Synthetic User and ID Pooling
Many online businesses are facing significant challenges in tracking user behavior due to the combined effects of ad-blockers, privacy-focused browsers like Safari and Firefox, and users' refusal to consent to data collection. Studies show that on average, companies lose up to 70% of user data due to lack of consent, ad-blockers and tracking prevention technologies. This leaves only 30% of available data for marketing decisions. As a result, websites' analytics are inaccurate, and crucial data on user behavior remains inaccessible. This lack of data affects business decision-making, marketing optimization, and user experience improvements, particularly as it relates to product recommendations and targeted advertising.
Synthetic user technology and ID pooling offer innovative solutions to these challenges, providing a legally compliant framework for data utilization while maintaining user privacy.
Synthetic User Implementation:
Activation of JENTIS Essential Mode: JENTIS Essential Mode is a feature that technically enables simultaneous tracking based on two user behaviour scenarios:
User provides consent: the access to a user device and storing of data on user device is enabled after the consent is received / user clicks “consent”.
User does not provide consent: JENTIS system allows website operators to configure website tracking within the limitations of the access to and storage of data on user device based on “strictly necessary” exception under ePrivacy Directive.
JENTIS Essential Mode needs to be activated to deploy synthetic user technology, enabling the creation of artificial user profiles that simulate the behaviors of non-consenting users. This process ensures that both consented and non-consented users’ data are used to generate 100% behavioral visibility in analytics.
Data Processing Flow:
Real User Data Collection: Data from the 30% of users who consent to tracking is collected using server-side tracking methods. The JENTIS system can be configured to process common behavioral metrics such as session duration, page views, items added to cart, scroll rate, and other predictors.
Identifying predictors: The data of consent users is measured as accurately as possible and analyzed to find unique "predictors" - non-personally identifiable data sufficient to bundle users. Typical example of predictors are browser, session duration, number of page views. The predictors determined by the algorithm are subject to ensuring that they are permissible under the strict necessity exception §25 para.2 TTDSG and Article 5(3) of the ePrivacy Directive. Corresponding raw data is completely deleted before the data is passed on to other systems.
Non-Consent Data Handling: For the remaining 70% of users, JENTIS systems are configured to process predictors as “strictly necessary” data in a non-persistent manner.
Data Synthesis and Pseudonymisation:
Synthetic Data Creation: Synthetic users are generated by combining predictors from non-consenting users with the real behavioral data of consenting users. This data synthesis adheres to the privacy-enhancing technologies (PET) framework, ensuring the artificial data is non-personal and pseudonymized as defined under Art. 4(5) GDPR.
Deletion of Raw Data: After the synthetic user profiles are generated, all raw data from non-consenting users is permanently deleted to ensure no trace of personal or identifiable data remains, further reinforcing compliance with GDPR.
ID Pooling:
As an additional step, synthetic user data sets can be populated by random third party tracking parameters, such as Google Click ID (GCLID) for example. When randomly assigned without an existing link to an individual user, such parameters may serve to implement conversion tracking for a group of synthetic (non-identifiable) users. Although synthetic user is deemed pseudonymised data in legal terms, the process of creating synthetic users rendereds such data anonymous for third parties becuase re-identification or singling out of an individual out of the synthetic user data set becomes highly unlikely.
The use of JENTIS Synthetic User Engine ensures compliance with both GDPR and TTDSG (ePrivacy Directive) through the following key mechanisms:
Pseudonymisation (Art. 4(5) GDPR): Synthetic data is treated as pseudonymized rather than anonymized, meaning the data is no longer directly linked to identifiable individuals. This classification protects user identities while enabling the e-commerce platform to use synthesized behavioral data legally.
Data Minimization and Necessity (Art. 5 GDPR): The platform adheres to GDPR’s principles of data minimization by only processing necessary non-personal predictors for synthetic user creation. No unnecessary personal data is collected from non-consenting users.
Legitimate Interest (Art. 6(1)(f) GDPR): The processing of synthetic user data is justified under the legal basis of legitimate interest. The platform ensures a clear balance of interests through a Legitimate Interest Assessment (LIA), documenting that the need for comprehensive user behavior analysis outweighs the minimal privacy impact due to the pseudonymization process.
Data Transfers and Third-Party Access: By employing server-side tracking, the platform maintains full control over its data, reducing the risks associated with third-party processors. The synthesized data is safely transferred to marketing and analytics tools, ensuring ongoing compliance.
Additional Resources
For legal assessment of Synthetic User and ID Pooling, please have a look at:
For any questions feel free to reach out to your direct contact at JENTIS or to the JENTIS Compliance & Legal Team via email at privacy@jentis.com.