New Breakthrough Targeting Technology Introduced by Cedato Overcomes GDPR Pitfalls

New Breakthrough Targeting Technology Introduced by Cedato Overcomes GDPR Pitfalls And Improves Accuracy in Programmatic Video Transactioning

Cedato’s Contextual Lookalike Targeting technology, based on data from billions of video ads served, coupled with contextual targeting, is already generating strong results for advertisers.

New York, July 30, 2018 – Cedato, the leading programmatic operating system for video, officially announces its Contextual Lookalike Targeting technology, developed on top of the company’s powerful Predictive Knowledge Graph. Based on data collected from over 400 billion video transactions, the new technology is able to determine when and where to serve a given video ad to achieve advertiser KPI’s such as completion rates, engagement rates and more. The new technology uses performance data collected from billions of ads, including scrolling, hovering, clicking, tapping, and completed views. The data is processed by Cedato’s advanced machine learning algorithms and analyzed by its predictive technology to determine the content, placement and ad units that will yield the desired results for a marketer’s video ad based on results generated for look alike ads.

The new targeting technology introduces a fresh new approach as an alternative to user-based targeting, previously a main anchor for advertisers. With new cookie restrictions announced by Apple and iOS, as well as the recently enforced GDPR, advertisers are seeking new methods for effectively reaching their target audience. Cedato’s Contextual Lookalike Targeting, while offering marketers and publishers GDPR-compliant and privacy-friendly targeting, enabling advertisers to maintain control over their engagement with the right users, has also been able to improve the pricing model for both programmatic advertisers and publishers, with CPM’s directly linked to target KPI’s.

According to the new targeting methodology, an automotive ad campaign looking to achieve certain engagement KPI’s would be able to identify the exact inventory that had previously delivered similar KPI’s for “look alike” campaigns. The advanced machine learning algorithms process all the campaign parameters to formulate a unique campaign profile. That profile is then replicated within the Predictive Knowledge Graph to determine the inventory that will yield the best results for it.

“By identifying patterns within our video transactions, we are able to provide our advertiser customers with a steady sense of control despite the turbulent market forces we are all experiencing” said Ron Dick, CEO, Cedato, “Targeting is a main pillar of programmatic advertising and as such a necessary base for the continued growth of the industry. As a technology leader, Cedato is committed to introducing innovations that will support that growth.”

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