An interesting quote from an interview with Hal Varian in the Real Time Economics blog:

I think marketing is the new finance. In the 1960s and 1970s [we] got interesting data, and a lot of analytic fire power focused on that data; Bob Merton and Fischer Black, the whole team of people that developed modern finance. So we saw huge gains in understanding performance in the finance industry. I think marketing is in the same place: now we’re getting a lot of really good data, we have tools, we have methods, we have smart people working on it. So my view is the quants are going to move from Wall Street to Madison Avenue.

(Ok I know the interview is from 3 months ago, but I just found it).

In case you don’t know, Hal is now the chief economist at Google. Not to be outdone, Yahoo! has hired Preston McAfee and a bunch of other smart people. Hal, Preston: If you need more people, please drop me an email :)

Anyway, I agree with Hal. The Internet and e-commerce have generated fantastic volumes of data suitable for demand estimation, which is at the heart of quantitative marketing. The basic data you need for this is information about what people purchased and what they paid. Sites like Amazon must be sitting on an absolute treasure-trove of such data. The analytical tools are getting quite sophisticated too. I’m more of a theory person but recently I’ve been reading up on the ‘BLP method‘ for estimating demand, and judging by how complicated it is, it must be pretty sophisticated. (See also the recent podcast on EconTalk with Ian Ayers, author of Supercrunchers).

It seems inevitable that businsess will become more and more quantitative as such methods for data analysis become more well known. I was thinking about what students should study to get the skills necessary to compete in this quantitative world. I think a graduate degree (’postgraduate’ if you’re European) is probably essential — at least a Master’s degree, if not a PhD. In terms of specific things to study, I came up with the following list

• Economics (of course), especially microeconomics, econometrics and game theory. Study to advanced graduate level.
• Statistics: Study theory to advanced undergraduate level or graduate level, plus some applied courses in data analysis. Get good at using software like Eviews, SPSS, or R.
• Applied mathematics: To at least intermediate undergraduate level. Learn how to use Matlab or Mathematica.
• Marketing: Take some quantitative courses, plus maybe a bit of theory.
• Computer programming: Take some computer science courses to intermediate undergraduate level. Learn one programming language quite well (it doesn’t really matter which one).
• Information systems: You need to know how to converse with databases and write scripts. Basic or intermediate undergraduate level should be enough. Or just play with MySQL and learn by yourself.

Maybe this list sounds a bit tough, but remember: If it’s easy, it won’t make you rich.

Prediction markets aren’t the only way to predict things. Probably the most popular way (aside from crystal ball gazing and guessing) is to use mathematical relationships that appear to hold true over time, and project them into the future or apply them to new data values. Author and Yale professor Ian Ayres has put together a list of online tools to do this kind of kind of forecasting on his site. Examples include predicting how long you’ll live, predicting your child’s height as an adult, etc.

Ian also has a book out on the topic of how businesses and other institutions are using data analysis to change the way they work. I haven’t read it yet, but it looks interesting so when I get a chance I’ll read it and post a review here.