MaxDiff: 3 Popular Applications
By Elizabeth Horn, Ph.D.
MaxDiff: 3 Popular Applications: Transcript
Hello. I’m Beth Horn, Senior Vice President of Advanced Analytics at Decision Analyst. Thank you for joining this video presentation of MaxDiff Analysis: 3 Popular Applications. The applications that I’ll discuss today include: ranking items such as product messages, features, or flavors; optimizing sets or bundles of messages or features; and driving segmentation analysis.
Briefly, the MaxDiff technique involves evaluating many Question Example smaller sets of product features, claims, or others attributes. For each small set, respondents select the most and least important item to their purchase decision. Advantages of this technique are that it works well with a variety of items such as products features, ideas, claims, benefits, and so forth. And, it is a simpler, more focused task for the respondent. Selecting most and least from small sets of items is much easier than rating each item on a five-point importance scale.
The first popular application of MaxDiff is to produce a relative ranking of product claims, positionings, flavors, product varieties, and so on. MaxDiff modeling produces a probability that each item will be selected as most from a set of items. These probabilities are relative measures of the strength of preference or importance of each item. Higher numbers indicate higher preference, and lower numbers indicate lower preference. And, these preferences can be used to make comparisons, both within and between respondent groups.
A second popular application of MaxDiff is to optimize sets of items. The results derived from the MaxDiff can be used to develop optimal combinations using a TURF-like optimization algorithm. The algorithm searches for the combination of items that reaches the most consumers. Companies use results like these to build optimal product lines, or flavor lines, or even optimal bundles of messages to use when marketing a product.
The third popular application for MaxDiff is for segmentation. The MaxDiff survey task forces respondents to make a discriminating choice about which item is most preferred and which item is least preferred. There’s no possibility to encounter scale bias, in which ratings are bunched up at the top end or bottom end of the scale. These discriminating choices help differentiate potential segments. Therefore, using MaxDiff preference scores as one of the inputs into a segmentation model is a great way to promote more distinct groups
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Elizabeth Horn, Ph.D. (firstname.lastname@example.org) is Senior Vice President, Advanced Analytics at Decision Analyst. She may be reached at 1-800-262-5974 or 1-817-640-6166.