As colleges and universities across the country collect their enrollment data for Fall 2018, many are seeing a continued decline in enrollment.
And this challenge isn’t new — last year, undergrad enrollment in the U.S. was down for the sixth straight year, according to numbers from the National Student Clearinghouse Research Center.
Without the ability to accurately predict enrollment numbers, administrators are left asking “why” without any easy answers or quick fixes.
It’s time to throw out the old playbook. But what should you use in its place? The answer isn’t some one-size-fits-all solution. In fact, for your institution to survive — and thrive — in this new world, your solution should be tailor-fit to your institution.
That’s where predictive analytics comes in.
What is Predictive Analytics?
Predictive analytics is a branch of advanced analytics that uses data, statistical algorithms, and machine learning to identify the likelihood of future events based on current and historical data. Simply put, predictive analytics can help you go beyond merely understanding what happened in the past to predict what will happen next.
Predict Next Year’s Enrollment Today
For administrators, poor decision-making based on gut feelings or incomplete data can lead to painful and costly layoffs. Rather than relying on intuition when making important decisions, top institutions are using statistics and machine learning to analyze demographic and performance data to predict whether a student will enroll at an institution, stay on track in their courses, or require support so that they do not fall behind.
Using this data, you can predict future enrollment numbers, understand what’s driving the drop in enrollment, and proactively tailor your advising services and personalize learning to improve student outcomes.
In a world where the future seems uncertain (at best), predictive analytics can help you know what will happen next so you can prepare. Because predictive analytics uses your own institution’s data, the models and resulting predictions are unique to your historical trends, and changes in your organizational climate.
Top Obstacles to Using Predictive Analytics
Though the benefits of using advanced analytics are clear, that doesn’t mean the road will be easy. Administrators looking to invest in predictive analytics will face challenges, including:
- Software Limitations: While many tools can help with data preparation, analysis, and access, you might find that one-size-fits-all solutions leave you with more questions than answers. They often do a good job of answering questions like “What happened?” and “Why did it happen?” but are unable to answer the most important question — “What will happen next?”
- Burden of Legacy Systems: Disparate systems lacking interoperability make it challenging to set up the necessary tooling to harvest data efficiently. If you fall into this category, it’s an excellent opportunity to create a roadmap for updating your legacy systems as part of your long-term strategic roadmap for adopting predictive analytics. It's worth thinking through whether a data warehouse solution would be right for your institution.
- Data Readiness Issues: Finding the data is rarely easy for institutions with various account structures and naming conventions. For predictive analytics (or any machine learning) to be effective, it’s essential to have accurate, complete, and properly labeled data. Don’t worry if a lot of your data is unstructured (text, images or audio). Thanks to advances in image recognition, natural language processing, and audio recognition, predictive analytics models can use unstructured data to inform decision-making.
Despite these obstacles, the risk of inaction is even more significant. In the next ten years, winners and losers will be determined by their ability to use data analytics to adapt to the changing industry.
If you’re unsure where to start or how to prepare, we can help. Our advanced analytics team can provide strategy, hands-on engineering, and machine learning expertise. In fact, we’ve built a tool to help a customer predict employee burnout three months before it happens, leading to a 950% decrease in voluntary turnover. Read their story.