The impending expiration of third-party cookies presents the advertising sector with unique challenges and opportunities.
Some of the most technical minds work in the adtech industry, from large multinational corporations to innovative small-scale start-ups. But the industry is currently facing significant disruption due to the lack of cookies and the ability to use personal data in advertising.
Future privacy concerns may arise with the suggested workarounds that take identity into account, and it is still unclear how easily these audiences can scale. However, without this information, advertisers would have to target anonymous users. Since the pandemic, large audiences have moved quickly to connected TV, which is still a developing environment for advertising and data consistency.
Despite the present difficulty, cookie-based targeting has always had some drawbacks. Although it has been helpful in determining if a person is appropriate for a campaign, it is predicated on a number of assumptions and frequently only considers past behavior.
The browsing moment has long passed by the time the data is usable, and on top of that, cookie-based targeting has never been easy to scale.
Marketers can now process superhuman amounts of data quickly thanks to AI and machine learning, which is making a difference. With the advent of this new capability, clever, privacy-friendly methods for understanding audiences during live campaigns are now possible. These insights can then be expanded upon to rapidly locate new users who are pertinent.
Also Read: Three Key Strategies to Prevent Customer Churn with AI
Pre-processing and inputs
Machine learning is often used to process large amounts of data at scale, but the real art of AI solutions is frequently found in the input system built around the algorithms, which organize and cleans data from various sources. Here is where the true potential of AI can be seen.
It combines data and systems to offer scalable, effective cookie less targeting. As a result, a clever AI system is created to increase first-party audiences. This system is proving especially helpful in scaling sparse but valuable data to deliver qualified reach and relevance at the same time.
The IT teams can utilize an AI method based on deep learning that analyzes signals and contexts before making targeted recommendations with a high likelihood of bringing in new and relevant audiences.
Its core consists of a sophisticated recommendation system that models and captures the available contextual data using proprietary feature scores. It also takes into account other audience behavioral indicators like brand awareness, ad engagement, and performance. With the help of these inputs, the recommendation engine expands campaigns to instantly locate new audiences with related interests.
Also Read: Why Marketers Need to Hop On the AI Train
Rapid response
Speed and cost can be issues with recommendation engines. It’s one thing to be able to analyze and process the contextual data you’re seeing in a campaign, but it can be difficult to act quickly enough for these insights to be useful due to how most recommendation systems operate.
This is addressed by layering the system infrastructure and implementing cutting-edge technologies like serverless architecture, containerization, and specialized databases. To enable agile targeting at speed, the system pre-computes massive tasks in advance, recalls, and ranks results.
To return to the topic of solving business problems, it is important to note that when learning and recommending systems are combined with valuable first-party data, a proprietary AI infrastructure is created that can scale quickly to small but valuable known audiences in a way that is both practical and distinctive every time.
Undoubtedly, the advertising industry is changing quickly, and AI and machine learning are fostering innovative new ideas that should support the open web’s success in the era of privacy-first technology.
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