Posts Tagged: social gaming

Applying Lessons Learned on Facebook to Mobile App Development

Author icon Josh Williams|Comments icon 0

The ARM funnel shows a typical customer lifecycle, which is comprised of three stages: acquisition, retention and monetization.

Mobile app developers and marketers can gain a huge advantage by looking at the way social app developers scientifically build and optimize their games around customer data on social platforms. They use that data to learn about what is and isn’t working in their applications, and iterate quickly for increasingly stronger returns. From Acquisition to Retention to Monetization–the three stages in the ARM funnel–successful developers have created entire organizations around data-driven design at each level and across all facets of their businesses.

At Kontagent, we’ve witnessed firsthand the ARM funnel’s crucial role in the success of a large majority of studios that publish games on Facebook–and now on iOS and Android as well. Insights garnered from this model help to focus resources and marketing investment. And, having a perpetual flow of data and a supporting data science infrastructure (with the expertise to act on it), has enabled game studies to acquire users more profitably. Teams of data scientists and developers at Zynga, Popcap, Crowdstar and Gaia, for example, have measured, iterated, tested and optimized relentlessly to acquire more users at better customer acquisition cost and with higher yield ratios than most. It’s no coincidence these are the same guys succeeding on mobile platforms as well. Continue Reading…

User Analytics: A Few Lessons from Moneyball

Author icon Catherine Mylinh|Comments icon 2

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.

Continue Reading…