Publishers in 2022 are evolving their approach to content strategy. This new outlook encompasses both how publishers approach new content development and dissemination, and also how they analyze and leverage the connections between pieces in their existing collections.
In our 2022 Future of Publishing Survey, Silverchair Director of Product Strategy, Hannah Heckner, commented on this shifting focus towards developing richer content acumen:
“...The connection between pieces of content is the new 'content' – identifying relationships between research artifacts and related research seems to be an area of growing emphasis with publishing organizations. This is a great opportunity to showcase the impact of research/a research organization/a publishing organization/their underlying technology provider.”
In order to develop the types of content relationships Heckner references, publishers must cultivate a holistic understanding of their entire body of content. Gaining a unified understanding of an entire content ecosystem allows publishers to develop new products, including those based around topical content collectives, as Heckner suggests. Publishers can also use this intelligence to more efficiently launch monetized content initiatives and better connect readers with the exact content they want and need.
To do this, publishers need content tracking that goes beyond read rates and top-level analytics. Publishers need to be able to identify and track key themes, topics, taxonomies, keywords, and engagement on an individual and collective basis. They must also have a way to analyze and classify the meaning behind each piece of content, so these connections can be made.
But this is not a task that could be done manually. In 2022, there’s a better way. Unsurprisingly, it involves artificial intelligence (AI).
Natural Language Processing for Publishers
Publishers can use automated Natural Language Processing (NLP) to get the unified view of their content required to understand and leverage content relationships.
NLP is a type of AI that analyzes patterns of human language (in this case, in research artifacts and/or other written or transcribed content) to identify key themes and classify large data sets. This highly technical work sits at the intersection of computer science and linguistics. Such projects used to require a team of in-house data scientists, but self-serve technology is reaching the point where this advanced data analysis is available to anyone.
How Hum’s Customer Data Platform Uses NLP
Hum’s Customer Data Platform (CDP) uses NLP to help our clients develop clean content tagging and gain a holistic understanding of their entire body of content, including journal articles, videos, marketing content, and more. NLP informs deep insights on each and every piece of content in our publishing clients’ ecosystem, helping them see the gaps, connections, and areas of opportunity in their content strategy.
Watch this one minute video to learn more about Hum’s full suite of content intelligence features for publishers:
Hum doesn’t just track read depth, popular topics, and reader engagement in real-time; Hum makes it easy to see how and where your key segments are responding to individual pieces and entire categories of content.
With a 360° view of how your content is performing, you can monetize & develop more of the content that drives results, and deliver a personalized experience that keeps readers engaged and coming back for more.
Top Publishing Use Cases for Customer Data Platforms
Interested in digging deeper? Explore our top use cases for publishers to discover how content intelligence leads to better publisher growth outcomes.