CUSTOMER SPOTLIGHT
Nova Southeastern University uses AI to potentially improve first-time in college student retention by 17% in 15 days
Company Profile

Largest private, selective research university in Florida

Industry

Higher Education

Region

Southeast US

Challenge

Nova Southeastern University wanted to leverage their data assets to improve student retention and optimize student welfare, particularly aiming at students within their undergraduate program.

Solution

Aible helped identify students who were most likely to leave. This helped the center for academic and student achievement target and prioritize their retention efforts to the most at-risk students.

Outcome

Aible helped identify ways to potentially lower student attrition by 17%

"During a one hour meeting we went from a raw dataset, to exploring insights in the data automatically highlighted by Aible, to creating and even deploying a predictive model. The collaboration with academic and financial aid advisors helped us further optimize the models and made them more useful - but we went end-to-end from raw data to deployed model in such a short amount of time.”

- Don Rudawsky, VP Institutional Effectiveness, Nova Southeastern University

Use Case & Project Details

  • Use Case

    First-time in college student retention

  • Potential Project Results

    17% reduction in student attrition

  • Time from data provision to project completion:

    15 days

  • Elapsed time from start of model training to completion of 500+ models on serverless infrastructure

    6 projects trained in ~25 minutes per project

15_days_student_Retention-Oct-27-2022-07-23-21-2341-PM

Nova Southeastern University uses AI to potentially improve first-time in college student retention by 17% in 15 days

More Customer Success Stories

USE CASE: Onboarding salespeople and first order optimization

DURATION: 17 days

RESULTS: $10M revenue

USE CASE: Perishable goods overstock & wastage

DURATION: 27 days

RESULTS: Reduce overstock point of purchase wastage by over 10%

USE CASE: Late shipment prediction and expedite optimization

DURATION: 17 days

RESULTS: $4M+ cost reduction