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Firebase Predictions

Firebase Predictions applies machine learning to your analytics data to create dynamic user segments based on your users' predicted behavior. These predictions are automatically available for use with Firebase Remote Config, the Notifications composer, Firebase In-App Messaging, and A/B Testing. You can also export your app's Predictions data to BigQuery for further analysis or to push to third party tools.

When you use Predictions with Remote Config, you can increase conversions by providing a custom experience based on each of your users' anticipated needs.

You can also use Predictions with the Notifications composer to deliver one-time messages or recurring campaigns. For example, you can automatically send a notification to users who are predicted to stop using your app.

With A/B Testing, you can compare the effectiveness between different the Notifications composer campaigns, or use Remote Config to test the result of different ways of customizing the in-app experience for users in a predicted segment.

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Key capabilities

Bring the power of Google's machine learning to your data Firebase Predictions applies Google's expertise in machine learning to your analytics data, creating dynamic user segments based on the predicted behavior of users in your app. With this capability, you can make product decisions based on predicted behavior, rather than historic behavior.
Boost conversions through customized user experiences Firebase Predictions is integrated with Remote Config, FCM, and Firebase In-App Messaging, letting you customize a user's experience based on their predicted behavior. For example, for users who are predicted to spend, you can show a new in-app purchase bundle, while for users who are predicted to not spend, you can adjust the frequency of ads.
Increase retention with smarter notifications Re-engaging a user who has already stopped using your app is tough. By using Predictions, you can engage users who are predicted not to return to your app, before they quit. You can set up a one-time campaign or automate sending notifications for certain predicted groups through recurring campaigns.
Create custom predictions In addition to the built-in predictions—likelihood to spend and likelihood to churn—Firebase Predictions allows you to create predictions based on any conversion event you have defined in your analytics data. Once you define the event, Predictions creates a dynamic user segment composed of users who are predicted to complete that event in your app in the near future.
Export to BigQuery You can schedule automatic daily pushes of your app's prediction data to BigQuery for further analysis or to push the data into third party tools and services.

How does it work?

Predictions are available for iOS, Android, Unity, and C++ apps that include the Analytics SDK. Predictions creates dynamic segments of users who are likely to complete a certain event. You can use these segments to target users with Remote Config, Firebase In-App Messaging, and the Notifications composer.

Firebase Predictions has two predefined predictions: churn, the likelihood that a user doesn't return to your app (that is, stops opening the app or app-related notifications), and spend, the likelihood users will make in-app purchases.

You can also create your own predictions based on custom conversion Analytics events that you collect in your app.

The machine learning model for your app improves as the amount and relevance of data collected using Analytics increases, and as your number of users increases. In addition, the accuracy of the model for a specific user will improve further after that user has used the app for at least a few days.

Do I need to share my data with Google in order to use Firebase Predictions?

You are not required to share your Google Analytics data with Google to help improve Google's products and services. You can turn that off in the Firebase console by going to Analytics > Dashboard > Settings at any time you wish.

You do need to use Google Analytics to log your app data to Firebase. You also need to make sure your Google Analytics data is available in Firebase (see Data-sharing settings).

Please note that Google Analytics uses a shared model. While your raw event data is safe and only available to you inside Firebase, the model quality does improve for everyone who uses Predictions. You need to explicitly opt into Predictions from the Predictions homepage. You can turn off Predictions at any time and your data will no longer be available for Predictions even if it is still available in the rest of Firebase.

Implementation path

Predictions works best for apps with 5,000 or more monthly active users, and apps with frequent usage and spend such as games.

Add Analytics to your app To make predictions, your app needs to record events using Google Analytics.
Enable Predictions and monitor prediction readiness Use the Firebase console to start making predictions based on your app's analytics data and to monitor whether predictions has enough data for the built-in churn and spend predictions. You can also use the Firebase console to monitor whether enough Analytics data is available for predictions that you create based on additional Analytics events collected by your app.
Access predictions results from your app with Remote Config

To use a prediction in your app, first use the Firebase console to define a user segment. A user segment contains the users that fall into the percentile range you specify.

Then, configure Remote Config to set a remote parameter for members of the user segment. Once you have the Remote Config SDK integrated into your app, you can get that Remote Config parameter and adjust your app's behavior accordingly. For example, you can offer a different first screen experience to users who are more likely to spend.

Reach users in a certain prediction segment using the Notifications composer or Firebase In-App Messaging

You can contact users in your predicted user segments using one-time or recurring campaigns using the Notifications composer or Firebase In-App Messaging.

For example, you could use the churn prediction with high risk tolerance to automatically send notification messages to new users who become likely to churn or stop using your app.

Next steps