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How Big Data Analytics Can Save Publishing

Private Exchanges Are The First Step Toward Reclaiming The 'Premium' in Premium Publishing

By Published on . 2

Traditional newspaper and magazine publishers, responsible for most of the high-quality and original content we consume, have seen a huge decline in advertising revenues. While it's the easy and obvious call to support premium publishers as they point fingers and blame VCs for investing in disruptive buy side tech, I'm going to go out on a limb and say something blunt: Publishers, you deserve every bit of this.

Publishers have not generated much of the almost infinite supply of channel-choking inventory, but they have also done next to nothing to preserve what is good and proprietary and "premium" about their own inventory. In some cases, they have chosen lowest common denominator ad networks, exchanges and supply side platforms to do the hard work of selling.

Publishers of high-quality content with large, desirable audiences need to reclaim their online ads inventory. Only big data tools can dig them out of the undifferentiated, over-supplied, machine- driven nightmare of the sell side by enabling publishers to scalably and cost-effectively analyze, price and allocate inventory in the new environment.

And 2011 has seen hopeful signs as publishers such as International Data Group, Weather.com, Forbes, CBS, Conde Nast and quadrantONE formed private exchanges to reclaim their rightful place in the display advertising value chain. At quadrantOne, for example, a joint venture of Gannett, Hearst, Tribune Co., and The New York Times Co. that aggregates uncommitted inventory from more than 300 local newspaper sites, real-time bidding on their private exchange increases the ad unit rate by more than 50 percent vs. a typical blind transaction in a public marketplace such as RightMedia or Google AdEx. And like other private exchanges, quadrantONE can retain exclusive ownership of the data surrounding the transaction, all while generating more income for publisher partners.

As another example, The Financial Times, one of our global publisher customers, uses big data analytics to optimize pricing on ads by section, audience, targeting parameters, geography, and time of day. Our friends at the FT sell more inventory because the team knows what they have, where it is and how it should be priced to capture the opportunity at hand. To boot, analytics reveal previously undersold areas of the publication, enabling premium pricing and resulting in found margin falling straight to the bottom line.

The partnership Yahoo, AOL, and Microsoft announced last month could work if it proves to be reliant on analytics (and a single clearing channel) to optimize prices and deals on pooled inventory. If these three new co-selling partners manually aggregate deals, fail to do proper analytics, and don't engage in appropriate pricing and clearing strategies, then real-time bidding marketplaces are guaranteed to drive down the prices of this newly combined inventory to suicidal levels.

Other "bulge bracket" publishing industry peers such as Time Inc., Conde Nast, and Hearst, should have mechanisms in place to benefit from machine selling, data analytics, and price optimization. Should these large sellers harness the opportunity at hand by making needed investments in analytics infrastructure, they will be able to charge a premium for their product, as well as maintain more control over the type and quality of advertisers filling their pages both above and below the fold. Their direct sales teams will not be redundant but rather will be freed up to create unique advertorial and ecommerce marketing partnerships aimed at high-quality audiences that are both niche and scale.

In 2009 and 2010, data-driven trading disproportionately aided the buy side in global media market; 2011 saw things shifting back in the direction of more neutral ground. It's our firm belief that 2012 will see analytics swing momentum back in favor of the sellers.

ABOUT THE AUTHOR
David Soloff is CEO of Metamarkets, which provides predictive analytics for publishers.
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