Is the content discovery network really valuable?

Is the content discovery network of value?

Content discovery networks inform much of what people view online and can be a great way to improve the virility of your content. They range from the oft-complained about click bait to well thought-out content that provides people with further quality links that are of interest to them.

What is content discovery?

Often called native ads or advertorials (in print), content discovery is a network of articles that are promoted at the bottom of another article. These are often relevant or related to the first one, but don’t belong to the website they’re posted on. Instead, they’re third-party links that aim to whisk you away to another website, possibly owned by a company or brand.

Some of this content appears because it’s popular, but just as much is there because of sponsorship and advertising.

When you click on the sponsored links, you help the website owner earn money, which makes the concept very popular. After all, the articles look like they belong on the website, so it’s a very subtle way to advertise and make some extra money.

The platform is a great way to promote your brand through content. The Content Discovery Network is prominent on many websites, especially news outlets, and receives millions of clicks every month—with a clear distinction between clicks on relevant content and click bait.

How content discovery works

The network uses user meta data and a search algorithm to help recommend appropriate content, so that any leads coming through the network to your website are already highly qualified. These visitors are in the market for this information and if you provide high-quality content, your chances of converting them into a customer is much higher.

But the content discovery network goes far beyond just online content for users. For example, Netflix makes recommendations to you based on what you and other users similar to you choose to watch; a group of academics leveraged the algorithm to sort through the mess of data to ensure they were only referred to papers that were relevant to their own research.