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Dotdash Meredith Is Now Using D/Cipher in More Than 30% of Direct Buys



Publisher Dotdash Meredith is now using its contextual solution, D/Cipher, in more than 30% of its direct ad buys less than one year after launching the product, according to CEO Neil Vogel.

For those advertisers not yet willing to quit third-party cookies, Dotdash Meredith lets the advertiser see it outperform other solutions.

The publisher asks an advertiser to set aside 15% to 20% of a given campaign to run using D/Cipher, and if it fails to outperform the benchmark, the media company will rerun the campaign. So far, it has not had to make good on any guarantees, according to Vogel.

In one case study involving a skincare brand, D/Cipher drove 3.8 times more lift and four times the incremental sales of the parallel cookie-targeted campaign, according to the publisher. 

“We always knew intent targeting would outperform cookies,” Vogel said. “The world is finally moving to us.”

Publishers and marketers have been forced by the looming depreciation of third-party cookies to experiment with, and ultimately adopt, a variety of solutions to serve relevant ads and measure their efficacy in a privacy-compliant fashion. 

In response, many publishers have scrambled to erect registration walls or gather first-party data from users, as well as to experiment with solutions from Google’s Privacy Sandbox or vendors like The Trade Desk or LiveRamp. 

Contextual solutions have typically outperformed cookie- or ID-based targeting in terms of reaching the right person, but they have often lacked sufficient scale—and the ability to deploy across multiple separate environments—to make them worthwhile for marketers, according to an advertising technology consultant. 

Additionally, some ad-tech vendors offer contextual solutions by scraping publishers’ websites, making it challenging for publishers to convince advertisers to embrace their offerings. And a significant erosion to search-based traffic, such as the kind that generative artificial intelligence portends, also poses a significant risk to the model.

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