The world of scholarly publishing is in the midst of a dramatic transformation. The traditional subscription-based business model has been upended by the rapid rise of open access and preprint servers, forcing publishers to drastically rethink their approach.
Where once publishers could rely on selling journal bundles to a limited number of academic institutions, now they need to market their services directly to millions of researchers around the world.
This massive shift to open access has completely changed the game.
No longer can publishers simply deal with a handful of librarians at 5,000 institutions.
Now they need deep insights into each of the world's researchers on an individual level, potentially tens of millions of people. Building these direct relationships through valuable experiences is the only way forward in an open access world.
At the same time, powerful aggregators like Google Scholar are monopolizing discovery and threatening the direct relationships between publishers and readers. These aggregators leverage their existing audiences and essentially tax publishers wanting access to those audiences.
Clearly, these are era-defining challenges that have disrupted the scholarly publishing landscape entirely. Publish or perish is now tempered by the looming threat of aggregators that sit between publishers and their audiences.
So what are forward-thinking publishers to do?
The answer lies in artificial intelligence
With their troves of data on articles, authors, institutions and readers, publishers are uniquely positioned to take advantage of what AI has to offer. Now more than ever they need the deep audience insights, predictive analytics and highly personalized experiences that AI can provide.
At SSP’s 2023 Innovation Showcase, Hum’s SVP of Business Development shared how Hum is poised to help forward-thinking publishers capitalize on AI to drive growth.
Alchemist, the AI engine at the heart of Hum, leverages language models to extract key insights from a publishers' corpus of content and first-party data about how readers are interacting with that content.
This includes automatically tagging content based on subtle themes and relationships within the full text of articles. AI-generated tags enable precise audience segmentation and highly accurate individual recommendations.
Alchemist is constantly building multi-dimensional maps of what pieces of content and individual readers are about, capturing the nuances of topics and interests in a way that goes far beyond looking for keyword matches.
This allows for incredibly precise semantic search. Publishers can instantly find potential reviewers or readers based on manuscript topics, even for emerging concepts that have scarcely appeared in the literature before. The AI understands content and audience interests at a conceptual level no metadata schema or lookup table could ever capture.
Hum’s Special Issues Generator takes things even further. By analyzing gaps in existing content and modeling audience engagement data, the generator can recommend new publication ideas tailored to current reader interests. No more guessing what topics are hot - let the AI crunch the data and tell you what to publish next!
AI relies on quality data to work its magic
Alchemist was trained to understand and learn from your corpus of content and billions of usage events from your readers. These tools finally allow publishers to act on their data, revolutionizing everything from author recruitment to personalized content alerts.
There is no doubt the future of scholarly publishing will be driven by data and AI. The era of "publish or perish" is evolving into "personalize or perish." Publishers who embrace AI early will gain a lasting competitive advantage, and with solutions like Alchemist, the future looks bright for publishers ready to adapt to the new data-driven reality.