Cryptography for business: MBA students decode profit opportunities

Author: Mendoza College

Cryptography for business: MBA students decode profit opportunities

The first thing you learn in the MBA course Business Forecasting and Data Mining is how to read Professor Barry Keating’s syllabus. It looks something like this:

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As is the case on the syllabus, the text above has been encrypted or “enciphered.” Here it’s a pretty simple code, a substitution. The second paragraph is just a repeat of the first with each letter moved forward 13 places in the alphabet.

The actual course syllabus is much harder to decipher, but decipher it MBA students must because the first test is on information contained in it, such as what percentage of the final grade will be derived from test scores.

It isn’t that Keating, Mendoza College’s Jesse H. Jones Professor of Finance, enjoys messing with students’ minds. It’s that he sees forecasting as being just like decrypting: You’re faced with a mind-boggling array of data, and your only hope of understanding it is to spot the patterns.

In business, that could mean something as simple as noticing how, year after year, your company’s sales are higher (or lower) in certain seasons or during certain points in the macroeconomic business cycle. With such insights, you could adjust your manufacturing or staffing to maximize profits.

Data mining, on the other hand, is about unearthing precious management metal from mountains of recorded information. In the Information Age, the volume of such material has become almost unfathomable. For instance, today’s sophisticated online merchants, Keating says, record every keystroke of every visitor to their sites.

“It is a gold mine,” he says. “It’s just like the ’49ers in California. This data is just waiting to give up its secrets, and we now have some tools to get it.”

Jared Shawlee (MBA ’11), senior director of ticket sales and strategy for the San Jose Earthquakes Major League Soccer team, is already using what he learned in Keating’s course to develop a dynamic-pricing strategy for the team’s individual-game tickets. Instead of charging the same for every game, prices will be set (before the season starts) based on historic data showing which games are likely to be most in demand. Those could be games in better weather months or when a star player is coming to town. After the season is under way, prices for any remaining tickets will continue to rise and fall like shares of stock, depending on demand.

Keating says students who take Business Forecasting and Data Mining don’t typically become professional forecasters or data miners themselves, but almost all business people will face challenges involving forecasting at some point in their careers.

This class teaches you how to do it yourself or evaluate the abilities of organizations looking to sell you their services.