Usporedite metode

	Adaptive Conjoint Analysis	Conjoint Market Simulator
Područje	Marketing Research	Marketing Research
Obitelj	Process / pipeline	Process / pipeline
Godina nastanka	1987	1999
Tvorac	Richard M. Johnson (Sawtooth Software)	Sawtooth Software (Bryan Orme, Joel Huber); random utility choice theory
Vrsta	Computer-adaptive conjoint combining self-explication and paired comparisons	Share-of-preference simulation from estimated conjoint utilities
Temeljni izvor	Green, P. E., Krieger, A. M., & Agarwal, M. K. (1991). Adaptive Conjoint Analysis: Some Caveats and Suggestions. Journal of Marketing Research, 28(2), 215-222.	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
Drugi nazivi	ACA, Adaptive Conjoint, Computer-Adaptive Conjoint, Self-Explicated and Paired-Comparison Conjoint	Choice Simulator, Share-of-Preference Simulator, Market Simulation, Randomized First Choice Simulator
Srodne	4	4
Sažetak	Adaptive Conjoint Analysis (ACA) is a hybrid, computer-administered conjoint method that builds each respondent's part-worth utilities by combining a self-explicated priors stage with a sequence of adaptively chosen paired-comparison trade-offs. Developed by Richard Johnson at Sawtooth Software in the mid-1980s, ACA was designed to handle many more attributes than a respondent could realistically evaluate in full-profile or choice tasks. The interview first asks people to rate the desirability of attribute levels and the importance of attributes, then uses those answers to generate paired product comparisons that are roughly balanced in utility, which are the most informative trade-offs. Respondents indicate graded preference between each pair, and the program updates the utilities in real time, focusing later questions where uncertainty is greatest. Green, Krieger, and Agarwal's 1991 evaluation in the Journal of Marketing Research documented both ACA's strengths and important caveats about its self-explicated component and attribute-importance estimates. ACA produces individual-level utilities that can drive purchase-likelihood calibration and market simulation.	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.