On this page:
- Modelled data vs observed data
- Google's behavioral modelling approach
- Prerequisites
- Show or hide modelled data in reports
- How behavioral modelling appears in Google Analytics
- Unsupported features
When you implement a consent banner for your website or app, Analytics will be missing data for users who decline consent. Behavioural modelling for consent mode uses machine learning to model the behaviour of users who decline Analytics cookies based on the behaviour of similar users who accept Analytics cookies. Modelled data allows you to gain useful insights from your Analytics reports while respecting your users’ privacy.
For example, behavioral modelling estimates data based on user and session metrics, such as daily active users and key event rate, that may be unobservable when identifiers like cookies or user IDs are not fully available. It helps you answer important questions like:
- How many daily active users do I have?
- How many new users did I acquire from my last campaign?
- What is the user journey from landing on my website to actually making a purchase?
- How many of my site visitors are based in Germany vs. the UK?
- What is the difference in user behaviour between mobile vs web visitors?
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Modelled data vs observed data
When users visit your site and grant consent for Analytics cookies or when they don't opt out of personalisation using advertising ID in Android Settings, Analytics associates user behaviour with various identifiers to provide continuity in measurement. We refer to this kind of data as observable data because it comes from users who have given Analytics permission to observe their behaviour.
When users don't grant consent, events aren't associated with a persistent user identifier. For example, if Analytics collects 10 page view events, it can’t observe and report whether that’s 10 users or 1 user. Instead, Analytics applies machine learning to estimate the behaviour of those users based on the behaviour of similar users who do accept Analytics cookies or equivalent app identifiers.
The training data used for modelling is based on the observed user data from the property where modelling is activated.
Google's behavioral modelling approach
Google's behavioural modelling approach applies the following machine learning best practices.
Check for accuracy and communicate changes
Holdback validation maintains the accuracy of Google’s models. Estimated user data is compared to a portion of observed user data that was held back from model training, and the information is used to tune the models. Google will communicate changes that might have a large impact on your data.
Maintain rigorous reporting
Behavioural modelling is only included when there's high confidence of model quality. For example, if there isn’t enough consented traffic to inform the model, then events triggered by users who decline consent aren't reported. This helps ensure the accuracy of the data.
Customise for your business
Google’s more general modelling algorithm is separately applied to reflect your unique business and customer behaviour.
Prerequisites
Because the model is trained on the observed data for your Google Analytics 4 property, your property must have enough data to train the model. To be eligible for behavioural modelling, your property must meet the following criteria:
- Consent mode is enabled across all pages of your sites and/or all app screens of your apps.
- Consent mode for web pages must be implemented so that tags are loaded before the consent dialogue appears and Google tags load in all cases, not only if the user consents (advanced implementation).
- The property collects at least 1,000 events per day with analytics_storage='denied' for at least seven days.
- The property has at least 1,000 daily users sending events with analytics_storage='granted' for at least 7 of the previous 28 days.
- It may take more than 7 days of meeting the data threshold within those 28 days to train the model successfully; however it's possible that even the additional data won't be sufficient for Analytics to train the model.
Keep in mind that meeting the external prerequisites for behavioral modelling listed above doesn't guarantee eligibility because the underlying machine learning model follows a set of eligibility criteria and data thresholds to ensure a high degree of modelled data accuracy. The model factors in additional criteria like the ratio of new to returning users and user to session counts.
Behavioral modelling is automatically enabled when a given property becomes eligible. When the modelling is enabled, it will be selectable in the description of the blended reporting identity.
In the very rare event that a property no longer meets the prerequisites for behavioral modelling after previously meeting them, estimated data will no longer be available. If the property later meets the prerequisites again, estimated data will be available again. The estimated data will be available only from the date when the property became eligible again.
Show or hide modelled data in reports
To see modelled data in your reports, choose the Blended reporting identity:
- In Admin, under Data display, click Reporting identity.
Note: The previous link opens to the last Analytics property that you accessed. You must be signed in to a Google Account to open the property. You can change the property using the property selector. You must be an Editor or above at the property level to control the reporting identity setting which allows you to show or hide modelled data in your reports.
- Select Blended.
- Click Save.
To stop seeing modelled data, select another option. The option that you choose doesn't affect data collection or processing. You can switch between the options at any time without making any permanent impact on data. Learn more about Reporting identity.
How behavioral modelling appears in Google Analytics
Analytics seamlessly integrates modelled data and observed data in your reports. When Analytics includes modelled data, you will probably see differences when compared to reports that include only observed data (for example, higher user counts in reports that include modelled data).
Use the data quality icon (shown below) to see when modelled data is integrated.
The following table summarises the messages that you might see via the icon.
| Data-quality icon status | Description |
|---|---|
| Including estimated user data | As of [modelling effective date], Analytics is estimating data that's missing due to factors such as cookie consent. |
| Including estimated user data |
As of [modelling effective date], Analytics is estimating all possible data that's missing due to factors like cookie consent. |
| Including estimated user data |
As of [modelling effective date], Analytics is estimating data that's missing due to factors such as cookie consent. * Estimated data may not yet be available for yesterday. |
| Excluding estimated user data | Your property's reporting identity setting doesn't allow Analytics to estimate data that's missing due to factors such as cookie consent. Unless you use the blended setting, your reports only include data available from users who consented to the use of identifiers. |
| Estimated user data unavailable | The date range selected is prior to when this property became eligible for estimated data. |
| Estimated user data unavailable | This report includes retention data or a segment which includes a sequence. As a result, it doesn't include estimated data. |
| Estimated user data unavailable | Your property doesn't meet the eligibility criteria to use estimated data. |
Some pages in the Analytics interface will also display a banner with information about the modelling status.
The following table summarises the messages you might see via a banner.
| Banner message | Banner location |
|---|---|
| Most templates include only data from users who consented to the use of identifiers, except for the free-form and segment-overlap templates, which do include data from estimated users. | Explorations home page |
| If an exploration has a segment with a sequence, it will show only data for users who consented to the use of identifiers. | Exploration detail page |
| This [report/exploration/audience] includes only data from users who consented to the use of identifiers. | Exploration detail page |
| If this segment includes a sequence, it will show only data for users who consented to the use of identifiers. | Segment builder |
Modelling in explorations
Modelling in path and funnel explorations is applied differently than in reports. In reports, modelling is applied to metrics like users, sessions and new users counts. However, modelling is not applied to event counts such as page_view, first_visit and session_start. Enabling modelling will make unconsented events (analytics_storage='denied') reportable, however these events will remain unaffected by modelling. If users do not grant consent for Analytics to associate a persistent user identifier with them, Analytics can't detect if the events are the action of the same user. That results in a higher number of first_visit and session_start events for those users, because the event is sent each time they load a page.
In the path or funnel exploration on the other hand, modelling is applied to the first_visit and session_start event. If users do not grant consent for Analytics to associate a persistent user identifier with them, Analytics estimates the true number of first_visit and session_start events. Thus, the first_visit and session_start event count is lower in path and funnel explorations than in reports.
Unsupported features
The following features don't support using modelled behavioural data:
- Audiences
- User explorer, cohort and user lifetime explorations
- Segments with a sequence
- Retention reports
- Predictive metrics
- Data export, for example, BigQuery export