Media companies are being held hostage by Google ― but it doesn't have to be that way, according to Dennis Mortensen, CEO and founder of Visual Revenue. The newly launched Front Page Automation Platform predicts and recommends content performance on the homepage, claiming to give front-page traffic an almost 30 percent lift.
Mortensen, an entrepreneur and former COO of IndexTools (now Yahoo! Web Analytics), hypothesized that media companies were putting too much emphasis on producing content for search, which is especially ineffective to maximize traffic for new content. Most traffic for the clients they work with comes through the front page (60 to 95 percent), making it untapped inventory publishers could better optimize and monetize, he said in an interview.
The new platform is most useful for news-oriented, larger publishers, from blogs to newspapers. Visual Revenue has tested the product
with a handful of clients (including the New York Daily News
), all of which agreed to follow the recommendation engine for story placement.
Mortensen said the platform results in a 29 percent lift in front-page traffic. On a micro scale, that means the average reader visiting a front page went from reading three articles to four articles. In monetary terms, a media property with about 200 million page views per month could see a revenue opportunity of more than $1 million a year, according to Mortensen (assuming a “revenue per thousand
” or eCPM of $6.75).
The platform works by tagging and crawling pages on the publisher's site and using semantic analysis to produce a predictive analysis of the content. Mortensen said the platform can look into the near future to determine how every article on the front page will perform, and therefore recommend the best front page at any given time.
Editors + automation = better traffic?
Editors can already predict that putting a story on the top of the page will generate more traffic for that story ― but Mortensen argues technology can make them more efficient. For instance, he said there might be a story “below the fold” that could generate more traffic if moved up a couple slots. “We can spot those low performers and start promoting them,” he said.
For instance, you know that Lindsay Lohan story generating a million pageviews? Maybe it should be generating 2 million. Mortensen said the predictive analysis can help publishers figure out how to take advantage of a story while it's still gaining momentum. He elaborates in a blog post
: "When you use traditional web analytics type reporting, you could easily end up generating a self-fulfilling prophecy, where the most popular articles are the most popular articles because they are in the most prominent positions."
So what happens to the “front page editors” big media companies are investing in; is this another technology that could make editors irrelevant? Not necessarily, but editorial roles could change with new tools. Similar to curation and aggregation platforms
, the software can work in conjunction with editors. As Mortensen explained to me, should highly paid editors really be spending so much time trying to guess what to put on the top slot of the homepage?
The concept behind Visual Revenue could even appease the journalism enthusiasts annoyed by content catering to search engines
. Mortensen said content optimization software promoting content can free up staff to focus on producing good content (rather than producing content for Google).
He said they've yet to see how publishers will use the platform, incorporating automation in some places and editorial judgment in others. Publishers could also use the software to meet revenue objectives beyond increasing pageviews, Mortensen noted. For instance, a publisher could optimize content to drive subscriptions.
Ready or not, the ol' automation-versus-curation debate will continue to be on the minds of media companies as algorithms become normal editorial tools. Mortensen envisions expanding to other channels next. For example, what if editors could have scientific tools to tell them what and how often to tweet? “You don't see too much science applied to content,” he said. That's certainly changing.