Oracle’s Moat unveils new invalid traffic detection capabilities, achieves rigorous MRC accreditation for SIVT of desktop and mobile web traffic

Moat’s Advanced Functionality Uses Insights from Oracle’s Proprietary Data Assets to Help Identify Bots & Other Invalid Traffic

Oracle’s Moat, a SaaS analytics measurement provider for marketers and publishers that is part of the Oracle Data Cloud, today announced new capabilities to detect invalid traffic, including traffic from sophisticated bots designed to look like consumers. Moat’s new functionality utilizes insights gained from Oracle’s proprietary data assets, including its Zenedge and Dyn acquisitions.

Highlighting the importance of these efforts, Moat also announced it has achieved the rigorous accreditation standards set by the Media Rating Council (MRC) for its desktop and mobile web Sophisticated Invalid Traffic (SIVT) detection.

“Collaborating with teams across Oracle has given us access to proprietary intelligence and approaches that uniquely position us to deliver even deeper insights for our customers and help drive business outcomes,” said Jonah Goodhart, SVP of Oracle Data Cloud and Co-Founder of Moat. “Along with our MRC accreditation, this strengthens the value of our invalid traffic detection capabilities as we continue equipping brands to make smarter decisions, improve transparency and move our industry forward.”

In addition, Moat has been granted accreditation by the Media Rating Council (MRC) for its detection of Sophisticated Invalid Traffic (SIVT) across desktop and mobile web. The milestone comes at a more crucial time than ever, as sources of invalid traffic have grown in complexity over the past year. In accordance with MRC guidelines, Moat’s platform includes a new breakout of GIVT and SIVT rates to give publishers and marketers an extra level of insight into traffic sources. These metrics are mutually exclusive, filtered first for GIVT before reporting SIVT.

Other enhancements to Moat’s platform arm brands with advanced metrics, including:

• Hidden Ad Rate—As part of Moat’s SIVT detection, this metric quantifies ads that are hidden from users for the entire duration of an impression, including the detection of different types of Hidden Ads.
• Session Hijacking—Another component of Moat’s SIVT detection, this metric detects manipulated human activity, such as when a user session is forcibly redirected to another website, tab, or app store.
• Invalid Domain Rate—An update to Moat’s Invalid Domain detection captures sophisticated domain-spoofing that attempts to take advantage of marketers and premium publishers alike.

“We congratulate Moat on the significant achievement of earning MRC accreditation for its Sophisticated Invalid Traffic detection and filtration capabilities for desktop and mobile web traffic,” said George W. Ivie, Executive Director and CEO of the MRC. “With the industry focused on improving transparency and reducing waste throughout the digital media supply chain, Moat’s MRC accreditation for SIVT once again clearly demonstrates that it’s at the forefront of industry leaders in promoting high quality digital measurement.”

“Transparency is critical to overcoming the impact of invalid traffic. Our integration with Oracle’s Moat opens new doors to greater measurement and gives us a better understanding of how viewability metrics, media quality and inventory integrity holistically impact the bottom line,” said Robert Stone, Senior Director, Digital Center of Excellence, Dr Pepper Snapple Group.

“The threat of invalid traffic impacts the overall health of our ecosystem,” said Luis Di Como, Senior Vice President of Media, Unilever. “Now more than ever we need technology offerings, like Moat’s platform, that deliver greater transparency and accountability by providing advanced metrics. It’s equally important that solutions providers work with the MRC to meet its rigorous standards for traffic validation and filtration.”

To learn more about invalid traffic, register for Moat’s webinar here: http://info.moat.com/invalid-traffic-webinar.html.