Predictive metrics

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.

Metric Definition
Purchase probability

The probability that a user who was active in the last 28 days will log a specific conversion event within the next 7 days. Currently, only purchase/ecommerce_purchase and in_app_purchase events are supported.

These models are trained on the 28 most- recent days of data.

Churn probability

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.

This model is trained on the 28 most-recent days of data.

Revenue prediction

The revenue expected from all purchase conversions within the next 28 days from a user who was active in the last 28 days.

 

Prerequisites

In order to successfully train predictive models, Analytics requires the following:

  • A minimum number of positive and negative examples of purchasers or churned users. In order to be eligible it is required that 1,000 users triggered the relevant predictive condition and that 1,000 users did not.
  • Model quality must be sustained over a period of time to be eligible.
  • To be eligible for both purchase probability and churn probability, a property has to send purchase and/or in_app_purchase events (which are collected automatically).

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 are available in the audience builder and in Analysis.

Audience builder

Predictive metrics can be used to create predictive audiences in the audience builder.

Analysis

You can use Purchase probability and Churn probability in Analysis within the User lifetime technique.

Best practices

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 purchase, and/or in_app_purchase events. 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. Also, please note that although we will continue to process the ecommerce_purchase event, we now recommend the purchase event instead.

Collecting a larger variety or volume of meaningful events corresponding to user behavior will help enhance our models and improve predictions. Likewise minimizing noisy events that are not meaningful in terms of user behavior will also help improve predictions.

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