The open internet is a living, breathing entity that is constantly evolving. With diverse content ranging from individual blogs to premium publisher websites, the open internet covers an incredibly wide range of topics from fashion and food to news, education and travel. In this domain, analyzing online content consumption patterns of consumers not only gives us insight into the context or the environment in which we can reach them, but also enables us to understand their behavior, interests, and ultimately their intent.
Context has always been an important part of creating compelling and relevant advertising experiences for consumers, and with the deprecation of third-party cookies, this is becoming even more important. However, the vastness and diversity of content on the open internet makes analyzing it a challenging problem to solve. This is where advances in AI and machine learning come in.
Ara TopicMap
One of the key capabilities of Ara, our AI and machine learning engine, which underpins the Quantcast Platform, is its ability to organize the open internet into a TopicMap, a multi-dimensional analysis of content by topics and interests. This contextual map empowers our customers to plan custom audiences based on browsing behavior, interest, and intent. Below is a visual representation of the random, messy, and diverse content on the open internet organized by Ara into a TopicMap with various contextual categories. For ease of visualization, this only maps about eight thousand URLs, whereas in reality Ara TopicMap looks at billions of URLs.
How does it work?
Ara scans and gathers content from billions of URLs. It then deploys deep learning-based natural language processing (NLP) to understand the actual text. Finally, it uses machine learning techniques to classify the URLs into contextual categories by analyzing the similarities and differences in the text across nearly a thousand dimensions. To elaborate on what I mean by a ‘dimension,’ here is an analogy: a red car and a red apple are similar along the color dimension, but different across almost all other dimensions. To be able to do the same with the vast and diverse content on the open internet, Ara must self-learn the thousands of dimensions that are most optimal for analysis. Once it learns these dimensions, Ara can then use them to analyze content and generate highly expressive contextual signals. With this multidimensional understanding of content, Ara TopicMap provides:
- Granular audience insights into changing consumer behavior, interest, and intent
- Custom audience definitions based on topics of interest
- Optimal audience reach in optimal environments by integrating contextual signals with predictive audience modeling to ensure context is taken into account for each ad impression
Powering key capabilities in the Quantcast Platform
With Ara TopicMap, the Quantcast Platform empowers marketers and publishers to reach desired audiences based on predefined interests, build custom audiences based on user-defined interests, and get actionable audience insights to inform campaign planning and activation.
- Precisely reach your desired audience
Choose from a list of over 150 predefined interest categories built from Ara TopicMap to precisely reach your desired audience.
- Create unique consumer interest categories
Use a custom list of words or phrases that hone in on consumer interest, passions, and intent to build and activate audiences tailored to your needs.
- Get actionable audience insights in seconds
Eliminate the guesswork in campaign planning by getting interactive and intuitive insights about the topics of interest for your desired audiences.
Ara TopicMap brings scale and accuracy to interest-based audience planning and activation within the Quantcast Platform. Since it is a technology that doesn’t depend on third-party cookies, it also plays a big role in our approach to a world without third-party cookies. You can get a demo here.
Learn More
Learn more about the Quantcast Platform’s capabilities, including cookieless solutions and Quantcast Connect, recently announced at our fall NOVA virtual event.