Using different machine learning algorithms, our predictive analytics solution provides an attrition risk score for every single student. Think of your personal crystal ball that identifies the particular students you should engage with.
Decrease the university dropout rate using artificial intelligence. From the integration of information from different sources we create a unique profile per student based on which it is possible to generate models of artificial intelligence that calculate the probability of a student's desertion at a specific time. With this analytical input, universities can design early retention strategies to decrease their dropout rate.
What is it?
It is a solution that predicts the risk that a student has of drop-out and also identifies the individual factors or causes of desertion.
How does it work?
- Predict if a student is going to defect
- Use of numerous mathematical techniques to choose the best
- Highly accurate prediction models
- Identify attrition risk factors
- Determine general causes of desertion
- Recognizes the individual factors of desertion
- Visualization of results in control panels
- Strategic control boards: causes of desertion
- Operational control boards: probability and individual factors of desertion
By implementing this solution, you will get an impact of 3-5% points on retention. So you would not reduce the incomes by avoiding this percentage of desertions.