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The Market Simulator is usually considered the most important tool resulting from a conjoint project. The simulator is used to convert raw conjoint (part-worth utility) data into something much more managerially useful: simulated market choices. Products can be introduced within a simulated market scenario and the simulator reports the percent of respondents projected to choose each. A market simulator lets an analyst or manager conduct what-if games to investigate issues such as new product design, product positioning, and pricing strategy. Market simulators may also be used to search for products to maximize share, revenue, profit, or utility.

A Warning about Interpreting the Output of Market Simulators

Under very controlled conditions (such as markets with equal information and distribution), market simulators often report results that closely match long-range equilibrium market shares. However, conjoint part-worth utilities cannot account for many real-world factors that shape market shares, such as length of time on the market, distribution, out-of-stock conditions, advertising, effectiveness of sales force, and awareness. Conjoint analysis predictions also assume that all relevant attributes that influence share have been measured. Therefore, the share of preference predictions usually should not be interpreted as market shares, but as relative indications of preference.

Forecasting Actual Market Behavior

Researchers who apply firm understanding of conjoint market simulator methodology, extensive experience with the product category, and in many cases external data (not available within the conjoint study) will find that they can supplement and enhance conjoint market simulators for effective forecasting.  

We've introduced new capabilities within this market simulator software that can lead to even better predictions of market behavior than with our older conjoint simulators.  These capabilities include: multi-store shopping adjustment for product availability, an improved method for implementing external effects, and ability to account for individual-level respondent awareness.

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