Firebase provides the ability to analyze your app’s data and create dynamic user groups based on users predicted behavior. Firebase Predictions can work with Remote Config to increase conversions by providing customized experiences based on user behavior, and you can work with notifications composer to deliver the right messages to the right user group.
Firebase predictions can also work hand in hand to assess the effectiveness of prediction strategies. Jumana Jhanb presented a machine learning feature that analyzes past user behavior in order to predict future ones. The model is then asked to take a whole historical span of events and make predictions for the next 7 days.
These predictions are the labels assigned to each user, such as what is shared, spent, agitated, and not agitated.
These rules set the rules for push notifications, push notifications and the labels used, as well as the types of content such as photos and videos.
Firebase Predictions has a prediction for users who do not return to your app (i.e. stop opening the app or any app – associated notifications) but make an in-app purchase. You can use these segments to appeal to users and create your own predictions based on the custom conversions and analytical events you collect in your apps.
Firebase Predictions applies machine learning to your analytical data to make predictions based on the predicted behavior of your users. These predictions are the result of data collected using analytics and enhanced by machine-learning models in your app.
You can also export your app’s forecast data to BigQuery for further analysis or move it to third-party tools. You can also export the forecast data of your apps to bigquery for further analysis. Slide it into third-party tools and export it back to your App Engine forFurther Analysis or Firebase.
You can also use predictions in the notifications to send one-time messages or recurring campaigns. If you use predictions in Remote Config, you can increase conversions by providing custom experiences based on the likely needs of your users.
For example, you can automatically send a notification to predict which apps you should stop using and which ones you should use. Once a forecast is made, it will take some time (depending on the service that created it) for the forecast to be made. The data will be available at least 24 hours after the calculation of the forecasts.
You can create a remote configuration parameter with default values in the Firebase console and add prediction conditions to assign other values based on the prediction.
The number of users you can target with forecast data and risk tolerance is also displayed on the forecast map. This value indicates which users should behave in a certain way over the next 7 days. You can update the status of the forecast data as soon as they are ready for use or as often as you like.
In Codelab, we create a custom prediction that predicts which users are unlikely to finish a game, based on the completed game and analysis events.
You will also learn how to use Predictions and Remote Config together to provide each user in a particular predicted group with different gaming difficulties. In addition to predictions, Firebase Prediction enables the creation of predictions that target users who are likely to trigger analytics conversion events, such as users with high risk tolerance and difficulty.
Today, Predictions makes more than 6 billion predictions per day for developers, enabling them to take meaningful action to make their products more relevant to their customers. We target users with high risk tolerance and difficulty as well as those with low risk of default, such as gamers.
At this year’s Firebase Summit, we announced that the forecasts will gradually become available to the general public, with new features added based on feedback.
While we update predictions based on actual user behavior in your app, I’ve heard from many of you that you want to know how stable your predictions are before you integrate them into your apps.
To make predictions, your app must use Google Analytics to record events, and you can use the Firebase console to start making predictions based on the app’s analytics data and monitor whether they have built-in output. You can also use it to monitor whether there is enough analytics data to make a prediction based on additional analytics events captured by your apps.
To monitor prediction readiness, you need to add analytics to your app, but before you make any predictions, you first need to monitor that your predictions have enough build-build expenditure to have been issued.
To use predictions in your app, you must first use the Firebase console to define a user segment. You can also integrate with Google Analytics to increase conversions by customizing the user experience so that you can customize it based on your users “predicted behavior.