10 Tips for Tackling Predictive Modeling in EnrollmentReading time: 5 minutes
Over the years, we’ve helped many organizations bring predictive modeling for enrollment in-house and have learned a lot along the way. Below is a “best of” list of ten tips from our customers to make the modeling process—from idea through execution—go smoothly.
1. Establish buy-in before your first modeling project
Establishing leadership support before your project gets off the ground will ensure that your modeling efforts have a strong foundation and will not flounder as you encounter challenges along the way. Communication during the modeling process is critical, and this is the first step.
By openly communicating about your predictive modeling project from the start, you can be clear about expectations, outcomes, and the process before you begin.
Choosing a product that provides transparent results makes the buy-in process easier, allowing an organization to understand and capitalize on the results.
For more on transparency within your models, read our blog post on Ethics and Predictive Analytics in Higher Ed.
2. Pick the best person for the project
The person in charge of predictive modeling will have to collect, analyze, and work with the results from your data. This person should be a creative problem-solver who is willing to learn.
Ideally, this person is someone who is already working with the data. A person who understands your data – how it is stored, labeled, and used – can learn any statistical techniques they might need to know, but it is harder to learn the data while doing so.
In-house software with a user-friendly interface like Rapid Insight can make this process go even smoother, and you’ll have models up and running in no time.
Several Rapid Insight customers shared their own in-house experiences. Whereas many of them have had experience using consultants and building their own predictive models, most expressed an inclination toward in-house predictive modeling because of its flexible nature.
“With a little time and effort, institutions can build predictive models in-house that they know they can trust,” stated Tony Parandi, Director of Institutional Research at Indiana Wesleyan University, about his experience with Rapid Insight software.
3. Data preparation is 80% of the modeling process.
You may have heard of the term GIGO – garbage in, garbage out – which is a creative way of saying that a model is only as good as the data you use to create it.
That said, data preparation is crucial as you’re gearing up to build a model. This means creating new variables, checking for missing values, and ensuring your dataset is “clean.”
If possible, create a repeatable data preparation process, which will ensure that your time is well-spent. It’s worth the investment.
Data preparation and blending products need to be simple. Construct makes working with data much easier, faster, and more fun!
4. Avoid common mistakes
Accurate models mean better results, which is an incentive to avoid “easy” mistakes.
There are lots of reputable sources online to check out as well – on our site. I’d start here. Don’t forget that being able to create repeatable processes helps cut down on dirty data.
5. Incorporate as much data as possible
Gather as much data about your historical students as possible. In collecting data, strive for the most complete picture of your students that you can create from your data.
Depending on when you hope to use your models, this can include inquiry data, information from applications, social media data, and financial aid information. Once you know what information you have consistently been collecting about prospective students, you can evaluate gaps that you could fill by collecting more or different data to impact your analyses in the future.
Deciding on a product that automatically mines variables affords you the ability to throw in the kitchen sink and discover new insights with your data. Rapid Insight’s Predict is excellent at getting you the answers you need with its automine feature.
6. Get creative
With predictive modeling in enrollment, there are plenty of ways to use a model’s outputs.
Sum the probabilities to get an idea of your incoming class size.
Weight financial aid with enrollment probabilities for an idea of your expected financial aid outlay.
Score your waitlist and decide which students would be most likely to accept an offer of admission – and have a better idea of how many to admit to fill necessary seats.
Aggregate the results of your model for extra insight about specific sub-groups of students and how to meet their needs best.
7. Communicate with stakeholders often
Staying in touch with stakeholders works both ways. Briefing stakeholders on your progress keeps everyone’s expectations on the same page. Checking in with them keeps you engaged with the bigger picture and how modeling will support your school’s objectives.
When framing communication, keep your end audience in mind. In many cases, a dashboard or visualization will convey what you’ve learned better than a series of raw numbers.
Again, this is when transparency comes into play. The more you understand the model, the more communicable it is to stakeholders and higher-ups.
8. Choose the right vendor
When you are using the results of a predictive model to make key decisions within your institution, it’s crucial that you can own the decisions and the knowledge that went into the model.
Additionally, ensure that you can change and update your models as needed without paying hefty fees.
If you aren’t confident in your predictive modeling skillset, look for a knowledgeable vendor partner who can teach you best practices and techniques.
You’ll need to be confident that you can explain the model scores to your team and how you got from raw data to the decisions you’ve made.
Flexibility, transparency, and ease of use are vital to successful predictive modeling and, ultimately, data-informed institutional decisions.
9. Measure your progress
As you implement your model results, keep your KPI’s and the campus-wide goals in mind.
Track the impact that your model has. For example, if you’re using the model to decide which students to do personalized outreach, keep track of the responses and compare them to previous years.
Read the Clark University case study to see how they integrated modeling into their admissions process.
10. Reduce, reuse, recycle
The process of building a predictive model shouldn’t be a single-use effort.
Reuse the data cleansing and extracting processes for other analyses.
Reduce stress by automating reports whenever possible to keep everyone on the same page.
Finally, recycle the knowledge you gain through your institution. The value of a model isn’t limited to the scores. Throughout the process, you’ll understand your data, historical trends, and an idea of what to expect going forward. This is all valuable information not necessarily contained in a single model score.
We hope you gained some insight from these tips on predictive modeling in enrollment. If you’re interested in a tool that makes enrollment modeling fast and easy, click the button below to set up a free demo with our analysts!