maximum Difference Scaling” or “MaxDiff”. MaxDiff is an advanced research technique which helps to determine attribute importance over the more simplistic self-explicated rating or ranking procedures. MaxDiff provides better differentiation between attributes & offers better reliability than ratings or paired comparison.
Unlike other techniques, where numerous attributes/features makes analysis results more complex, MaxDiff provides easy to comprehend results, allowing for up to 30 attributes/features within a full MaxDiff exercise
AbsolutData Approach to MaxDiff
AbsolutData has extensive expertise in developing an optimal MaxDiff exercise design, yielding insightful and unbiased results, while minimizing the risk of respondent fatigue. We design MaxDiff in such a way that questions are simple to understand thus ensuring reliable data.
AbsolutData utilizes MaxDiff to discriminate various subsets of attributes/features by utilizing following unique approach:
• AbsolutData can design MaxDiff in such a way that it allows user to make trades off between a large number of attributes in an efficient manner.
• Using MaxDiff, we can showcase attributes/benefits in random groupings & respondents choose the item that is most or least important.
• Data collected utilizes Conjoint based modeling to assign utility values or relative importance to each attribute.
• The result will help client identify priority attributes for a particular segment of customers.
Responses to MaxDiff questions are analyzed using Hierarchical Bayes (HB) techniques to calculate importance scores at the individual respondent level.
