Conjoint analysis and discrete choice experiments measure respondent preferences (utility) for products or services. In these experiments, the researcher asks respondents to evaluate a number of carefully constructed product concepts, in which each independent component (or "attribute") of a product concept is systematically varied. Respondents provide feedback regarding their preference for each product concept, generally in the form of a stated choice, rating, ranking, probability, or allocation of choices. After the data are collected, a market simulator is created that works like a voting machine, to predict which products the market would most likely select if they were available in the real world. Even though a respondent may evaluate just a small fraction of the possible product concepts within the questionnaire, the market simulator can predict the respondent's preferences for any possible combination of attribute levels.
Examining average utility scores can help us get a general idea of respondent preferences. But to really understand respondent preferences, we need to dig deeper. The market simulator is often the most important component of a well-designed conjoint analysis or discrete choice project. Simulators transform raw conjoint utility scores into simulated market choices. Simulators allow researchers and managers to analyze potential demand in a competitive market context, and see how various changes to competing product profiles might impact demand. Researchers can conduct "what-if" games to investigate issues such as new product design, product positioning, pricing strategy, cannibalization, and product portfolio optimization. Simulators typically also report average utility scores and importance scores, and allow you to segment your analysis.
Sawtooth Software offers two excellent tools for conducting market simulations based on conjoint or discrete choice data models, and a third tool for use with MaxDiff datasets.
|| Sawtooth Software's Online Simulator is designed to provide an easy-to-use, graphically rich simulation and analysis environment. As a web-based tool, the simulator is always up-to-date, and is available anywhere you can find a web connection. With nothing to install, and with a well-designed, intuitive interface, the Online Simulator means you'll spend far less time dealing with technical issues and explaining the simulator to your client, and they'll have far more time to run simulations and glean valuable insights from the data.
When you sign up for the Online Simulator, you'll automatically receive a FREE 30-DAY TRIAL* period. Click here to create an account.
Desktop Simulator (SMRT)
|| The simulator integrated into our SMRT software platform has been the standard conjoint simulator in the market research industry for many years. Its robust design, advanced analysis features, and optional Advanced Simulation Module provide a wide range of tools for experienced analysts and advanced clients.
|| The MaxDiff Analyzer enhances your analysis options for MaxDiff data sets. The tool allows you to rescale scores for a subset of items, run TURF analysis, and simulate preference share allocation across items. This is an online tool, and requires an annual subscription.
Which Conjoint Simulator Should I Use?
Having trouble deciding which one will work best for your project? Click here for a comparison chart.
More Information About Simulators
- Introduction to Market Simulators for Conjoint Analysis (2009)
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.
This paper covers the topic from an intuitive and strategic standpoint. It explains why interpreting average
part worths or importances falls short, and the additional benefits of conducting appropriate simulations.
Three common strategic questions that simulators can respond to are listed, and examples are provided
using hypothetical data. The examples include new product introduction, repositioning existing products,
price sensitivity measurement, and line extensions.
- Dealing with Product Similarity in Conjoint Simulations (1999)
Conjoint simulators have been very useful for transforming part-worth
utility values into the more concrete and managerially appealing
shares of preference. Such simulators let the analyst play "What-If"
games with real market scenarios, such as estimating the impact of
pricing changes, product design modifications, or the effect of a
line extension. However, traditional conjoint simulators based on the
BTL or logit model have suffered from IIA problems. A common example
is that of the red bus company that repaints half of its fleet blue and
nearly doubles its predicted market share. Similar or identical products
placed in IIA simulators tend to result in "share inflation." The first
choice model, while not susceptible to IIA difficulties and unrealistic
share inflation for similar offerings, typically produces
shares of preference that are too extreme relative to real world behavior.
Also, first choice models are inappropriate for use with logit or latent
In the family of Sawtooth Software products, a Model 3 "Correction for
Product Similarity" has been offered to deal with problems stemming from
product similarity. However, this model is often too
simplistic to accurately reflect real world behavior. The authors propose
a new method called "Randomized First Choice (RFC)" for tuning market
simulators to real world behavior. RFC adds random variation to both
attribute part-worths and to the product utility, and simulates respondent
choices under the first choice rule. RFC can be tuned to reflect any
similar product substitution behavior between the extreme first choice
rule and the IIA-grounded logit rule. RFC is shown to improve predictions
of holdout choice tasks (reflecting severe differences in product similarity)
for logit, latent class, ICE and hierarchical Bayes. The greatest gains
were for the aggregate methods. The disaggregate methods, while less in
need of corrections for product similarity, still benefit from RFC.
- External Effect Adjustments in Conjoint Analysis (2006)
Market simulations from conjoint data often do not closely predict actual market shares. That is to be expected, as the model doesn't incorporate many real-world factors that critically affect market shares (such as distribution, awareness, time on the market, etc.). The authors argue that the best approach is to understand (and explain to others) the assumptions within the conjoint model, and to use the market simulator as-is-- focusing on its strengths, rather than making it do something it often cannot (predict market shares). Researchers over the years have (for better or worse) adjusted shares of preference to match known targets or market shares. The Sawtooth Software simulator offers an "external effect" correction to do this. However, it remains a "dangerous" practice, and the documentation warns against its use. The authors investigate how different methods for adjusting shares affect the fundamental properties of the market simulator, in terms of substitution effects, elasticities, and cross-elasticities. They find that the method used in the Sawtooth Software simulation tool has some undesirable properties. A method for adjusting part-worths at the individual level is also tested, and shown to perform better. Perhaps the most valuable section of this paper (and a very defensible adjustment) is the section dealing with corrections for distribution.
- Comparing Hierarchical Bayes Draws and Randomized First Choice for Conjoint Simulations (2000)
Randomized First Choice is a new market simulation technique that shows promise
for reducing IIA problems, especially when using aggregate utilities. It
combines the strengths of the first choice rule and the share of preference
Conjoint simulators have traditionally used part worths as point estimates of
preference. Most recently, Hierarchical Bayes (HB) draws and Randomized
First Choice (RFC) reflect uncertainty (error distributions) about part
worths. RFC makes simplifying assumptions. HB draws, though theoretically
more complete, have some unexpected properties. The authors (Orme and Baker)
find that RFC with point estimates performs slightly better than using HB draws
during simulations. Using RFC on point estimates avoids having to use the
enormous HB draws files. The authors present two reasons why HB draws did
not perform as well: a reverse number of levels effect, and an
excluded levels effect. This paper was delivered at the 2000
Sawtooth Software Conference.
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