In just a few months’ time, the pandemic sped the adoption of digital technologies by several years.
In Hum’s State of Personalization Report for Publishers, we explored the ways that constant connectivity and increased capability for digital connection have also changed the way consumers behave and consume content.
Paired with the growing importance of keeping your reader's eyes on your content in an OA environment - the necessity of meeting these rising expectations is more critical than ever. Publishers that aren’t able to transition from a one-size-fits-all approach to a personalized one will struggle to stay competitive.
One of the key technologies making waves for digital content organizations and driving this shift forward is AI and machine learning.
Artificial intelligence and machine learning refer to algorithms that are continually collecting and analyzing member behavior, recognizing patterns and predicting what a user’s next step might be.
Here are four key ways artificial intelligence is helping scholarly publishers be more efficient and deliver a better experience:
Automating manual tasks
One of the most common use cases for artificial intelligence across industries is the ability to automate manual, repetitive, or time-consuming processes, reducing the strain on your team’s time. Publishers can use AI to quickly identify papers that don’t meet journal standards and automate several of the tasks that are important in improving the quality of published papers.
There are emerging AI tools that can help publishers:
- Run copyright and plagiarism tests
- Flag inaccurate or incorrectly attributed data
- Conduct reference checks
- Determine the length of time required to read a paper
- Translate digital content into other languages
When implemented correctly, artificial intelligence augments your team’s ability to publish high quality papers, knocking out the manual and mundane tasks and giving your team greater bandwidth for decision-making.
Simplifying the peer review process
Artificial intelligence doesn’t just save time for publishers in-house; it can also add efficiencies and improve experience when it comes to peer review. When AI is used to validate statistics and summarize the conclusions of research articles, the entire peer review process takes less time.
Machine learning can even help scholarly publishers with the recruitment process by leveraging first-party data to identify top author or reviewer candidates, showing those with a higher likelihood to submit or review.
Tagging content
CueBERT - Hum’s content tagging tool, relies on natural language processing (NLP) to analyze and learn from your full content library, NLP is a form of AI that “reads” your content, identifying key themes, topics, taxonomies, and keywords.
Compared to author-supplied keywords, AI tagging is often more accurate, more objective, and more consistent.
An AI-generated taxonomy can feed content recommendation engines or advertising segments the best content for individual users based on their real interests, and give you more insight into which topics or keywords are driving success.
Recommending new content
Your readers - the younger ones, in particular - are increasingly plugged into platforms like TikTok, Netflix, and Amazon that deliver hyper-personalized recommendations of what to watch, read, or buy next.
Pair that with decreased attention spans and the constant barrage of media we filter through daily, and you’ll begin to understand why smart content delivery is setting great publishers apart from the rest. Your readers want personalized content - and they want to find it, fast.
In order to serve people the right content, publishers have to be in tune - not only with what readers have engaged with in the past - but in predicting what they’d like to see next.
Artificial intelligence and machine learning make it possible for publishers to personalize each reader’s journey by delivering content selected just for them, based on reading habits and preferences. (This, in turn, bolsters engagement, which drives reader retention, subscriptions, and advertising revenue)
Artificial intelligence has massive potential to personalize your readers’ experience and keep them engaged. As technology continues to evolve and publishing organizations seek more sophisticated ways of leveraging first-party data to build relationships with their readers, the opportunities to apply AI and machine learning will only expand.
Want to learn more about how leading scholarly publishers are using technology to tackle their biggest challenges? See how Hum clients are getting ahead!