Sightline to enrollment
Recruiting and marketing teams at universities are inundated with lists of students, but have no real way to know if a student is or is not likely to enroll or even apply to their university, despite marketing efforts through personal recruitment and mailed marketing materials. Targeted toward eligible students applying to a university, the Sightline to Enrollment package uses data to determine the probability the student will enroll and succeed.
Our team of experts has developed predictive models that analyze high school performance, tuition, scholarships, expected family contribution, parents’ educational background, and student demographics from the application and FAFSA form with our own SightLine variables. These key enrollment factors are available to universities before a student even begins their first day of class and can help you to ensure your university is spending resources in marketing and recruitment toward the students most likely to attend. This application is ideal for marketing, recruiting, and outreach.
SightLine provided us with a comprehensive view of our future enrollment thanks to their predictive modeling. Their insights allowed us to confidently build our budget and allocate institutional aid with more efficiency and effectiveness.”
Sightline to Success
Student retention is one of the larger problems plaguing universities. University officials struggle to decide which students to reach out to, relying on traditional demographic data, or intervene too late on students who are already on academic probation. We identify the at-risk students earlier, so you can intervene sooner and with better success.
Sightline has created a predictive model that identifies an individual, at-risk student based on more than 50 factors, including academic performance, demographic background information, university engagement, student interventions, and SightLine internally created variables and mined data. Through the process, each student receives a probability of dropping out, re-enrolling, or graduating for the semester of interest. As part of the predictive modeling, we also identify key retention factors, and the top reasons students drop out, while offering guidance on touchpoints to add to increase student engagement and retention. We have conservatively identified $1.2M of opportunity if the university acts on our findings to retain these newly identified students.
As professionals in the field, we have gut instincts of what the correlating factors are that attribute to attrition. Sightline validates those gut instincts, giving an accuracy range much tighter than before.”
SightLine to Accurate Aid
If universities do not know which continuing undergraduate students are most likely to re-enroll at the university, then it is very difficult to create accurate financial aid budgets.
We use predictive models and simulations to create accurate financial aid budgets, up to a year in advance, focused on institutional funds for internal university management. Our tools allow us to forecast aid allocation to students predicted to drop out or continue on to graduate.
At Michigan Tech, we were able to reduce their annual financial aid budgeting error for continuing undergraduates from 8-10% annually to less than 3% on average, with more than six forecasts validated at this time.
SightLine helped free up money in our financial aid budget, opening money we used to hold in contingency and allowing us to apply it with a fair degree of confidence.”
SightLine to Financial Optimization
Financial aid officers around the world are forced to carefully manage expenses and protect revenue, while allocating money to attract and retain students. Our financial optimization tool helps to determine optimal scholarship allocation for incoming students, offering the right amount to students likely to attend and avoiding those less likely to enroll.
Michigan Tech came to SightLine to determine budget allocation, scholarship offerings, and alumni discounts using enrollment and retention models to simulate changing financial aid forecasts. Throughout the process, we simulated hundreds of scenarios to find the optimal allocation strategy. We used forecasted/simulated net tuition revenue, enrollment, and retention rates as some of the main metrics to determine the optimal allocations. Based on our findings, we conservatively expect an annual revenue increase of $300K from the incoming class alone. The result is equivalent to $1.2M in year 4 of this implemented change.
We knew we had the answers within our datasets, but didn’t know how to find them. Sightline helped us redirect resources so they have the most impact.”