OUP partnered with Hum to implement AI-driven, personalized call-for-papers campaigns. Utilizing Hum’s Alchemist Taxonomy & Tagging Engine, OUP analyzed reader behavior on their Oxford Academic platform to identify potential authors based on topical interests. Targeted pop-up messages were then displayed to these readers, inviting them to submit manuscripts, thereby delivering timely and relevant prompts to engaged audiences.
Benefits & outcomes
- The first campaign achieved nearly 5,600 impressions and approximately 260 clicks to the submissions page.
- The second campaign garnered nearly 65,000 impressions and over 1,300 clicks.
- Engagement rates were up to 50% higher than traditional email campaigns and surpassed benchmarks for paid and organic social posts.
- OUP expanded this approach across multiple journals and began applying similar strategies for peer reviewer recruitment.
Notes & quotes
“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 Ganley, Director of Marketing Strategy & Partners at OUP.
The campaigns “received significantly higher engagement rates than other marketing activities — in some cases 50% higher than email campaigns.”
OUP is “also adopting a similar model for recruiting expert peer reviewers, creating curated segments of scholars with deep topical expertise.”
Publisher / company: Oxford University Press (OUP)
Date of case study: 30/04/2024
Source: https://blog.hum.works/posts/case-study-oup-boosts-author-engagement