Norway



Predicting in- behavior with ’s machine learning expertise

Predictions uses deep-learning models powered by TensorFlow to predict app behavior such as the likelihood of a buying a good in a game or the chances of a becoming inactive. Making the most of game event from the Google Analytics for Firebase SDK, it dynamically groups users into different segments based on their predicted behavior.
- LTqEr8bx0vW3gi3r3h7V4iYDT0OqwuAsErQiPv8DDagHwbPOsvcpVin2KcAZIkP0FsAVmRuiPhRDfNpnPdBa34b ejOkEEttGAu9ltWMIGhwpZ497c5Cvy0xMMWowqkf2usEZjjv - Taking the guesswork out of app monetization with Firebase Predictions

These predictive segments are updated daily based on actual and ongoing user behavior. Armed with this information, you can take a variety of actions such as personalizing the UI of your app for users who are predicted to spend money in-app using Firebase Remote Config or sending users an offer via Cloud Messaging.
- Rsi6IYTj5uvD3vivrbr3U6n8i9xSnH8wNqxKfqyFLQB5OSrfC2pRE 7 JyF8cERWwesg0hTujwkuJR36YWTFA3Suf7yDtZfbHQC7YAOc9ZfIMaXL2u3q0I0nYJL2DL0q3Ef EGT2 - Taking the guesswork out of app monetization with Firebase PredictionsHelping developers show ads to the right audiences 

Developers like Rockbite have successfully used Predictions to build smarter strategies. For their game Deep Town, the Rockbite team incorporated rewarded ads to complement revenue from the sale of digital goods. To optimize their strategy and ensure they weren’t showing ads to paying customers, the team first experimented with segmenting players and serving ads by demographics. However, the results from these experiments were inconclusive. So, they turned to Predictions.

Predictions used machine learning to segment the game’s users based on their likelihood to spend. Rockbite started showing ads only to those players who were predicted to not spend. The net result: a 24 percent increase in revenue from the predicted nonspender group! 

Optimizing app strategies with Predictions 

Predictions can help developers optimize their app strategy in many other ways. Halfbrick, for example, used experiments in Predictions to test different strategies for re-engaging customers and minimizing churn. Developers can set up a variety of experiments with Predictions, to maximize their revenue and user engagement. Some examples include:
  1. Experiments with ad frequency Tuning ad frequency (more or less) for players who are predicted to churn or tuning ads so that they’re only shown to users not predicted to spend
  2. Experiments with ad content Showing rewarded house ads for other games in your portfolio to players who are predicted to churn or promoting unique IAP offers to players who are expected to spend 
  3. Experiments with different rewards Predictions can be combined with other analytics attributes, user properties or audience insights to fine tune your strategy so you may, for example, reward only users predicted not to spend in specific geos or regions, or who play your game in specific ways. 

Get started with Predictions 

Predictions has only been in beta for a little while, but we’re encouraged by the results our partners are already seeing with it. If you’re already using Google Analytics for Firebase, you can enable Predictions with a single click in the Firebase Console. Refer to our implementation guides to try some of these strategies for yourself. 
Reach out to us if you have any questions, feedback, or suggestions. We look forward to hearing from you! 


1. Google Play data, Global, Jan. 2017 vs. Jan. . Developer-declared ads & IAP flags utilized from Google Play Store.



Source link

LEAVE A REPLY

Please enter your comment!
Please enter your name here