By Joel Cadwell
Choice modeling begins with a researcher “deciding on what attributes or levels fully describe the good or service.” This is consistent with the early neural networks in which features were precoded outside of the learning model. That is, choice modeling can be seen as learning the feature weights that recognize whether the input was of type “buy” or not.
As I have argued in the previous post, the last step in the purchase task may involve attribute tradeoffs among a few differentiating features for the remaining options in the consideration set. The aging shopper removes two boxes of cereal from the well-stocked supermarket shelves and decides whether low-sodium beats low-fat. The choice modeler is satisfied, but the package designer wants to know how these two boxes got noticed and selected for comparison. More importantly for the marketer, how is the purchase being framed by the consumer? Is it advertising that focused attention on nutrition? Was it health claims by other cereal boxes nearby on the same shelf?
With caveats concerning the need to avoid caricature, one can describe this conflict between the choice modeler and the marketer in terms of shallow versus deep learning (see slide #2 from Yann LeCun’s 2013 tutorial with video here). From this perspective, choice modeling is a form of more shallow information integration where the features are structured (varied according to some experimental design) and presented in a simplified format (the R package support.CEs aids in this process and you can find R code for hierarchical Bayes using bayesm in this link).
Choice modeling or information integration is illustrated on the upper left of the above diagram. The capital S’s are the attribute inputs that are translated into utilities so that they can be evaluated on a common value scale. Those utilities are combined or integrated and …read more
Source:: r-bloggers.com