How North Labs helped a higher education institution deploy predictive enrollment analytics and natural language querying capabilities in just 90 days—giving leadership instant access to the insights that drive student success.
"Our enrollment team finally has predictive visibility. We went from guessing to knowing—and we got there in a single quarter."
— VP of Enrollment Management, Higher Education Institution
A mid-size higher education institution was facing enrollment volatility and lacked the data infrastructure to forecast trends, identify at-risk students, or understand the drivers behind application-to-enrollment conversion. Leadership relied on static reports that were weeks old by the time they reached decision-makers.
The enrollment management and institutional research teams had access to rich data across admissions, financial aid, student success, and retention systems—but no way to query it in real time or surface predictive insights without submitting formal requests to an overburdened IT department.
The institution needed to go from reactive, report-driven enrollment management to a predictive, self-service analytics model—and they needed it fast, before the next enrollment cycle.
From kickoff to production deployment of natural language querying and predictive models in a single academic quarter.
Machine learning models identifying at-risk students and forecasting enrollment trends with actionable lead time for intervention.
Non-technical staff can now ask questions of their data in plain English and receive instant, accurate answers.
North Labs built a unified data layer connecting the institution's admissions, financial aid, student success, and retention systems. On top of this foundation, two key capabilities were deployed: predictive enrollment models and a natural language querying interface.
The predictive models analyze historical enrollment patterns, demographic trends, financial aid acceptance rates, and student engagement signals to forecast enrollment outcomes and flag at-risk applicants. The natural language querying layer allows enrollment managers and institutional leaders to ask questions like "How many first-generation students from the Midwest applied this month?" and get instant answers—no SQL required.
The entire solution was deployed in 90 days, timed to be production-ready before the institution's critical enrollment period.
The institution now operates with a predictive enrollment platform that gives leadership real-time visibility into student pipeline health, conversion probability, and retention risk. Natural language querying has democratized data access, reducing dependency on IT and empowering front-line staff to make data-informed decisions.
The engagement continues to expand into student success analytics, financial aid optimization, and alumni engagement—building on the data foundation established in the initial 90-day sprint.
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