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Choice modeling

Understanding how and why people make choices among competing products and services is critical to the design and positioning of all products and services, whether they be new televisions, highways, hotel loyalty programs, or transit services. Partnering with leading academic researchers, RSG has been a pioneer in the application of new and powerful methods for modeling consumer behavior. For more than 25 years, we have been developing, refining, and applying discrete choice modeling methods to create robust forecasting models of people’s choices among alternative products and services. These models provide rich descriptions of how products’ features, designs, and attributes, such as price and positioning, affect choices, and are derived from specially constructed surveys that include choice-based conjoint or stated-choice exercises, and/or data on actual choices observed in the marketplace.

RSG’s choice exercises* and choice modeling methods** go well beyond what can be done with off-the-shelf software used by many companies. Our methods are fully customizable, and take into account aspects such as a respondents’ past choices, different underlying distributions and model structures, attribute interactions, and efficient experimental designs. We have also developed and applied complementary methods to model the myriad other considerations that are involved in fully understanding consumers, including: how people decide what products and services to consider; what influences how much / how often they will use the product or service; how the set of alternatives influence choices; and what drives satisfaction, loyalty, and re-purchase/re-use.

* Stated Choice Elicitation Methods
Discrete choice/choice-based conjoint
Adaptive choice-based conjoint
Adaptive conjoint analysis
Best-worst/maximum-difference (MaxDiff) scaling

** Selected Choice Modeling Approaches
Mixed logit models
Integrated choice and latent variable (ICLV) models
Advanced market simulation modeling
Joint revealed preference/stated preference modeling
Generalized extreme value/nested logit/cross-nested logit models
Latent class modeling
Multiple discrete and continuous extreme value (MDCEV) models
Choice-based TURF (Total Unduplicated Reach & Frequency)

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