The Tel Aviv data science and data engineering teams are developing best in class software solutions for brands, agencies, publishers and technology platforms to measure and optimise their advertising, to identify and eliminate ad-fraud and create a healthier online ads echo-system using state-of-the-art data-science and machine learning technologies. Development of Moat Analytics products, Cross-Device Matching and Invalid raffic (bot) detection. Page quality scoring – we aim to score the quality of web pages to better inform advertisers about the potential of serving ads on these sites. This is based on a multitude of data sources available to Oracle Data Cloud and to the broader Oracle, as well as other sources that we can collect ourselves (e.g. scraping & text analysis). This is a newly initiated project that is in the process of being created from scratch by our team, leaving a lot of room for innovation and exploration. Bots and invalid traffic detection – as advertisers pay websites to show their ads, sophisticated fraudsters drive traffic to their own websites in order to be paid more by advertisers. This invalid traffic could be scripts, bot networks, click farms in 3rd world countries, and so forth. These fraud operations are getting more and more sophisticated and costing the ad industry billions of dollars yearly. Our role is to identify if a human or a bot is “viewing” each ad.
We are building the world’s largest identity graph using state-of-the art machine-learning and graph-analysis algorithms. Machine Learning to build the core of our matching technology for the development of high accuracy big data algorithms responsible for grouping devices belonging to same user.
Crosswise (Acquired) provides authoritative anonymous cross-device mapping data. Based on machine learning, data science and Big Data technologies, Crosswise’s device map enables ad tech vendors, retail brands and premium publishers to realize the tremendous benefits of cross-device advertising, content personalization and analytics.