Content marketing can't be measured on the last-click: Outbrain CEO

CEO and founder of the content discovery platform says marketers are too focused on Google and search when it comes to measuring success and need to broaden their scope

Marketers are too focused on search and last-click attribution and need to look further afield if they want to better understand consumer engagement and content’s effectiveness.

That’s the view of Outbrain CEO, Yaron Galai, who spoke with CMO during a recent press event to discuss the content discovery platform’s work with publishers and brands to date, plans for the future, and the importance of content in a modern marketing play.

According to Galai, one big challenge is getting the brands to produce the right creative content in the first place. The other, arguably bigger hurdle, is overcoming preconceptions about how to measure the impact and effectiveness of content and advertising activity.

Galai claimed marketing ROI today is highly skewed to the short-term and last-click attribution, a trend that’s overwhelmingly giving the credit to Google and search engines. As a result, marketers are failing to recognise the longer-term value and impact engaging with content has in the sales process, he said.

“Google takes harvested intent and then take 100 per cent of the credit,” he commented. “That intent is being created elsewhere.

“The challenge is showing that search isn’t the important thing.”

While he expected the industry’s views on attribution would eventually mature, Galai saw one of Outbrain’s roles as helping educate brands that search isn’t the most important thing, and that the impact of content discovery with a consumer is a long-term but ultimately more rewarding game.

“Marketers are looking at immediate conversion metrics, but it’s the stuff that’s not measurable that’s often most effective,” he said. “The proxies for immediate conversion are very powerful, but one of the questions I ask brands is: ‘What’s the cookie time they have for conversions, two days or 12 months?’ It takes time to build awareness, but a lot of measurement is focused on last-click attribution.

“We need to go higher in the funnel.”

Galai also had a piece of advice for marketers trying to come to terms with the effectiveness of their own content marketing efforts: “Be self-experential”.

“The smart players are focusing on the flow of where a consumer’s attention is being paid,” he commented. “You need long-term discipline.”

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Outbrain was established in 2006 by Galai, who previously founded AOL-acquired performance marketing vendor, Quigo, and now has an installed base of more than 100,000 sites worldwide. Its content distribution platform delivers 150 billion recommendations per month to 500,000 consumers using a combination of organic content and third-party links.

External links are delivered on a CPC model, with revenue split between Outbrain and the publishing sites where those links appear.

While its original client base was publishers, the company has seen a host of brands utilising its platform thanks to the rise of content marketing, and now claims about 40 per cent of total customers are brands. These include GE and Ford.

Galai said the next step is giving clients more data on the types of people interacting with their content. To do this, the company has struck a partnership with Exelate to integrate the DMP’s audience data with Outbrain in order to provide more information on the audiences clicking on recommended content.

Galai told CMO Outbrain has also started testing adding conversion intelligence into the consideration set of its algorithms.

“At the moment, we link to engagement, but that’s not necessarily the right metric for all,” he said. By linking Outbrain’s data with a site’s registration system, and feeding that into the algorithm, brands and publishers would be able to see who registered as a result of seeing the content, and the path that led them there, he said.

Galai explained Outbrain’s model consists of 50 algorithms looking at what might be interesting and relevant to a particular individual (represented through cookie data). Each time, 10 of these engines run in competition for that cookie data and come up with a recommendation and competence score for serving up certain content, including text and multimedia.

The winning algorithm delivers the content, much the same way collaborative filtering is used in Amazon’s recommendation engine.

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