Omnicom First To Integrate TelevisaUnivision’s Hispanic HH Data Graph

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NEW YORK —  Omnicom Media Group (OMG) will be the first agency partner to leverage TelevisaUnivision‘s Hispanic household data graph for its brands — a move the Spanish-language media giant believes will expand “reach and resonance” with U.S. Hispanics.


Now covering nearly 100% of U.S. Hispanic Households, TelevisaUnivision’s propriety data graph will integrate with OMG’s identity solution, Omni ID, via privacy-oriented clean room technology to power its targeting, optimization, and measurement for “always on activation” across the entirety of its brands.

The announcement was Thursday (11/17) at TelevisaUnivision’s annual “Leading the Change” conference, a forum for marketers from various U.S. companies.

“At launch we were clear that TelevisaUnivision’s Hispanic household data graph was built for activation, and this partnership with OMG is a critical milestone underpinning our steadfast commitment to ensuring data is inclusive and representative of diverse audiences,” said TelevisaUnivision SVP of Data, Analytics and Advanced Advertising Dan Aversano. “By integrating TelevisaUnivision’s data graph into Omni ID, we’re confident that OMG’s vast roster of clients will be able to engage U.S. Hispanics in a more effective way that will drive meaningful business results and ROI.”

Kelly Metz, North America Managing Director, Advanced TV at Omnicom Media Group, added, “OMG saw a clear opportunity to leverage the great work TelevisaUnivision has done to improve representation and coverage of the US Hispanic community with their identity graph. Enabling this advanced identity solution via Omni ID translates to a powerful first-mover opportunity for Omnicom clients and another critical component of how OMG lays the foundation for a solid approach in manifesting diversity in the media process, work, and investment.”

TelevisaUnivision launched the Hispanic household data graph in May 2022 “to help solve for the inequities that cause U.S. Hispanics to be vastly underrepresented in data sets.”