The Big Time

This week's Directions Magazine's podcast discusses the ever popular question for geographers - when will companies completely embrace the technology? The question is very similar for statisticians too - mostly, both disciplines are kept in a niche. Statistics applies to direct marketing models, quality control, some forecasting, and perhaps some supply chain problems. Geography applies to site selection and maybe some cost forecasting.

Of course, this is a complete generalization. Many companies completely embrace these disciplines in many ways. But as the podcast notes, even Microsoft and Oracle have a hard time selling geography (and their statistical products are light too).

I have two points. First, you don't need fancy software or training to get started in either discipline. Geography, for starters, isn't that complicated. Just get some zip codes, or states, and away you go. When I worked at the Bank, I helped with the Seattle Seahawk credit card - and surprise, surpise, they tend to be centered around Seattle! Don't need a master's in geography for that one. Similarly, you can do lots of statistical analysis with Excel. Just right click on a chart, and you have a regression showing you R^2.

To break from this level of analysis, you need a company that values information. Which is inherently the problem. If executive management doesn't know the difference between a right click regression and something done properly, then how can they decide? Basic statistics are taught in MBA classes and geography isn't, but statistics is typically just for your thesis. It is rarely applied to case studies or integrated into finance, marketing, operations, etc.

Secondly, both disciplines do not speak the language of the corporation - money. Locating a store at this location, because it is analytically proper - either by visual inspection of a map or complex spatial analysis, isn't the point. The root question is - how profitable will this location be? You need to transform your geographical analysis to the Profit and Loss statement - or any other financial document.

Statistics does get closer. With direct marketing models, one can easily calculate gross profit/marketing contribution and show that the marketing campaign was profitable. But it is difficult to jump from a campaign-centric financial document to a corporate strategy financial document.

Until we can make the transition, we'll be in support roles. We help evaluate risk by providing a framework to think about it. With a statistical model, direct mail campaigns have a x% response rate. With this geographic analysis, we are confident that this proposed site will have sales at least chain average.

With all the pressure of quarterly numbers, executive management is watching their financial documents. It is our challenge to be there.