Visual Revenue adds science to editorial decisions

Visual Revenue predicts the performance of stories on the front page in order to help editors make decisions about story placement. It can also operate independently on pages without editors, but founder Dennis Mortenson said most news organizations prefer to use it as a tool to inform decisions. He compares it to how the Bloomberg Terminal is used by stock traders. “We want to be that Bloomberg terminal of the newsroom,” he said in a recent interview.

 

The software provides real-time recommendations about which content to place in which position on the front page, based on the criteria of the media organization (such as increasing traffic or engagement or emphasizing a certain type of content). It also works for mobile and tablet editions and news feeds. 

 

 

One of the first news organizations to use the tools was New York Daily News. Web Editor Meena Hartenstein told me that while the tool might not be flawless, it helps her see how readers are responding to stories. For instance, a feature they put on the homepage in a lower position could receive a lot of audience interest, influencing the editors to move it up to a higher position. 

 

“We definitely don't take all of the recommendations or even the majority of recommendations but we're always keeping our eye on what people are interested in on our site,” she said. 

 

The home page for NYDailyNews.com is an important traffic portal for the publisher (unlike some sites that might get as much home page traffic). While Hartenstein wouldn't give out the specifics of how much traffic comes through the home page, she said “a significant amount of our readers find out content by coming directly to our home page.” 

 

A tool like Visual Revenue is another example of newsroom tools that help editors make decisions. Hartenstein emphasized that editors can't just chase clicks, but rather emphasize quality and use tools to enhance it. This is the opposite of how companies like Demand Media rely solely on algorithms to inform editorial. 

 

“There's definitely so much data than ever before,” Hartenstein said. “It's huge for us to use the data as humans and not just blindly follow what a machine is spitting out.”