Predictive Enrollment & Dropout Modeling

Use machine learning to identify at-risk students before they disengage and take action when it matters most.

What We Do

Our predictive modeling service helps institutions move from reactive intervention to proactive support. We build custom machine learning models (logistic regression, random forest, etc.)

Key Benefits

Up to 85% model accuracy using behavior, financial aid, and academic indicators
Real-time risk flags that plug directly into your CRM
12% average increase in enrollment achieved in previous implementations
Supports grant proposals, DEI initiatives, and smarter budget allocation

How It Works

We collect your historical student and enrollment data.
Our team builds and trains your model using Python, R, and AI libraries.
You receive a plug-and-play toolkit: predictive scores, CRM workflows, and dashboards.
We train your team on how to use it, adjust it, and scale it.

Real Impact

At Midwestern State University, our model identified Hispanic students likely to drop out based on incomplete applications and financial aid gaps. By connecting this to the CRM and launching tailored messaging, the university increased enrollment by 12% and tuition revenue by 7% in just one year.

Step 1

Data Collection & Assessment

We start by gathering key data from the institution's existing systems — including CRM, admissions records, financial aid files, academic performance, and communication logs.
We clean and standardize the data to ensure accuracy and consistency across sources.

Step 2

Model Development & Training

Using Python and tools like Scikit-learn or R, we build custom machine learning models such as logistic regression and random forest — to predict key outcomes like:

Likelihood to enroll

Risk of dropout

Financial aid dependency

We train and test the model to ensure it reaches high accuracy (typically 80–85%+).

Step 3

CRM Integration & Automation

We connect the predictive insights to the institution’s CRM (like HubSpot, Slate, or Salesforce). Students flagged as high-risk are automatically enrolled into targeted workflows:

Email and SMS reminders

Scholarship nudges

Advisor meeting requests
All bilingual, personalized, and timed to key decision points.