MaxDiff is ideal for obtaining preference/importance scores for multiple items such as brands, product features, ad claims, side effects, etc. MaxDiff is also known as "best-worst scaling" and because of its versatility has become the measurement equivalent of the Swiss army knife for surveys in marketing research and economics.
MaxDiff scores demonstrate greater discrimination among items and between respondents on the items than traditional ratings scales. The MaxDiff question is simple to understand, so respondents from children to adults with a variety of educational and cultural backgrounds can provide reliable data. Since respondents make choices rather than expressing strength of preference using some numeric scale, there is no opportunity for scale use bias. This is an extremely valuable property for cross-cultural research studies.
MaxDiff makes it easy for researchers with only minimal exposure to statistics to conduct sophisticated research for the scaling of multiple items. The resulting item scores are also easy to interpret, as they are placed on a 0 to 100 point common scale and sum to 100.
We start with a list of items (brands, features, claims, etc.). Rather than show the respondent all items at once, we show a subset (typically 4 or 5) of the possible items in each of multiple MaxDiff questions. For each question, we ask respondents to indicate the best and worst items (or most and least important, etc.)
Respondents typically complete eight to fifteen such MaxDiff questions. The combinations of items are designed very carefully with the goal that each item is shown an equal number of times and pairs of items are shown an equal number of times. MaxDiff exercises focus on estimating preference or importance scores for typically about 15 to 40 items—though hundreds of items could be accommodated in advanced applications.
We typically estimate item scores for each individual using a hierarchical Bayes (HB) methodology. The HB tool is built right into the point-and-click interface. The default settings are robust, so users with very little background in statistics can obtain good results. We also offer a built-in Latent Class capability for discovering segments of respondents with similar needs/preferences.
TURF (Total Unduplicated Reach and Frequency) is a portfolio optimization approach that is often applied to MaxDiff data. For example, TURF can help determine the optimal set of 5 flavors to offer to appeal to the largest number of customers. The MaxDiff Analyzer tool makes TURF analysis easy.
Although MaxDiff shares much in common with conjoint analysis, it is easier to use and applicable to a wider variety of research situations. Common use cases include: