Publishing System Helps Writers Determine Audience Interest | Adweek Publishing System Helps Writers Determine Audience Interest | Adweek
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Publishing System Helps Writers Determine Audience Interest

Motor Trend Magazine among InboundWriter's first clients

Photo: Getty Images

InboundWriter wants to help big publishers and niche bloggers figure out how much demand there is for their idea in the Web ecosystem before they type a word or hit "publish." At least that's the idea behind the newest version of the startup's software, which the company said will be used by Source Interlink Media brands including Motor Trend Magazine and which has garnered interest from major television networks.

The system considers the topic and the Web publication, and then scans the Web to see what has already been written about the topic and how well it has been "picked up"—whether it's been seen by thousands of readers via social media and search engines or only a scant few. It can also weigh how well the article is written for the Web and measures a few other factors such as if it's "evergreen" to ultimately determine if the post is worth the author's time, the San Mateo-based company told Adweek. It gives more leeway to writers for highly trafficked sites—in other words, their ideas don't have to be as original to be deemed publish-worthy by InboundWriter.

InboundWriter says that its research tool goes far beyond what writers can learn via a simple Google search and will detail what's wrong and what's right about their idea.

"A lot of people out there can tell you if your title is too long or [whatnot]," said Skip Bestoff, CEO of InboundWriter, which has been in the search-engine-optimization publishing game for two years before the upgrade today. "We know that a green piece of content will drive five to 10 times more organic page views."

It's no longer enough to know how well a post is written on a gut level or whether it contains the right SEO keywords, Bestoff contended.

"A variety of factors are going to determine the performance of a piece of content that are independent of how that post reads," he said. "Most people don't understand that's what's going on beneath the hood. And if they do understand it, they don't have actionable data. The tools and applications people use today are kind of like hammers. We are more like a nail-gun."

And while whether content performance can be predicted by algorithms still probably remains to be seen, Bestoff and his clients aim to find out.

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