McGraw-Hill needed to re-evaluate its instructor segmentation in light of post-COVID education changes and a rapidly evolving higher-education landscape. The company needed to understand how different instructor types were using digital course materials — and where the greatest opportunities lay for improving adoption and outcomes.
Vivaldi led a three-phase approach including qualitative research, quantitative segmentation, and The Opportunity prioritization — developing six distinct instructor personas and identifying the digital platforms and learning experiences that would drive the highest growth and adoption in each segment.
The six instructor personas gave McGraw-Hill a precise map of its most important audience segments — enabling the company to prioritize digital platform investments with confidence and develop more targeted marketing and sales strategies for each persona type.
McGraw-Hill returned to Vivaldi to re-evaluate instructor segmentation in light of post-COVID changes in higher education and the rapid evolution of digital course materials. Vivaldi ran a three-phase approach across qualitative research, quantitative segmentation, and opportunity prioritization, producing six distinct instructor personas and identifying the digital platforms and learning experiences that would drive the highest growth and adoption in each.
McGraw-Hill is a global learning company founded in 1888 in New York City, serving millions of students, educators, and professionals across K–12, higher education, and professional learning markets. The company has invested heavily in its transition to a digital-first model, building data-driven products designed to personalize the learning experience at scale.
Vivaldi delivered six distinct instructor personas alongside prioritized digital platform investments aligned to each persona type, giving McGraw-Hill a precise map of its most important audience segments. The output enabled the company to direct platform investment with confidence and develop targeted sales and marketing strategies for each instructor type.
Phase one was qualitative research to surface the behavior patterns and motivations that shape how instructors actually use digital materials, phase two was quantitative segmentation to test and size those patterns at scale, and phase three was opportunity prioritization that mapped each persona to specific platform investments. Running the phases in that order is what kept the segmentation grounded in real behavior rather than imposed taxonomy.
Instructor behavior shifted substantially during and after the pandemic, in course design, in technology adoption, and in what they expect from publishers, and the shifts have not reverted to the pre-2020 baseline. Segmentation built on legacy data systematically misclassifies the cohort, which means platform investments built on that data tend to target audience patterns that no longer exist.
The lesson is that segmentation in education has to be redone whenever the underlying delivery model changes meaningfully, because the personas that worked under one model do not necessarily survive under the next. Companies that treat segmentation as a one-time exercise tend to anchor their roadmap to obsolete personas; the discipline of refreshing the work when the context shifts is what keeps digital platform investment aimed at where adoption actually moves.
