Compare methods

	MaxDiff / Best-Worst Scaling	Conjoint Market Simulator
Field	Marketing Research	Marketing Research
Family	Regression model	Process / pipeline
Year of origin	2005	1999
Originator	Jordan J. Louviere; A. A. J. Marley & Jordan Louviere	Sawtooth Software (Bryan Orme, Joel Huber); random utility choice theory
Type	Best-worst choice task for scaling relative importance of items	Share-of-preference simulation from estimated conjoint utilities
Seminal source	Louviere, J. J., Flynn, T. N., & Marley, A. A. J. (2015). Best-Worst Scaling: Theory, Methods and Applications. Cambridge: Cambridge University Press.	Orme, B. K. (2020). Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research (4th ed.). Madison, WI: Research Publishers LLC. ISBN: 9780972729772
Aliases	MaxDiff, Best-Worst Scaling, BWS, Maximum Difference Scaling	Choice Simulator, Share-of-Preference Simulator, Market Simulation, Randomized First Choice Simulator
Related	4	4
Summary	MaxDiff, also known as best-worst scaling (BWS), measures the relative importance or preference of a set of items by repeatedly asking respondents to identify the best (most important or most preferred) and worst (least) item within small subsets. Introduced by Jordan Louviere and formalized by Marley and Louviere's 2005 probabilistic models, the method exploits the fact that people are far better at picking extremes than at rating many items on a scale. Each best-worst judgment reveals the maximum-difference pair in a set, and across many balanced subsets the choices pin down a single interval scale of item utilities. Because every respondent is forced to make trade-offs, MaxDiff sidesteps the scale-use bias and lack of discrimination that plague rating grids, where respondents often call everything important. Item scores can be computed by simple best-minus-worst counts or, more rigorously, by fitting a multinomial logit choice model, with hierarchical Bayes yielding individual-level, probability-scaled importances. The result is a clear, discriminating ranking of items that supports prioritization, segmentation, and feature selection.	A conjoint market simulator turns the part-worth utilities estimated from a conjoint or discrete-choice study into predicted shares of preference for a set of competing products, letting analysts run 'what if' experiments on product design and pricing. Once each respondent's utilities are known, any product configuration can be scored, and a choice rule converts those scores into the probability that each respondent prefers each product; averaging across respondents gives the simulated market share. Practitioners choose among several rules: the first-choice rule assigns each respondent wholly to their highest-utility product, the share-of-preference rule uses the logit equation to spread probability across products, and the randomized first-choice rule, developed by Sawtooth Software, blends the two and adds attribute-level error to produce realistic substitution. Because the simulator runs on individual-level utilities, it reproduces heterogeneity and competitive interaction that aggregate models miss. The simulator is where conjoint delivers managerial value, supporting line optimization, pricing, cannibalization analysis, and competitive response. It is a simulation, however, predicting relative shares rather than absolute sales.