Monday, November 16, 2009

Predictive Analytics...Maybe the Models are More Accurate than I Imagined

I attended an Emerging Markets conference last week alongside a group of entrepreneurs, libertarians, and disheartened taxpayers from the first world, looking for ways to escape the long grasping arm of the tax man, the encroachment of inflation and the total destruction of their wealth.

Bill Bonner, in his opening remarks, complained to us that economics is a dismal science. He shared a joke about 4 economists who get lost in the woods, and sit down to calculate their whereabouts.

The punchline goes...."you see the second mountaintop north of us across the valley? Well, we're there."

Reminded me of a lot of marketing budgets I've been called in to assess over the years. The inputs looks sort of right...the outputs are believable as long as you don't look at them too hard, or compare them to the reality.

It also reminded me of another joke about an economist and an engineer stuck on a desert island with a crate of canned beans.

After the engineer loses an eye and the contents of a precious can of beans in a failed thermodynamics experiment over a roaring campfire, the economist leaps to his feet. He proclaims he has the problem licked...

"First off, assume a can opener..."

I set about reading on the subject of asset allocation for the purposes of risk management over the last few weeks for work, and because predictive models interst me.

The prevailing theory speaks of an optimal risk curve, a theoretical line along which the maximum return is achieved for each level of risk. This formula supposedly automates the process of selecting the best assortment of investments for each individual according to their risk tolerance.

The problem with this, of course, is that someone -- a person -- inputs the assumed level of risk for each investment, or investment class into the model. The key input.

Which of course assumes that the person inputting actually knows the level of risk involved in an investment.

"assume a can opener....."

So what you end up with is the clear illustration of a concept with little utility. With worse than little utility. With awesome destructive power.

This model formalizes someone's 'educated guesses' and gives them the weight of prognostication via clever formatting. The individual investor is seduced into turning over their hard-earned funds under the guise of hard science.

Hang on...I guess it really is an extremely effective and accurate model....

Effect at separating the investor from his money.

Which brought me back to thinking about how accurate predictive analytics models are in predicting -- and altering -- buyer behaviour. I guess it depends on which buyer's behaviour you seek to alter. The online shopper, or the marketing exec thinking about buying the product?