Be the Brad Pitt Billy Beane of the Social and Mobile Web

What do Kontagent data scientist Martin Colaco and Billy Beane have in common?

On the surface, probably not much. But, when it comes to analytics, Beane changed the game for baseball. Colaco could be doing the same for business.

Moneyball is based on Michael Lewis’s book, Moneyball: The Art of Winning an Unfair Game. Faced with a relatively meager budget, Oakland Athletics manager Billy Beane (played by Brad Pitt) uses a groundbreaking, sabermetric approach—the specialized analysis of baseball through objective, empirical evidence that measures in-game activity—to recruit a competitive baseball team. (Read more on how sabermetrics works.)

Beane goes against convention, taking advantage of more empirical gauges of player performance to build a team that could successfully compete in Major League Baseball. He analyzes stats that are not generally considered top priority in the traditional scouting process. While most managers pored over stats like running speed, stolen bases and batting averages, the sabermetric approach, developed by baseball statistician Bill James, predicts other factors are better indicators of a player’s offensive success, e.g., on-base and slugging percentages.

Beane basically stared the “old guard” of baseball recruiting in the face and said, “Suck it.” And it worked.

Using sabermetrics, the Oakland A’s were able to put together a competitive team on a $41 million salary; by comparison, the New York Yankees spent more than $125 million in payroll in the same 2002 season. It paid off: the Athletics led a 20-game winning streak, and Beane shifted the paradigm of baseball scouting forever.

When it comes to Web business analytics, it’s not a bad idea to take a page out of the Moneyball book. Sometimes you have to look at data through a different lens in order to better make predictions and optimize opportunities. In baseball, Beane approached player stats differently and got different results. In business, data scientists like Colaco say there’s a similar shift happening in the world of Web 3.0.

Moneyball lesson #1: Challenge the status quo.
There’s a scene in Moneyball where Beane and his scouts are arguing over which players to recruit. His scouts want the highly sought-after players. Beane can’t afford them. Frustrated, he says, “You guys are talking the same non-sense. We’ve got to think differently.” Thinking differently, it turned out, made all the difference in the world.

It’s a good lesson to remember when it comes to business. With the explosion of social and mobile applications, we have to move away from the “old guard” of analytics and begin measuring KPIs around virality, engagement, retention, and monetization. It’s no longer about page views; it’s about users. What are these users doing within your applications? Every click of every action of every second. How can we use that information to win? We’ve got to think differently.

Moneyball lesson #2: You can compete with the New York Yankees.
Companies like Zynga, Playdom and CrowdStar aren’t wildly successful just because they’re in the social gaming space. No, these companies are behemoths when it comes to data. And, they’ve figured out how to collect, store, analyze and optimize all this data to succeed.

But what about the little guys who are trying to compete in this space? They may not have the infrastructure to support such massive amounts of data (or the hundreds of thousands of dollars needed to get that infrastructure in place). How do they (think: Oakland A’s) go up against Zynga (think: New York Yankees with the deep pockets)?! Luckily, analytics platforms like kSuite by Kontagent now provide the infrastructure needed to access data for organizations of all sizes.

Okay, great—you have access, but now what?

Moneyball lesson #3: Look at the data that really matters; it may not be obvious at first.
In Moneyball, Beane relied on Yale economics graduate Peter Brand (Jonah Hill) to help him make sense of all of the stats. It was Brand who took the core data and interpreted it so that Beane could make better scouting decisions.“Your goal shouldn’t be to buy players; your goal should be to buy wins,” he says.

The lesson here? Good data is useless if it’s mismanaged.

The right analytics solution needs to give you not only access to big-data analytics, but also an easy-to-understand dashboard and support from data scientists (your own team of Peter Brands!).

Data scientists can help you understand what the patterns of data are telling you to find the users with the greatest potential for monetization and lifetime value. What might appear obvious could be deceiving when you slice the information differently. And it’s these users that you want to understand and develop. It’s these users you need to understand. In. Order. To. Win.

Call it leveling the playing field.

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About the author: Catherine Mylinh is a member of Kontagent’s storytelling team, where she is head of content marketing. She credits her journalism and computer science roots—she was once a programmer!—for her love of learning and writing about all things high tech. You can contact Catherine at @cat_mylinh.