"It takes a data scientist less than 30 minutes to create a new pipeline. Then, with our fully automated solution, the pipeline is activated within 24 hours."
"… our business moves fast and we needed to make the technology available quickly across our teams. With Cortex, our team is able to build ML into our products in a matter of minutes, as opposed to months."
The value of your 1st party data is increasing dramatically with the death of 3rd party cookies. Many media companies make investments in data warehouses, data lakes, and CDPs to store and manage 1st party data. Cortex empowers your teams to transform this large scale data into advanced advertising segments. Some of the largest global media companies use Cortex to build thousands of ad segments by leveraging techniques like look-alike modeling, classification, prescriptive uplift modeling, real-time behavioral modeling, and goal-based segment optimization.
A machine learning platform empowers your adtech team to not only build look-alike models, but also more advanced segments using techniques like uplift modeling, real-time modeling, and goal-based optimization. All these modeling techniques are available within an intuitive UI enabling you to build and sell new segments with only a few clicks.
Learn how one media business increased ad CTR by 43% using goal-based optimization
Some of the largest global media brands are building segments across hundreds of millions of unique users and billions of behavioral data points. All aspects of the machine learning pipeline are designed to work at scale including automated feature cleaning and automated feature engineering.
Learn how News Corp Australia is using Cortex to Build Segments Across Billions of Behaviors
It’s important to understand how the models are built in order to be certain they are working correctly. Opaque modeling techniques often result in incorrect models and, as a result, put your reputation and future ad revenue at risk. Cortex enables you to dissect your model and understand not only what features are used for modeling but relative feature importance to ensure the highest quality models are brought to market.
