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Conjoint Market Simulator

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.

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Conjoint Market Simulator (Share-of-Preference Simulation)

Rekodi ya mbinu ya kiajenda · process-pipeline / marketing-research

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
Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge: Cambridge University Press. · ISBN 9780521766555
Sawtooth Software. Market Simulator Models (Lighthouse Studio Manual): First Choice, Share of Preference, Randomized First Choice, Purchase Likelihood, and Utility methods. · URL

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Same method familyAdaptive Conjoint Analysismachine-suggested · Relational suggestion, not evidence.Often confused withChoice-Based Conjointmachine-suggested · Relational suggestion, not evidence.Used in the same domainDiscrete Choice Experimentmachine-suggested · Relational suggestion, not evidence.Often confused withMaxDiff / Best-Worst Scalingmachine-suggested · Relational suggestion, not evidence.