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 class models.
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.