Oxford University Press (OUP) is one of the world's most prestigious academic publishers, attracting and publishing groundbreaking research from leading scholars and scientists in more than 500 academic and research journals covering a broad range of subject areas. 

To continue-reaching the best authors and publishing the latest research in its journals, OUP wanted to bring a more personalized approach to its author marketing strategy. The goal was to leverage data and AI to more effectively promote calls for papers and build relationships with potential authors who may be a great fit.

"We're always taking steps to provide an exceptional author experience," said Erin Ganley, Director of Marketing Strategy & Partners at OUP. "That starts with taking a more personalized approach with calls for papers and being more intentional and meaningful in  our touchpoints with potential authors."

How They Did It: Delivering Highly Targeted, Contextually Relevant Campaigns

OUP launched two Live Engagement campaigns in Hum to advertise calls for papers for two prestigious biology journals from precisely targeted segments of potential author groups.

With each campaign, OUP wanted to reach specific audiences of readers who were highly engaged with related topics, based on their content consumption patterns, but had not yet visited the call for papers page.

OUP leveraged Hum's AI-powered audience segmentation capabilities to build out these targeted lists. Hum's Alchemist Taxonomy & Tagging Engine uses AI and natural language processing to analyze OUP content on their Oxford Academic platform and accurately apply topic tags. This provided a complete record of every reader's topical interests based on their activity.

As readers who qualified as part of these pre-built target audience segments visited relevant journal content on OUP's site, they received timely pop-up messages on the site inviting them to learn more about submitting a manuscript - delivering the right message to the right researchers at the exact right moment.

Results: Significantly Higher Engagement from Qualified Potential Authors

The contextual, personalized approach paid off. OUP's on-platform campaigns facilitated by Hum received significantly higher engagement rates than other marketing activities - in some cases 50% higher than email campaigns and well above benchmarks for paid and organic social posts and OUP banner advertisements.

Over two months, the first campaign served nearly 5,600 impressions and generated almost 260 clicks to the submissions page from potential authors.

The second campaign was even more successful, serving nearly 65,000 impressions and driving over 1,300 potential authors to click through and learn more about submitting their research.

"Being able to target readers based on proven topic interests has focused our marketing efforts for calls for papers and made it easier to reach the right researchers, at the right time, with the right message," Erin noted. "It's been encouraging to see how effective these campaigns were at surfacing interest from potential authors."

What’s Next?: Scaling Up Personalized Author & Reviewer Outreach

Given the promising results, OUP has expanded this data-driven audience targeting approach to calls for papers across many other journals in its portfolio, with more campaigns planned. As the program scales, OUP continues to fine-tune their campaign targeting using Hum's AI segmentation capabilities, in some cases generating over 10% engagement rates from precisely targeted author segments.

OUP is also adopting a similar model for recruiting expert peer reviewers, creating curated segments of scholars with deep topical expertise related to specific journals and effectively promoting reviewer opportunities to the most qualified academics.

Through continued collaboration with Hum, OUP plans to continue exploring innovative ways to use data and AI to drive meaningful touchpoints and personalized experiences for authors, reviewers, researchers, librarians, and readers. 


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