10 Tips for Tackling Predictive Modeling in Enrollment

Photo Courtesy Wikimedia Commons, Klaus Peter

Photo Courtesy Wikimedia Commons, Klaus Peter

Over the years, we’ve helped many organizations bring predictive modeling in-house and have learned a lot along the way. Below is a “best of” list of ten tips that our customers helped us put together to make the modeling process – from idea through execution – go a bit more 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 key 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 fully 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 that has an extremely 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, a majority of them have 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, in reference to 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 making sure your dataset is “clean”. If possible, create a data preparation process that is repeatable, which will ensure that your time is well-spent. It’s worth the investment. Data preparation and blending products need to be simple, like Rapid Insight Veera Construct. Veera Construct makes working with data much easier, much faster, and way more fun! You’ll be a Data Superhero in no time!

4. Avoid common mistakes.

Talk to anyone who’s worked with models to see what has worked for them, what hasn’t and what advice they have. Accurate models mean better results, which is incentive to avoid any of the “easy” mistakes. There are lots of reputable sources online to check out as well – on our site, I’d start here and here. Don’t forget that being able to create repeatable processes definitely eliminates 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 whether there are gaps that could be filled by collecting more or different data to impact your analyses going forward. Deciding on a product that automatically mines variables affords you the ability to throw in the kitchen sink and discover new insights with you data. Rapid Insight Anayltics software is great when it comes to getting you the answers you need faster with its automine feature.

6. Get creative

Especially in enrollment, there are plenty of ways to use the outputs of a predictive model. By summing the probabilities, you can get an idea of your incoming class size. By weighting financial aid with enrollment probabilities, you can get an idea of your expected financial aid outlay. By scoring your waitlist, you can decide which students would be most likely to accept an offer of admission – and have a better idea of how many to admit in order to fill necessary seats. Aggregating the results of your model can give you extra insight about specific sub-groups of students and how to best meet their needs.

7. Communicate with stakeholders often

Staying in touch with stakeholders works both ways – making sure that they are informed of your progress keeps everyone’s expectations on the same page, and 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 lots of cases, a dashboard or visualization will convey what you’ve learned better than a series of raw numbers. Again, this is when transparency really 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 important that you can own the decisions and the knowledge that went into the model. Additionally, make sure that you have the ability to change and update your models as needed without having to pay heavy fees. If you aren’t confident in your predictive modeling skillset, look for a knowledge partner who can teach you best practices and techniques. At the end of the day, 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 mode. Remember, flexibility, transparency, and ease of use are vital to successful predictive modeling and ultimately, data driven driven institutional decisions.

9. Measure your progress

As you’re implementing your model results, keep your KPI’s and the campus-wide goals in mind. Track the impact that your model is having. 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 got creative with their admissions process.

10. Reduce, reuse, recycle

The process of building a 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 gain an understanding of your data, historical trends, and an idea of what to expect going forward – all of which is valuable and not necessarily contained in a single model score.

Related: Attend an upcoming webinar Data Driven Enrollment Management Using Predictive Modeling