Market simulations

There are different add-ons to flavor your market simulations:

1) SMRT
2) Advanced Simulation Module (ASM)
In typical market simulations, marketers specify a given test product and a competitive context and ask "How good would this product be?" With optimization problems, what they really want to know is "What product would be best?" The Advanced Simulation Module (ASM) extends the capabilities of our popular conjoint market simulator within the SMRT Platform to offer such product optimization.

Conjoint simulators provide an exceptional tool for product optimization. They can take into account the characteristics of currently-available products as well as the desires of a heterogeneous population of potential buyers. Subject to reasonable caveats about the quality of respondent sampling and questionnaire design, conjoint simulations can accurately assess likely product success long before a product is ready for test market. The ASM can optimize based on maximum utility, purchase likelihood, market share, revenue or profitability. Profitability optimization requires additional user-provided information about feature costs. Also, if you include feature costs, the ASM can perform cost minimization searches, searching for products that meet some threshold of utility, share, revenue, or profit while minimizing cost.

3) Latent Class (LClass)
Choice-based conjoint data have traditionally been analyzed by estimating average part worths for groups of respondents. However, if there are distinct segments, a model that recognizes them can produce more accurate results than an aggregate (single group) solution. Although one can conduct separate analyses for subgroups differing by demography or product-usage, it has been difficult to do segmentation based on choices themselves.
The CBC Latent Class Segmentation Module is an add-on analytical tool that is integrated within the SMRT interface. Data are automatically available in SMRT when using CBC for Windows, and may be easily imported from CBC/Web. It uses choice data for the simultaneous development of segments and estimation of part worth utilities. For example, one segment might be composed of price-sensitive shoppers, and another might be composed of those who usually select premium brands. Each respondent has a probability of belonging to each segment, but can be classified into the most likely segment for subsequent tabulation.

This module has other features that may be of interest to CBC users: it permits weighting of respondents, and you can specify attributes for which utilities should be monotonic, such as for levels of price or quality. Choosing the number of segments is facilitated by specifying a range to investigate, such as from 2 through 30 segments. Statistics are provided for assessing goodness of fit for each solution, and resulting group membership for different solutions is tabulated with one another.

If the market is truly segmented, conducting Latent Class analysis prior to using hierarchical Bayes estimation might be beneficial. CBC/HB can be run separately within groups of relatively homogeneous respondents identified by Latent Class to estimate individual-level part worths. This approach has theoretical merit, but evidence from practitioners suggests that segmenting prior to running HB has not improved overall model fit to holdout criteria.