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What is the Difference Between MaxDiff and Conjoint Analysis?

What is the difference between MaxDiff and conjoint analysis?

MaxDiff, also known as best-worst scaling, is an approach for obtaining preference/importance scores for multiple items. Respondents are typically shown 2-6 items at a time (ex. ice cream flavors - chocolate, strawberry, blue moon, superman, cookie dough) and asked to indicate which is best and which is worst. The task is repeated many times, showing a different set of items in each task. Common applications include message testing, brand preference, customer satisfaction, or product features. The result is an ordered list with interval scaled utility scores for each item.  These scores tell us how much unique value each item garners and can be transformed into ratio-scaled probabilities so that a utility score of 4 for an item is 2x as great as a utility score of 2.

MaxDiff is roughly comparable to a one-attribute, multi-level Choice-Based Conjoint (CBC) exercise. So once you learn MaxDiff, you are well on your way to learning Conjoint Analysis!

In conjoint analysis, we add more dimensions to the experiment, describing the product/ service with multiple attributes. So in our ice cream example, we would now have brand and price, in addition to flavor, to test. We vary the product features (independent variables) to build many product concepts. Then, we ask respondents to rate, rank or, most commonly, choose (depending on the type of conjoint analysis) which concept they prefer (dependent variable). Based on the respondents’ evaluations of the concepts, we can figure out how much unique value (utility) each feature adds to the product.

Common applications of conjoint analysis include designing new products, product line extensions, estimating brand equity, measuring price sensitivity (elasticity), and branding and packaging.

The biggest difference between conjoint analysis and MaxDiff is that in conjoint analysis, the rating or choice of a concept is based on the SUM TOTAL of its components (the items conjoined). It assumes an additive model, where the value of the overall product concept is equal to the sum of its parts. MaxDiff is not an additive model.

In terms of similarity, both techniques force tradeoffs, which lead to greater discrimination among items.  In addition, both techniques result in interval scaled utility scores for each item/ level tested, which can be transformed into ratio-scaled probabilities.

  MaxDiff Analysis Conjoint Analysis
Preview
What does it do?
  • Measure buyers preference toward a list of items/ features/ messages, etc. 
  • Measures how buyers value components of a product/ service bundle
  • Optimizes the product/ service offering
How does it work?
  • We create a list of items to be tested
  • We show respondents 2-6 items at a time and ask them to indicate which is best and which is worst
  • We repeat this task many times, showing a different set of items in each task
  • Based on the respondents' evaluations of the items, we figure out how much unique value (utility) each feature garners 
  • We break a product/ service down into its component parts (attributes) and the different offerings within each part (levels)
  • We then create different product concepts by varying those features (levels)
  • We show respondents 3-5 product concepts at a time and ask them to rate, rank or, most commonly, choose the concept they prefer. We repeat this task many times, showing a different set of product concepts in each task
  • Based on the respondents' evaluations of the concepts, we figure out how much unique value (utility) each feature adds 
Similarities
  • Both techniques force trade-offs, which lead to greater discrimination among items
  • Both techniques result in interval scaled utility scores for each item/ level tested, which can be transformed into ratio-scaled probabilities  
Differences
  • Conjoint analysis assumes an additive model, where the value of the overall product concept is equal to the sum of its parts. MaxDiff is NOT an additive model.
  • All items are measured on a common scale in MaxDiff, and can be directly compared. With conjoint analysis, one can only directly compare the utilities within each attribute. Direct comparisons of a level from one attribute to another from a separate attribute are not proper.  

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