TURF, an acronym for Total Unduplicated Reach and Frequency, is a mathematical procedure for optimizing sets of choices. For example, TURF can help determine the optimal set of 5 flavors to offer to appeal to the largest number of customers. A notable research instrument that delivers multiple menu optimization benefits is a TURF analysis. TURF (Total Unduplicated Reach and Frequency) is a portfolio optimization approach that is often applied to MaxDiff data. We also offer a built-in Latent Class capability for discovering segments of respondents with similar needs/preferences. The default settings are robust, so users with very little background in statistics can obtain good results. The HB tool is built right into the point-and-click interface. We typically estimate item scores for each individual using a hierarchical Bayes (HB) methodology. 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. 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. Respondents typically complete eight to fifteen such MaxDiff questions. For each question, we ask respondents to indicate the best and worst items (or most and least important, etc.) List items are shown to respondents in a MaxDiff 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. We start with a list of items (brands, features, claims, etc.). 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. MaxDiff makes it easy for researchers with only minimal exposure to statistics to conduct sophisticated research for the scaling of multiple items.
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