Separating Statistical Models of "What Is Learned" from "How It Is Learned"

Something triggers our interest. Possibly it's an ad, a review or just word of mouth. We want to know more about the movie, the device, the software, or the service. Because we come with different preferences and needs, our searches vary in intensity. For some it is one and done, but others expend some effort and seek out many sources. My last post on the consumer decision journey laid out the argument for using nonnegative matrix factorization and the R package nmf to identify the different pathways taken in the search for product information.

Information Search ≠ Knowledge Acquired

It is easy to confuse the learning process with what is learned. The internet gives consumers control over their information search, and they are free to improvise as they wish. However, what is learned remains determined by the competition in the product category. What knowledge do we acquire as we search online or in person? Careful, there is no exam, therefore we are not required to be objective or thorough. Andy Clark reminds us that "...minds evolved to make things happen." So we learn what is available and what we want because we intend to make a purchase. We learn only what we need to know to make a choice.

The marketer and the consumer join forces to simplify the purchase process so that only a limited amount of information search and knowledge acquisition is needed to reach a satisfying decision. When the choice is hard, only a few buy. The simplification is a one-dimensional array of features and benefits running from the basic to the premium product, from the least to the most expensive. Every product category offers alternatives that are good, better, and best. Learning this is not difficult since everyone is ready and willing to help. The marketing department, the experts who review and recommend, and even other users will let you know what features differentiate the good from the better and the better from the best. One cannot search long for product information without learning what features are basic, what features are added to create the next quality level, and finally what features indicate a premium product.

In the end, we require one statistical model for analyzing how well the brand is doing and a different statistical model for investigating the pathways taken in the consumer decision journey. As we have already seen, R provides a method for uncovering the learning pathways with matrix factorization packages such as nmf. Brand performance or achievement (what is learned) can be modeled using latent-trait or item response theory (see the section "Thinking Like an Item Response Theorist"). I have provided more detail in previous posts showing how to analyze both checklists and rating scales.

Marketers have resource allocation decisions to make. They need to put their money where consumers are looking. When marketing is successful, the brand will do well. Since process and product are distinct systems governed by different rules, each demands its own statistical model and associated measurement procedures.