Admissions Intelligence System for a Private University
Private liberal arts university, New England
01 / Challenge
A 5,200-student private university faced declining yield rates falling from 31% to 24% over four years, threatening class size and net tuition revenue targets. The admissions team had no predictive analytics capability and was operating on gut instinct and historical cohort comparisons.
02 / Approach
We built an admissions intelligence platform using 12 years of deidentified applicant and yield data to predict enrollment likelihood by applicant segment. The system generates personalized engagement recommendations for each admitted student and surfaces financial aid optimization scenarios showing the yield impact of specific award changes.
03 / Outcome
Yield rate improved from 24% to 29% in the first full application cycle using the system. The incoming class exceeded headcount targets for the first time in four years. Financial aid efficiency improved — the university achieved the same yield with 8% less discount rate than the prior year.
Representative case study illustrating common agentic-AI deployment patterns in Education; not a specific QuettaMinds client engagement.
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