Technological solutions for personalized post-cookie advertising
Posted: Wed Jan 22, 2025 9:39 am
With the disappearance of third-party cookies in 2025 , the digital marketing sector has had to adapt quickly, implementing technological solutions that allow maintaining the personalization and effectiveness of campaigns, while respecting user privacy. Below, we can see some of the most relevant alternatives:
Local Storage
This solution leverages native browser capabilities to store information directly on users' devices, such as preferences or anonymous identifiers. While it is a basic tool, it is useful for personalizing experiences on a single website, but it does not allow cross-domain tracking.
Unique IDs
Some platforms have introduced unified identification at&t email list systems that are generated with the user’s consent. These IDs allow tracking behaviors across different digital properties, while maintaining greater control over privacy. However, their mass adoption depends on clear standards and the collaboration of multiple actors in the ecosystem.
Privacy cohorts (e.g. Google Topics API)
Google has developed solutions such as Topics API, which replaces the failed FLoC (Federated Learning of Cohorts). This technology groups users into categories based on their recent activity, without identifying specific individuals. While it protects privacy, some question its accuracy for segmenting complex audiences.
Using tools such as data clean rooms and conversion modeling
Data Clean Rooms
These platforms allow advertisers and data owners to collaborate to analyze information in an aggregated and anonymized manner, avoiding the sharing of sensitive personal data. Clean rooms such as those of AWS, Google or Snowflake are becoming a standard for campaign measurement and audience analysis.
Conversion Modeling
Since individual tracking is more complicated, conversion modeling uses advanced algorithms to estimate campaign effectiveness based on aggregated data. While it does not offer the same accuracy as cookies, it is a valid solution to maintain consistent measurements.
Advantages and limitations of each approach
Advantages:
Greater compliance with privacy regulations, reducing legal risks.
Segmentation models that respect privacy and improve brand perception.
Incentive for companies to invest in building direct relationships with their users.
Limitations:
Some solutions, such as unique IDs, rely on explicit user consent, which may limit their reach.
Tools such as clean rooms require significant investments in infrastructure and technical expertise.
The accuracy of personalization is reduced compared to third-party cookies.
Together, these alternatives are transforming the way we approach personalized advertising, promoting a model that is more respectful of privacy and aligned with current consumer expectations.
Local Storage
This solution leverages native browser capabilities to store information directly on users' devices, such as preferences or anonymous identifiers. While it is a basic tool, it is useful for personalizing experiences on a single website, but it does not allow cross-domain tracking.
Unique IDs
Some platforms have introduced unified identification at&t email list systems that are generated with the user’s consent. These IDs allow tracking behaviors across different digital properties, while maintaining greater control over privacy. However, their mass adoption depends on clear standards and the collaboration of multiple actors in the ecosystem.
Privacy cohorts (e.g. Google Topics API)
Google has developed solutions such as Topics API, which replaces the failed FLoC (Federated Learning of Cohorts). This technology groups users into categories based on their recent activity, without identifying specific individuals. While it protects privacy, some question its accuracy for segmenting complex audiences.
Using tools such as data clean rooms and conversion modeling
Data Clean Rooms
These platforms allow advertisers and data owners to collaborate to analyze information in an aggregated and anonymized manner, avoiding the sharing of sensitive personal data. Clean rooms such as those of AWS, Google or Snowflake are becoming a standard for campaign measurement and audience analysis.
Conversion Modeling
Since individual tracking is more complicated, conversion modeling uses advanced algorithms to estimate campaign effectiveness based on aggregated data. While it does not offer the same accuracy as cookies, it is a valid solution to maintain consistent measurements.
Advantages and limitations of each approach
Advantages:
Greater compliance with privacy regulations, reducing legal risks.
Segmentation models that respect privacy and improve brand perception.
Incentive for companies to invest in building direct relationships with their users.
Limitations:
Some solutions, such as unique IDs, rely on explicit user consent, which may limit their reach.
Tools such as clean rooms require significant investments in infrastructure and technical expertise.
The accuracy of personalization is reduced compared to third-party cookies.
Together, these alternatives are transforming the way we approach personalized advertising, promoting a model that is more respectful of privacy and aligned with current consumer expectations.