Expanding Internet Advertising Targetable Audiences Using Machine Learning and First Party Data Structures
Why Third Party Cookies are Important
Third-party cookies have been a cornerstone of digital advertising for decades, enabling advertisers to track users across multiple websites, understand their behavior, and serve personalized ads tailored to their interests.These cookies facilitated the creation of detailed user profiles, allowing advertisers to implement highly effective retargeting campaigns and measure the success of their ads. However, growing concerns about user privacy and data security have led to their decline, with major web browsers like Safari and Firefox already blocking them by default, and Google Chrome planning to phase them out by 2024. As the dominant browser with the largest user base, Chrome’s move signals a seismic shift in the advertising landscape. In response, Google has introduced initiatives like the Privacy Sandbox, which aims to provide privacy-preserving alternatives to third-party cookies. These new technologies promise to balance user privacy with advertisers’ need for effective targeting, heralding a new era in digital advertising where first-party data and innovative AI-driven approaches play a central role.
Key Features of Our Project
Cookieless Functionality:
Leverages first-party cookies and on-site user interactions for data insights.
CTR Prediction Models:
Advanced deep learning algorithms to predict user interactions with ads.
Recommender System:
Personalized ad recommendations tailored to user preferences and browsing habits.
Network-Wide Scalability:
Centralized model training with decentralized data processing for efficiency.
Machine Learning & Deep Learning Techniques
Our approach combines various ML and DL methods to achieve robust performance:
Graph Neural Networks (GNNs): To model relationships between users, content, and advertisements effectively.
Transformer Architectures: For capturing sequential user behaviors and contextual relevance.
Ensemble Learning: Combining multiple models to improve prediction accuracy and system reliability.
Federated Learning: Enhancing data privacy by training models locally on user data without transferring it to a central server.
Benefits
Improved Ad Relevance: By analyzing user behavior and preferences, we deliver ads that align closely with their interests.
Increased Revenue: Higher CTRs directly lead to better CPM (Cost Per Mille) values and revenue growth.
Privacy Compliance: Reliance on first-party data ensures adherence to privacy laws and builds user trust.
Vision for the Future
This project not only addresses the immediate challenges of a cookieless world but also sets the foundation for future innovations in digital advertising. By investing in machine learning and deep learning, we are building a system that is not only resilient to industry changes but also capable of delivering unparalleled value to advertisers and publishers alike.
This ambitious endeavor exemplifies our commitment to staying ahead of the curve in the ever-evolving digital advertising landscape.
Contact
Ostim OSB mahallesi, Cevat Dündar Caddesi, No:1/1 Kat:5 No:71, Ostim Teknopark Turuncu Bina, 06374, Yenimahalle, Ankara, Türkiye
+90 530 416 76 16
info@boldblu.com