About predictive metrics
Google Analytics automatically enriches your data by bringing Google machine-learning expertise to bear on your dataset to predict the future behavior of your users. With predictive metrics, you learn more about your customers just by collecting structured event data.
The probability that a user who was active in the last 28 days will log a specific conversion event within the next 7 days.
The probability that a user who was active on your app or site within the last 7 days will not be active within the next 7 days.
The revenue expected from all purchase conversions within the next 28 days from a user who was active in the last 28 days.
in_app_purchase events are supported for the Purchase probability and Revenue prediction metrics.
ecommerce_purchaseevent, we now recommend the
In order to successfully train predictive models, Analytics requires that the following criteria are met::
- A minimum number of positive and negative examples of purchasers and churned users. In the last 28 days, over a seven-day period, at least 1,000 returning users must have triggered the relevant predictive condition (purchase or churn) and at least 1,000 returning users must not.
- Model quality must be sustained over a period of time to be eligible.
- To be eligible for both the purchase probability and predicted revenue metrics, a property has to send the
purchase(recommended for collection) and/or
in_app_purchase(collected automatically) events. When you collect the
purchaseevent, you need to also collect the
currencyparameters for that event. Learn more
Predictive metrics for each eligible model will be generated for each active user once per day. If the model quality for your property falls below the minimum threshold, then Analytics will stop updating the corresponding predictions and they may become unavailable in Analytics.
You can check the eligibility status of each prediction by going to the predictive section within suggested-audience templates in the audience builder.
Using predictive metrics
Predictive metrics can be used to create predictive audiences in the audience builder.
You can use Purchase probability and Churn probability in Explorations within the User lifetime technique.
In your data-sharing settings, enable the benchmarking setting. You benefit when this setting is ON because Analytics is able to use shared aggregated and anonymous data to improve model quality and improve your predictions.
Make sure to maximize the use of event recommendations in your property.
Make sure you are collecting the
in_app_purchase events are collected automatically. However, you must link to Google Play via your Firebase account in order to see the
in_app_purchase event if you have an Android app. Keep in mind that although we will continue to process the
ecommerce_purchase event, we now recommend the
purchase event instead.
If you define a custom audience and add predictive conditions to use In-app purchase probability and Purchase probability, only users who complete both a
purchase and an
in_app_purchase will be included in the audience.