Find out what 400,000 articles taught us about content engagement
The current content writing algorithms, produced by firms such as Narrative Science, are primarily Natural Language Generation tools that write content based on data inputs. They are not perfect but work well if you want to write content about movements in share prices. The algorithms are improving each week but the good news for content writers is that machines are not generating mainstream content that engages audiences. At least not yet.
For an algorithm to write engaging content, it would need to understand the DNA of successful content and the complex interaction of different content elements. This is something we explore in our latest research report with LinkedIn.
In our report we look at a number of content elements, and their relative importance in the success of content across different industries. For example:
At the most basic level, choosing the right topic can influence your success. At any one time there are certain in vogue topics that resonate with audiences. It may be a celebrity individual, a celebrity company or trending topics such as artificial intelligence or healthcare policies.
. Publisher content is often categorised into different types such as news articles, opinion pieces or explainer articles. In a business context you can extend these types or categories, for example ‘how to’ posts, research reports and case studies. Content on AI could take any of these formats, such as an opinion piece, news article, case study, explainer article or ‘how to’ post.
This is distinct from content type. You may have decided to write a story or case study on AI but the format could be a long form or short form article, a list post, a video, an infographic or a picture list post for example.
This may be a difficult one for algorithms. However, we can see from the data that the content author plays an important role in the success of content. Has the author built an audience that respects their work? And how influential are the followers of the author?
Before we get to the quality of content, will people click through to read it? Does the headline resonate with the target audience, does it provide a clear promise or provoke curiosity or generate particular emotions? What is the headline structure and does it use phrases and words that will engage the audience?
As we know the success of an article can depend on the site or domain it is published on. This article on our blog may generate far more engagement if it was published on say, the New York Times. This reflects not just the nature and size of their audience but also the authority, trust and respect the site has.
There are many elements which can determine the quality of the content. For example, is it written cogently and concisely? How well has it been researched? How far does it generate valuable insights; how well does it use illustrations and charts, and many more.
The relative importance of the different content elements varies from industry to industry. Our research with LinkedIn looks specifically at the differences across ten industries. Here are just a few examples:
The simple point is that an effective content writing algorithm would need to understand the DNA of successful content for a specific context and audience, and adapt the content it produces accordingly.
This is equally true of human content writers. The more you understand the DNA of successful content in your industry the more you will have a clear picture of the content you should be aspiring to create. Algorithms may be many years away from being able to generate content which is not data driven, such as opinion content, but as writers, we still need to do more to understand our audience. Analysing data is one way we can deepen our understanding.
There are increasingly large datasets available for us to learn from. For example, just from our BuzzSumo database we can pull hundreds of thousands of articles for a topic or an industry along with data such as the number of shares and links they received (plus who shared and who linked to the article). Other datasets will show you how the article ranks in Google or estimate the traffic the article gained. The key question is how quickly algorithms and human writers can learn from this data and adapt accordingly.
Our joint report with LinkedIn is a first step at using data to help us understand the DNA of successful content across different industries. We reviewed the 40,000 most shared posts in ten industries over the last year and analysed the elements that correlated with high levels of shares and links. While there is no simple formula for content success, the data demonstrates how important it is to adapt your content for your industry and your audience.
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