Principal Component Analysis

One problem for analytical marketers is that lots of questions don't have particularly strong theoretical roots.

Take a question like the speed with which an innovation is adopted by the market. There is an awful lot of information about product diffusion and, to be honest, there is nothing easier than finding some parameters to drop in to a Bass model. The problem is with forecasting the diffusion of new products.

Basically, if you plan to use a Bass model, you are going to have to select some parameters. And it isn't clear what criteria matter for that problem. Does the similarity of the product matter more than the similarity of markets? And what do we mean by similarity? Is a product aimed at the same demographic in a different country a similar market?

Right? There are going to be lots of questions like this, requiring some sort of good judgment to be used. And that's a pretty big problem, since the whole point of analytical marketing is to make these judgments rigorous.

One possible improvement is a principal component anlysis. Basically, the goal is to reduce the number of important factors that determine an outcome to the fewest possible -- subsuming the secondary factors into the smaller set of important ones.

Marketing Science had an interesting paper back in 2009 by Sood, James, and Tellis doing precisely this. Their complaint was that almost all the literature on product diffusion was based on some adaption of the Bass model. So, by using a principal factor analysis, along with additional clustering and so on, they were able to firm up which elements matter most.

Of course, factor analysis / PCA is an a-theoretical process. And it is good to remember that sometimes a-theoretical analysis can serve to introduce rigor to the process.