We compare two common ways for modeling differential sourcing (substitution effects) among 38 SKUs in a brand/price CBC experiment: simulating on the HB respondent-level point estimates and simulating on the HB respondent-level draws. We find that both approaches lead to very similar sourcing patterns, with simulation on the draws maintaining a slight edge in terms of demonstrating stronger differential substitution patterns. The two approaches are so similar that we don’t hesitate to recommend the common (and simpler) practice of simulating on the point estimates. To confirm our findings, we repeated the analysis with a more sparse second CBC dataset with 51 SKUs.

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.

In conjoint/choice analysis, we often build choice simulators to investigate product or product line solutions that maximize some criterion such as share, revenue, or profit. However, solutions that maximize relative share often yield low profit. Conversely, profit-maximizing solutions can give up a great deal in terms of relative share. Multi-objective searches find solutions that optimize the trade-off among multiple objectives like share and profit, allowing organizations to focus on solutions that can satisfy multiple goals. As an example, solutions can be identified that nearly optimize profit while also nearly optimizing relative share of preference.

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.