Monetizr’s ML algorithm gathers 4.2 million player sessions. More data, more revenue for game developers.

In the last 3 months, Monetizr’s tech gathered 4,2m data points of player sessions via Monetizr’s iOS SDK. The immediate question is what’s in it for developers and what the heck the algorithm is doing. Here are more overview and the value we bring.

4.2 million player session, but so what?

Where is the value for game devs you might ask? Simple.The more player sessions, the better the conversion rates. More revenue for developers.

What the algorithm is doing?

What the algorithm essentially does it’s collecting data points to use collective data learning. The main purpose of this algorithm is to analyze collected data and determine if the player who has launched a specific game will become a paying customer.

What’s the process?

When our customer integrates with our infrastructure we provide them with a general-purpose publishable API key that can be used to access Monetizr public API.

If our customer uses Unity, Android or iOS SDK we gather telemetric data about a number of launches, a number of times when our rewards have been opened, seconds while rewards are visible, when rewards are closed, devices overall data, available payment options, and other data that are not GDPR regulated.

The overall architecture.

When the end-user initiates the checkout process and proceeds through the payment process Monetizr initiates the order fulfillment process and collects data about orders, purchases and analyzes data to provide our customers with conversion rates and order tracking. The overall architecture is as seen in this diagram

Showing real-time ROI in the future.

Over time, games on Monetizr’s network can learn from each other and improve their business KPIs from conversion rates, retention or CLTV. With the integration of Firebase and attribution services, Monetizr will be able to suggest the best placement suggestions, most effective strategies and even show real-time campaign ROI.

Thanks to our investors Alfabeat who are backing this R&D project.