विधियों की तुलना करें

	Choice-Based Conjoint	Conjoint Market Simulator
क्षेत्र	Marketing Research	Marketing Research
परिवार	Regression model	Process / pipeline
उद्भव वर्ष	1983	1999
प्रवर्तक	Jordan J. Louviere & George Woodworth; Sawtooth Software (Bryan Orme)	Sawtooth Software (Bryan Orme, Joel Huber); random utility choice theory
प्रकार	Discrete-choice experiment for product preference and part-worth utilities	Share-of-preference simulation from estimated conjoint utilities
मौलिक स्रोत	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.	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
उपनाम	CBC, Discrete-Choice Conjoint, Choice Experiment Conjoint, Choice-Based Conjoint Analysis	Choice Simulator, Share-of-Preference Simulator, Market Simulation, Randomized First Choice Simulator
संबंधित	4	4
सारांश	Choice-based conjoint analysis (CBC) measures how consumers value the features of a product by observing the choices they make among competing, attribute-defined profiles rather than by asking them to rate attributes directly. Each respondent completes a series of choice tasks, picking the single most preferred alternative (often with a 'none' option) from a small set, and the pattern of choices across many tasks reveals the implicit trade-offs people make. The method grew out of Louviere and Woodworth's 1983 integration of conjoint measurement with discrete-choice theory, which showed that controlled choice experiments could be analyzed with the multinomial logit model. Because the choice task mimics a real purchase decision, CBC has become the dominant form of conjoint in commercial marketing research, popularized by Sawtooth Software. Estimation recovers part-worth utilities for every attribute level, either at the aggregate level or, more commonly today, individually through hierarchical Bayes. Those utilities then feed market simulators that predict shares of preference for new or hypothetical product configurations.	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.