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MaxDiff is a statistical method invented by Jordan Louviere in 1987 while on the faculty at the MaxDiff represents an extension of Thurstone's Law of Comparitive Judgement. Accoding to Louviere, MaxDiff assumes that respondents evaluate all possible pairs of items within the displayed set and choose the pair that reflects the maximum difference in preference or importance. MaxDiff may be thought of as a more sophisticated extension of the method of Paired Comparisons. Consider a set in which a respondent evaluates four items: A, B, C and D. If the respondent says that A is best and D is worst, these two responses inform us on five of six possible implied paired comparisons: A>B, A>C, A>D, B>D, C>D The only paired comparison that cannot be inferred is B vs. C. In a choice among five items, MaxDiff questioning informs on seven of ten implied paired comparisons. MaxDiff questionnaires are relatively easy for most respondents to understand. Furthermore, humans are much better at judging items at extremes than in discriminating among items of middling importance or preference. And since the responses involve choices of items rather than expressing strength of preference, there is no opportunity for scale use bias. ProcessThe basic steps are: * select products to be tested * show product products to potential consumers (textually or visually) * respondents choose the best and worst from each task * input the data from a representative sample of potential customers into a statistical software program and choose the MaxDiff analysis procedure. The software will produce utility functions for each of the features. In addition to utility scores, you can also request raw counts which will simply sum the total number of times a product was selected as best and worst. AnalysisAny number of algorithms may be used to estimate utility functions. One of the most popular methods is the Hierarchical Bayesian procedures that operate on choice data. These utility functions indicate the perceived value of the product on an individual level and how sensitive consumer perceptions and preferences are to changes in product features. Hierarchal Bayes is beneficial because it allows for borrowing across the data. Copyright 2008 - France BtoB from Wikipédia
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