Compară metode

	Conjoint Market Simulator	Discrete Choice Experiment
Domeniu	Marketing Research	Marketing Research
Familie	Process / pipeline	Regression model
Anul apariției	1999	1983
Autorul original	Sawtooth Software (Bryan Orme, Joel Huber); random utility choice theory	Jordan J. Louviere & George Woodworth; Daniel McFadden (random utility theory)
Tip	Share-of-preference simulation from estimated conjoint utilities	Stated-preference experiment for estimating preferences and willingness to pay
Sursa seminală	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	Louviere, J. J., & Woodworth, G. (1983). Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data. Journal of Marketing Research, 20(4), 350-367.
Denumiri alternative	Choice Simulator, Share-of-Preference Simulator, Market Simulation, Randomized First Choice Simulator	DCE, Stated Choice Experiment, Stated-Preference Choice Experiment, Choice Experiment
Înrudite	4	4
Rezumat	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.	A discrete choice experiment (DCE) is a stated-preference method in which respondents repeatedly choose their preferred option from sets of alternatives described by systematically varied attributes, allowing the analyst to estimate how each attribute drives choice. Grounded in McFadden's random utility theory and operationalized for designed experiments by Louviere and Woodworth in 1983, the DCE treats each choice as the selection of the alternative with the highest latent utility and recovers the utility coefficients from observed choices. Because attributes are varied independently by experimental design, the method isolates the marginal effect of each attribute, including price, and yields marginal rates of substitution such as willingness to pay. DCEs are analyzed with multinomial (conditional) logit and, increasingly, with mixed and nested logit models that relax restrictive assumptions and capture preference heterogeneity. The approach is essentially the same machinery as choice-based conjoint but is the standard term in transport, health, and environmental economics, where it is used to value non-market goods. Its rigor and flexibility have made it a dominant stated-preference technique across the social sciences.