The evolution of data privacy and its impact on advertising continues to be a hot topic among marketers and agencies alike. With the deprecation of third-party cookies approaching, there’s a scramble to find new tracking solutions. Recognizing that the industry is at a crossroads, Google has been experimenting with interest-based audience solutions. In addition to impacts on conversions per dollar and click-through-rates, the experiment also suggested that “AI-powered optimization solutions can positively impact campaign performance.”
Updates from major industry players will only ramp up from here. So, what does this new AdTech era mean for marketers? What do you need to know?
Marketing Doctor’s POV
Here’s our point of view to help you cut through the noise: if you want to keep your current digital measurement strong, then the very last thing you want to do is play the “wait and see game” in hopes that the broader industry will eventually land on the one golden solution. It just won’t happen! Marketing teams shouldn’t necessarily hitch their wagons to specific analytics vendors, but instead commit to trying these new technologies as fast as possible to meet emerging demands.
The Marketer’s Conundrum
AdTech 2.0 refers to the category of technology that focuses on privacy-first measurement beyond the functionality of cookies. Marketers face a seemingly unsolvable conundrum of:
- Modern systems need lots of data to function optimally.
- Users want personalized, relevant experiences.
- Consumers, regulators, and tech players are pushing for an end to legacy uses of cookies web browsers’ storage.
For examples of how various AdTech 2.0 tools solve privacy issues in measurement, we can look at what players like Google and the Snowflake Data Engine are doing as alternatives within the broader AdTech 2.0 category.
AdTech 2.0 in the Case of Google
Google’s approach represents the advantage of having a variety of subscription-based users. They can be aggregated within a walled garden and made accessible to advertising technologies via Federated Data Sharing. Google’s Privacy Sandbox Initiative’s FLEDGE and FLOC proposals encapsulate these efforts.
Overall, methods in this category share data by introducing abstractions layers between tools. Publishers and vendors can then interact with a meta-version of aggregated user data without honing in on any one individual, as can be done with cookie-based measurement.
AdTech 2.0 in the Case of Snowflake
Snowflake uses the same approach to sharing metadata in an anonymized “sandbox environment”. It uses an obscured version of the data while the complete consumer data remains on a person’s device or in a vendor’s server. It never physically moves in order to share.
How Will the Industry Benefit?
These approaches with AdTech 2.0 features address two central challenges to data storage and sharing risks among advertisers and publishers:
- Mitigates the “honeypot effect” where a data breach can expose heaps of data if it is all stored in one place. With federated data, data belonging to anyone or any organization’s servers will never move or be copied from location to location in order to pass data between vendors. Instead it is only available as an abstracted version in an aggregate environment, reducing the possibility of data leaks from data in transit.
- Stops vendors’ tracking tools from being able to learn about one specific individual in depth, and instead makes it so that tools can learn about everyone without learning about anyone. The aggregated and anonymized data pool works under a type of consensus model where a consensus about the greater group encompasses traits that can be used for personalization—a method with many inaccuracies that the martech development community is tasked with improving.
For Agencies & Marketers
Federated Data Sharing is the practice of abstracting data for analysis in sandboxed environments by a number of third party tools, without moving data from its origin location. It can be seen as a paradigm shift in Data Architecture. Agencies will need to sift through a storm of offerings, while SaaS-based services will have to find the most effective ways to preserve detailed measurement and targeting between channels using AdTech 2.0 methodology. They must convince omni-channel marketers that their solution deserves a place in their tech stack! Marketing Doctor practices due diligence in reviewing these offerings, assessing the best tools for advancing client growth.
The evolution of data privacy and its impact on advertising continues to be a hot topic among marketers and agencies alike. With the deprecation of third-party cookies approaching, there’s a scramble to find new tracking solutions. Recognizing that the industry is at a crossroads, Google has been experimenting with interest-based audience solutions. In addition to impacts on conversions per dollar and click-through-rates, the experiment also suggested that “AI-powered optimization solutions can positively impact campaign performance.
Updates from major industry players will only ramp up from here. So, what does this new AdTech era mean for marketers? What do you need to know?
Marketing Doctor’s POV
Here’s our point of view to help you cut through the noise: if you want to keep your current digital measurement strong, then the very last thing you want to do is play the “wait and see game” in hopes that the broader industry will eventually land on the one golden solution. It just won’t happen! Marketing teams shouldn’t necessarily hitch their wagons to specific analytics vendors, but instead commit to trying these new technologies as fast as possible to meet emerging demands.
The Marketer’s Conundrum
AdTech 2.0 refers to the category of technology that focuses on privacy-first measurement beyond the functionality of cookies. Marketers face a seemingly unsolvable conundrum of:
- Modern systems need lots of data to function optimally.
- Users want personalized, relevant experiences.
- Consumers, regulators, and tech players are pushing for an end to legacy uses of cookies web browsers’ storage.
For examples of how various AdTech 2.0 tools solve privacy issues in measurement, we can look at what players like Google and the Snowflake Data Engine are doing as alternatives within the broader AdTech 2.0 category.
AdTech 2.0 in the Case of Google
Google’s approach represents the advantage of having a variety of subscription-based users. They can be aggregated within a walled garden and made accessible to advertising technologies via Federated Data Sharing. Google’s Privacy Sandbox Initiative’s FLEDGE and FLOC proposals encapsulate these efforts.
Overall, methods in this category share data by introducing abstractions layers between tools. Publishers and vendors can then interact with a meta-version of aggregated user data without honing in on any one individual, as can be done with cookie-based measurement.
AdTech 2.0 in the Case of Snowflake
Snowflake uses the same approach to sharing metadata in an anonymized “sandbox environment”. It uses an obscured version of the data while the complete consumer data remains on a person’s device or in a vendor’s server. It never physically moves in order to share.
How Will the Industry Benefit?
These approaches with AdTech 2.0 features address two central challenges to data storage and sharing risks among advertisers and publishers:
- Mitigates the “honeypot effect” where a data breach can expose heaps of data if it is all stored in one place. With federated data, data belonging to anyone or any organization’s servers will never move or be copied from location to location in order to pass data between vendors. Instead it is only available as an abstracted version in an aggregate environment, reducing the possibility of data leaks from data in transit.
- Stops vendors’ tracking tools from being able to learn about one specific individual in depth, and instead makes it so that tools can learn about everyone without learning about anyone. The aggregated and anonymized data pool works under a type of consensus model where a consensus about the greater group encompasses traits that can be used for personalization—a method with many inaccuracies that the martech development community is tasked with improving.
For Agencies & Marketers
Federated Data Sharing is the practice of abstracting data for analysis in sandboxed environments by a number of third party tools, without moving data from its origin location. It can be seen as a paradigm shift in Data Architecture. Agencies will need to sift through a storm of offerings, while SaaS-based services will have to find the most effective ways to preserve detailed measurement and targeting between channels using AdTech 2.0 methodology. They must convince omni-channel marketers that their solution deserves a place in their tech stack! Marketing Doctor practices due diligence in reviewing these offerings, assessing the best tools for advancing client growth.
