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Measure Twice, Model Once: 3 Steps to Help Improve Student Enrollment Modeling

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Improve Student Enrollment Modeling

By Rapid Insight

If you’re thinking about building statistical models to predict your enrollment, you’ll need a few things before you get started. Based on feedback from our customers, I’ve boiled down these needs down to three steps to get your modeling efforts started on a firm foundation. Once you have gotten started, you are well on your way to helping support the efforts to improve student enrollment numbers.


Make sure to establish buy-in before getting started with a new predictive modeling project.  If predictive modeling is new to your environment, you might need to explain how it can help your institution reach enrollment goals and what the process will entail. When discussing the modeling project with leadership, be clear and upfront about expectations, outcomes, and the modeling process. Emphasize the benefits that predictive modeling will bring to your institution and how it will impact the bottom line as well as staff workload.

Open communication from the onset of a project helps to ensure that everyone is on the same page as you carry out your analysis and sets expectations accordingly. Working with leadership when setting goals can also help to ensure that the goals of the project will benefit the institution as a whole. When you’ve finished a modeling project, don’t forget to sell your success – it can be the first step to future projects and making your school more data-driven as a whole.

Specific goal(s)

The first step to starting a predictive modeling project is to establish your end goal. Try to envision where you’d like to end up after you’ve completed your modeling project and use that end point to formulate a specific goal to help get you there. Examples of possible end goals for an enrollment model include:

  • Reduce your prospect mailing budget
  • Increase accuracy of enrollment yield predictions
  • Meet diversity objectives

Establishing a goal ahead of time is important because it narrows the focus of your modeling efforts, and it will help you to be clear when you need to communicate your needs and goals with other people within your institution. Discussing your goal with others is also a good check of whether or not your goal will be achievable in the timeframe you have – which is worth considering. Using your goal, you can decide how exactly to set up your predictive model in order to reach it. Your goal will also directly impact how you create your y-variable and what information you may bring into a model.

Historical data

Building a predictive model generally requires multiple years of historical data. When planning ahead, consider how you might gain access to your data. This is trickier for some people than others, depending on whether or not you have access to your database or data source(s). If you have to go through IT to access your data, it helps to have a good idea of what fields or tables you’ll need so that you can communicate what data you want clearly.

It’s worth double-checking your goal against your data, once you’ve collected it, to make sure that what you’ve collected makes sense to support your modeling outcome. Sometimes you’ll find that you might need to tweak your goal or get more data after taking a look at the data you have available, and that’s okay – model building is often an iterative process.

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