Consent Mode

Here at SegmentStream, we prioritize the privacy of your users and want to ensure that you have a clear understanding of how behavioral data is handled within our application.

Consent-Driven Model

We strictly adhere to a consent-driven model, collecting behavioral data only when users explicitly consent through the implemented consent management solution on your website or app. This guide emphasizes transparency in our data practices and aligns with industry standards.

Implementing Consent Mode

Consent implementation will differ depending on the event tracking method used to integrate SegmentStream, for more information on event tracking please refer to the relevant documentation section - Events tracking.


When using event tracking through a streaming connection, data is sent directly to SegmentStream servers. If users give consent, their choices align with Google Analytics 4 for consistent and compliant data collection. This means that if consent mode is set up in the Google Analytics 4 tag forwarding data to SegmentStream, it will be processed accordingly.

Google Analytics 4 BigQuery Link

When events tracking is using the BigQuery Link, SegmentStream will access data that has already been processed according to the consent mode set up for the connected Google Analytics 4 stream. It is recommended to review your GA4 consent mode implementation before connecting SegmentStream.

Adobe Analytics

When event tracking is implemented through Adobe Analytics, SegmentStream will only be able to access data that has been collected in accordance to the rule conditions set in the Adobe customer consent implementation. It is recommended to review your Adobe Analytics consent mode implementation before connection SegmentStream.

Consent State Behavior

If users reject statistics (analytics) cookies, the event data from their sessions is anonymized and non-identifiable. Despite being anonymized, this event data remains valuable for enhancing SegmentStream's machine learning models, contributing to the creation of a more accurate prediction model.