Challenge
Students often struggle to find and access the right support services at the right time. Traditional outreach efforts were reactive—waiting for students to raise their hands rather than proactively identifying who needed help. The result? Disproportionate engagement with services, missed opportunities for early intervention, and inefficient use of support staff resources.
Strategy
To build a more responsive and proactive support system, we coordinated between three primary groups at Arizona State University: data specialists, service providers, and communicators.
We developed Signals—a data-driven outreach framework that integrates:
Big data insights to assess student risk levels (low, medium, high)
A segmented care model inspired by healthcare triage—matching students to the right level of support
A scalable communication strategy using personalized, targeted messaging via email and SMS
An example of the graphics framework used to coordinate across siloes, communicating our data-driven, segmented care framework
There’s a significant amount of planning and coordinating to do, which is where a lot of graphic charting happens.
Execution:
To make this complex system actionable and accessible, we:
Developed a Campaigns Manual—a structured framework aligning dozens of professionals across student services, data teams, and communications.
Designed graphic-driven, highly targeted outreach campaigns tailored to student risk levels.
Implemented a "Robots + Humans" approach—leveraging automation for scalable messaging while connecting high-need students to trained staff for personal outreach.
Impact
✔ 14,000 students engaged through targeted campaigns
✔ Increased service adoption across support programs
✔ Stronger coordination between data, service, and communications teams
✔ More equitable student support—ensuring proactive outreach rather than relying on self-selection
Sample email
Here’s one of many emails we sent, including stylized motion graphics and interactives, that we found helpd increase engagement (through A/.B testing).
These emails would help trigger additional support personalized to student behavior.