using predictive analytics to overcome enrollment management … · 2020-03-14 · predictive...

Using Predictive Analytics to Overcome Enrollment

Management Challenges

Alex HerbertSales Manager

Jon MacMillanSenior Data Analyst

February 26th, 2019

Agenda• Who We Are

• Enrollment Management Challenges

• Enrollment Modeling

• Retention Modeling

• Utilizing the Results

• Q & A

About Rapid Insight

Founded in 2002 and headquartered in Conway, NH

Predictive analytics and data preparation software company empowering professionals of all skill levels to turn raw data into actionable insights

Serving hundreds of customers worldwide, ranging from healthcare to higher education

The Veera platform enables users to easily build predictive models, perform advanced data analysis, and share insights

Code free (but code friendly)self-service analytics platform

Veera Construct enables everyone from citizen data scientists to PhD statisticians to turn any data into actionable information

Veera Predict enables users of any skill level to analyze data and build predictive models in a fraction of time required by other tools

Veera Bridge empowers organizations by democratizing data with its cloud-based collaboration platform

The Veera Platform

Using Predictive Analytics to Overcome Enrollment

Management Challenges

Enrollment Management/en•roll•ment man•age•ment/ [noun]

1. An organizational concept and a systematic set of activities designed to enable educational institutions to exert more influence over their student enrollments

Improve Yields

Grow Applicant Pool

Increase Net Revenue

Improve Retention

(Hossler & Bean, The Strategic Management of College Enrollments)

Enrollment Management ChallengesPercent Change From Previous Year, Enrollment by Sector

Term Enrollment Estimates Fall 2018 (https://nscresearchcenter.org)

Predictive Analytics in Enrollment Management

Enrollment Retention

Improve Yields

Grow Applicant Pool

IncreaseNet Revenue

Improve Retention

Predictive Analytics in Enrollment

•Qualify names

•Prioritize inquiry pool

•Optimize contact

•Run simulations

•Craft your class

• Lost applicants

Leads/Prospects

Inquiries

Applicants

Stealth Apps

Completed Apps

Admits

Enrolled

What is Predictive Analytics?= Enrolled

= Applied

Distance From Campus

Enrollment Likelihood

Common Enrollment Modeling Data Points

ConnectIntegrate data in any format, from virtually any source

PrepareCreate step-by-step processes using easy, drag-and-drop visual workflows with no coding required

AnalyzeBuild and schedule jobs to run automatically, or run on-demand analyses

ShareWrite back to databases, create and disseminate reports, publish dashboards to visual analytics tools such as Tableau, or output datasets for predictive modeling

Predictive Analytics in Retention• Early retention

• First year retention

• Term GPA

•Academic standing

•Graduation likelihood

•Persistence in major

Common Retention Modeling Data Points

Unique Retention Modeling Data Points

• Registration for classes

• Degree progress

• Academic marks

• Prerequisite course performance

• Final grades

• Interventions

• Campus engagement

• Survey data

• Organizational membership

• Student record hold

• Mid-term deficiency reports

• Low career clarity

Fully TransparentAll methods and calculations performed are easily viewable

Automated Data MiningIdentify statistically significant data variables without manual effort

Easy to UseNew users can build expert models within two hours of training

No Programming NeededUse simple pick lists instead of programming or custom command languages

Automated Modeling ProcessBrings complex data analyses and predictive model building to users of any skill level

Communicating Your ResultsStudent

IDEnrollment Probability

1115791 High

1112845 High

1119128 High

1112464 High

1122893 High

1107559 High

1115937 High

1104038 Medium

1122038 Medium

1122876 Medium

1108547 Medium

1111941 Medium

1113438 Medium

1109982 Medium

1117425 Low

1106681 Low

1114973 Low

1110257 Low

1111303 Low



1115791 94.88%

1112845 92.20%

1119128 89.78%

1112464 87.70%

1122893 84.81%

1107559 83.27%

1115937 81.22%

1104038 76.03%

1122038 75.94%

1122876 74.39%

1108547 68.10%

1111941 58.71%

1113438 57.24%

1109982 51.33%

1117425 37.33%

1106681 27.90%

1114973 14.71%

1110257 8.26%

1111303 5.38%


IDEnrollment Probability Variable1

Variable1 Contribution Variable2

Variable2 Contribution

1115791 94.88%DaysBetween 71.1 % (+) Distance 16.15 % (+)

1112845 92.20%Distance 63.7 % (+) DaysBetween 23.39 % (+)

1119128 89.78%Distance 65.04 % (+) DaysBetween 21.77 % (+)

1112464 87.70%Distance 71.97 % (+) AppliedForFA 9.75 % (+)

1122893 84.81%MediaStudies 35.88 % (+) Distance 33.72 % (+)

1107559 83.27%MediaStudies 39.2 % (+) Distance 36.83 % (+)

1115937 81.22%SAT 30.2 % (+) Distance 21.84 % (+)

1104038 76.03%SAT 36.67 % (+) Distance 25.56 % (+)

1122038 75.94%DaysBetween 34.03 % (+) Distance 21.86 % (+)

1122876 74.39%Distance 36.48 % (+) AppliedForFA 19.25 % (+)

1108547 68.10%AppliedForFA 37.18 % (-) Distance 18.15 % (+)

1111941 58.71%SAT 36.89 % (-) AppliedForFA 32.67 % (+)

1113438 57.24%Distance 47.31 % (-) AppliedForFA 20.64 % (+)

1109982 51.33%SAT 23.48 % (-) Distance 20.79 % (-)

1117425 37.33%Distance 40.15 % (-) HS% 23.39 % (-)

1106681 27.90%Distance 58.44 % (-) EFC 11.87 % (-)

1114973 14.71%Distance 39.12 % (-) SAT 22.01 % (-)

1110257 8.26%Distance 70.42 % (-) EFC 7.86 % (-)

1111303 5.38%Distance 81.51 % (-) Undeclared 5.24 % (-)



Explanation

1115791 94.88%This student applied later in the admissions cycle which increases their enrollment likelihood

1112845 92.20%This student lives near campus, which increases their enrollment likelihood



1122893 84.81%This student is not a Media Studies major, which increases their enrollment likelihood

1107559 83.27%This student is not a Media Studies major, which increases their enrollment likelihood

1115937 81.22%This student has lower SAT scores than average, which increases their enrollment likelihood

1104038 76.03%This student has lower SAT scores than average, which increases their enrollment likelihood

1122038 75.94%This student applied later in the admissions cycle which increases their enrollment likelihood


1108547 68.10%This student did not apply for FA which decreases their enrollment likelihood

1111941 58.71%This student has higher SAT scores than average, which decreases their enrollment likelihood

1113438 57.24%This student lives farther from campus, which decreases their enrollment likelihood

1109982 51.33%This student has higher SAT scores than average, which decreases their enrollment likelihood






Total Predicted Enrollment

Student Type # of Students Predicted Yield Predicted Total

Admitted Applicants 4,207 46.23% 1,945

Continuing Students 4,256 78.64% 3,347

Total Enrollment 8,463 62.53% 5,292

Predictive Analytics in Practice

Enrollment Retention

• Optimized out-of-state recruitment

• Renewed focus on in-state recruitment

• Targeted faculty phone-a-thons

• Created buy-in

• Identified most at-risk students

• Faculty outreach and notification system

Results

• 5.7% increase in applicants

• 6.3% increase in confirms

• 4.5% increase in retention

+ =

Personalized Demo

data prepreporting

predictive modelingadvanced analytics

cloud-based reportingdata exploration

To schedule your personalized demo contact [email protected]

Questions?

Thank You!

For more information, visit www.rapidinsight.com

[email protected]@rapidinsight.com

[email protected]

using predictive analytics to overcome enrollment management … · 2020-03-14 · predictive...

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