Solving Other People's Problems

There is an interesting literature on the ways managers select which projects to pursue. Not to give the game away, but it doesn't have much to do with selecting NPV-positive projects. Sorry if that causes disillusion for anybody.

The decision is made like any other -- utility maximization by the decision-maker. And the question for anybody who wants a decision-maker to implement a particular program.

This is on my mind because of a couple recent events. Most recently, I engaged in a small consulting project for an event marketing firm that was having a little problem with getting the subsidiary of one of their clients to sign off on a marketing plan that the parent company had already endorsed.

In this case, the problem was pretty straightforward: the reason the subsidiary managers have their jobs is because they have successfully made the argument that the subsidiary objectives and strategies are sufficiently different from those of the parent firm that the parent firm would have no hope of effectively running the subsidiary without the subsidiary managers. You can't say all that and then turn around and adopt an event marketing plan that includes some small tweaks to include the subsidiary's brand.

Part of the value of the marketing proposal is its distinctness from the parent's proposal. That it is distinct is probably the most important thing about any project -- from the perspective of the subsidiary.

The other thing was a recent proposal from some consultants to the firm where I used to work. It's not a secret that firms in that industry have a significant gap in understanding pricing -- it is the legacy of being a regulated service provider for so long. Anyway, the consultants from one of the big firms come in and basically make three points.

1. You guys don't have any idea how to price your product
2. If you priced properly, you could increase profit by a very significant amount
3. You should pay us a very tiny fraction of that amount to tell you how to solve point 1

Do the NPV on that. Heck, do the NPV after assuming that the consultants were mentally unstable circus barkers who were overstating the benefits by a factor of twenty or thirty and you are still so positive that you have to do it. Not in the budget? Dude, this is a goose that lays golden eggs; take up a collection.

In this case the problem is also pretty simple. Marketing -- the division that asked for the consultants to present all this -- was measured on volume. For them, the profit targets were merely a constraint, not an objective. What they are supposed to do -- in this particular compensation scheme -- is to maximize volume subject to a profit constraint. In an industry where everybody knows the price elasticity is less than unity, the marketing function is really not interested in learning how expensive (in terms of foregone profit) it is for the firm to put up with their volume goals.

They don't want to hear the consultants setting a pricing policy to increase profit. What they want is an argument that lets them off the hook for low volume numbers -- now and in the future. What they want is the ability to support the claim that some numbers are low because of the profit constraint. And that has a whole different NPV.

And so the problem with solving other people's problems is simply that they are so unlikely to come out and tell you what the problem really is. Whether working from outside or inside the firm, figuring out what goes in the utility function and why is just about the most important question you have to answer.

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.