MaxDiff Analysis (Best-Worst Scaling)

Overview & Method Framework

MaxDiff (Maximum Difference Scaling) eliminates standard scale bias and 'straight-lining' by asking respondents to identify the single most important and least important item from a series of randomized subsets. Hierarchical Bayes estimation converts these choices into a zero-to-100 relative preference scale.

Key Capabilities & Benefits

• Free from: Free from rating scale bias (no 'everything is 5/5' syndrome)
• Clear discrimination: Clear discrimination and statistical separation between features
• Generates normalized: Generates normalized zero-centered interval scores (0–100 scale)
• Segmentable across: Segmentable across respondent personas and demographics

When to Use This Method

• Feature prioritization for engineering roadmaps and agile sprints
• Ad claim and packaging copy testing (which claim drives conversion)
• Brand benefit ranking and messaging hierarchy
• Customer service and loyalty program benefit selection