Any publisher will tell you how costly it can be to create innovative, attention-grabbing content that resonates with audiences and moves them to engage. But the stakes are even higher when it comes to distributing this content: failing to deliver content effectively can mean depriving it of the attention it deserves, undermining the utility of your initial content investment.

Distributing content is a critical step for publishers and media organizations of all types in delivering quality news and entertainment to audiences and driving revenue through advertising or subscriptions. Yet the channels most often used are noisy environments where user attention is limited and competition is boundless. When not approached strategically, using data as a guide, content distribution can be a laborious, frustratingly inexact science with erratic results.

But publishers are increasingly adopting an effective solution: Artificial Intelligence (AI). Providing immense value in many operational aspects of the newsroom, AI is also transforming how publishers distribute content to maximize reach and performance. Below we’ll look at four key ways in which AI is driving this transformation.

1. Automate content delivery workflows to save time

Frequent and repetitive processes can often be broken down into discreet actions which can be automated to save time. 

Consider the example of social media publishing:

Despite the significant changes underway at social media platforms such as Facebook and Twitter in recent times, social media is still one of the preeminent means for publishers to distribute content to audiences, offering an efficient way to reach billions of people of all ages across the globe. 

Publishing content to different social platforms is a manual, time-consuming process. It involves selecting the best content to share, creating posts, writing adapted share messages, selecting hashtags, choosing images, analyzing performance and extracting insights for your publication’s social media content strategy. And, of course, all this should be done with specific social platforms in mind (Facebook, Twitter, Instagram, and a growing list of newer platforms). By applying AI, publishers are able to automate this entire process, saving immense time while ensuring content is optimally delivered to audiences across social media platforms. 

Another increasingly common example is with email newsletters, a burgeoning area of investment for many publishers. Where applications of AI were previously restricted to send time recommendations, AI is now transforming the entire process of delivering content via email by fully automating the creation, sending and optimization of emails. Publishers are now employing AI to automatically curate, build, send, test and optimize their newsletters, all without requiring human input. The time gained from this level of automation is evident, and can be reinvested into the creation of new, engaging content.  

2. Optimize content performance with powerful machine learning

Determining which content will perform best at any given moment, in any given channel, is something that arguably exceeds human ability. But unprecedentedly powerful algorithms can now integrate audience data and real-time trends. This offers the ability to pinpoint which content will attract attention and engagement, and the best time to publish to capture this attention.

By applying AI in this way across key channels, publishers are maximizing their content’s performance: 

• Machine learning algorithms deliver newsletters with personalized content, sent at optimal times determined by machine learning, to achieve higher open rates and click rates.
• AI determines which content is most likely to go viral on social media, and determines the precise optimal post time to gain higher visibility and more user engagement.
• Machine learning systems identify which creative elements are likely to generate the most advertising clicks, and tailor ads to viewers on the fly.
• AI understands visitor behaviors and personalizes website content for a tailored user experience. 

There are many other examples of how AI is transforming the content distribution strategies of publishers and media groups, with exciting new applications of AI surfacing every day.

3. Automatically maximize your content’s lifetime value

A key aspect of any content distribution strategy is knowing when, on which channels, and how often to redistribute existing content. In addition to planning and managing the distribution of new pieces, publishers must also constantly think about opportunities to recycle and redeploy content from their archives, whether these are evergreen pieces or seasonal features that can be reused each year. Republishing content in this way is an effective strategy for maximizing the value it can drive across its lifetime. 

Publishers can use AI to fully automate this republishing process and ensure that relevant content is reused at the opportune time. One example is the automated resharing of content on social media: AI algorithms monitor current social audience data and trends on each platform, then check a publisher’s content archives to determine which existing pieces should be republished, and the precise moment to generate a new wave of traffic and engagement. Leveraging AI to effectively and automatically manage content redistribution can help publishers squeeze the most return from content over its entire lifespan.

4. Test and learn with AI-driven insights  

Running tests is a critical practice to optimize performance over time. But planning and executing tests, collecting and cleaning data, and analyzing results, then transforming them into action has traditionally been laborious. Publishers are now using AI to gain efficiencies with the testing process on various content distribution channels. 

Email newsletters are a prime example, given that they have multiple elements available to test such as layout, font, colors, image size, content order, subject line and more. Implementing tests on each email blast, then collating data and identifying trends and larger patterns can be expertly handled by a machine. And in most use cases, AI can be employed to not only collect and analyze test data, but also to automatically iterate and implement improvements based on test results, ensuring content achieves continuous increases in performance.

Unlock content’s full potential with AI-powered delivery

Distribution is a decisive stage of the content life cycle, with the potential to make or break the performance of a piece of journalism. Getting distribution right – and across an increasing number of channels – is time-consuming and requires data and strategy to execute properly. 

Fortunately, with AI technology, publishers and media organizations can bypass much of the cumbersome, manual work involved in delivering content at scale. Adopting AI for content distribution can mean more accuracy and consistency than a human can provide, and this enhanced intelligence can have a significant impact on your  publication’s bottom line.

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About the author

Ashley Kibler is the Marketing Director at Echobox, the leading solution for publishing automation used by 1,500 publishers and media groups worldwide to automate and optimize content curation and distribution.