But those sorts of apps aren't just the most popular ones, they're the most profitable too, with some of the biggest games and time wasters netting the companies behind them millions of dollars a day.
What's surprising though, isn't that that they make as much money as they do, but that they do so off of such a small percentage of their player base. While it does differ a little from app to app, the average percentage of free to play users who actually put money into their app is often as low as five percent.
That obviously is enough to keep many companies sustained, but that means there are a lot of other customers and there is plenty of money being left on the table. So how do you go about turning those non-paying users into paying ones? Presumably, not with the same tactics that you used for getting your already paying users.
This is where Tapjoy comes in. Tapjoy has developed an analytical system that is designed to target users with specific marketing campaigns and prompts, based around statistics gathered during their use of the application, and on predictive analysis of the future.
While it was launched only recently after the company purchased South Korean analytics firm 5Rocks back in August, Tapjoy says it is already seeing a lot of interest in its new platform. This led to it increasing its supported app base to 9,200 in the fourth quarter of 2014, with many clients that had used its services previously coming back to take advantage of the new analytics and predictive services.
I spoke with Patrick Seybold, Tapjoy's VP of global communications and marketing, who said of the response to Tapjoy's new service, “We can now serve our partners with a solution consisting of predictive analytics, marketing automation, and both ad and IAP based monetization in one single SDK. App developers can now know the future lifetime value of their users and take action to drive them to either IAP or ad based monetization based on the predicted actions they'd take.”
The analytics platform isn't designed to wring customers for every cent though. In many instances, it's about making ads and prompts more applicable to the user. Consequently, those who are already spending (or likely to) will see far fewer ads and have a more streamlined app use experience.
“A developer likely wouldn't serve ads to someone who is going to spend or complete an in-app purchase in their app, and conversely they're likely to serve ads to someone they know would never make a purchase, so they could generate revenue from them."
Developers might hope that those non-buyers will change their minds, but at minimum, the developer can reap the benefit of getting ad impressions out of non-paying customer, and bring in at least a small amount of revenue.
Analytics certainly has the potential to deliver a personalized experience to customers. What are some of the solutions your firms have to do the same?