In this second installment of our three-part enrollment series, which we began by talking about recruitment with John Buckley, we sit down with industry expert Donald Saleh to learn how he balances data and experience to manage variable admission goals.

HAI: Thanks for joining us, Don. Would you start by giving us a brief professional bio?

DS: I’m glad to. I served as Director of Financial Aid and then Dean of Admissions and Financial Aid at Cornell for 18 years, then VP of Enrollment Management at Syracuse University for nearly 14 years. Since then, I’ve gone on to work as a consultant for many institutions including The New School, Bryn Mawr College, Le Moyne College, and The Geffen School of Medicine at UCLA. And I’m currently serving as the interim VP for Enrollment Management at Franklin & Marshall College.

HAI: What are some of the biggest challenges you face in meeting enrollment goals?

DS: One is that we need to constantly remind ourselves that our goals have to be a bit fluid. You can’t go in saying that exactly this % of class will be international, or that % will be from a certain area. If the discount rate is a number you have to strictly adhere to, that ties your hands. There are many factions on campus who have an interest in the outcomes of enrolling the class – what percent are legacy, underrepresented, etc. You can lay out your plan and your goals, but you have to roll with the punches as applications come in and decisions are being made.

Another big challenge is managing the discount rate (DR). It’s become a major focus of what we do. The pressure to increase the DR is often greater than the budget allows.

HAI: How do you deal with those challenges?

DS: Well, most importantly, you need to keep your president informed as trends start to emerge within the application or admit pool. You never want to walk in three days before letters go out and say, “So, here’s what it looks like.“

You also need to be prepared to make compromises as to whom you admit as you go through the process, so that you keep the DR as close to budget as possible.

HAI: You’ve worked at several different institutions. Do you find that schools have common pain points in enrolling the class, or that each school is unique in circumstances and/or challenges?

DS: There are some common pain points, although they differ in scope from one institution to the next. For starters, every institution that I’ve worked with has had a need to grow in new geographic markets. This is constantly a focus of planning, and it translates back to how we look at applications during the admission process. At some institutions, this meant trying to grow the number of applicants we attracted from outside of the state. At others, it was “how do we grow in California?” or “how do we build diversity in our international pool?”

In addition, every institution has had to pay a lot of attention to what used to be referred to as the financial aid budget—but now is just commonly thought of as the discount rate and net revenue. I’ve worked at schools with very large endowments, and schools with relatively small ones. At some, you could manage the discount rate as needed and still hit your targets. At others, we had to let the DR rise to meet targets.

One thing that has differed a lot from school to school are admit rates. Some that I’ve worked with were selecting 20% of applications for admission, and others were selecting 85%. For the latter, it was a matter of “can we hit the enrollment goal?” If a school with a 20% admit rate misses an enrollment target, they have done something very wrong.

HAI: How important is it to you to be data-driven?

DS: You can’t do this job without being data-driven. There is no part of the job that has to do with planning, evaluation of your work, engaging in the admission of students and the awarding of financial aid that shouldn’t be informed by facts. If you aren’t constantly asking yourself questions and digging deeper and deeper into the data, you’re not doing your job. There is so much information to digest and you have to constantly be asking 2^nd and 3^rd-order questions. Make sure you aren’t just asking the same questions every year. The nuances in the data change rapidly and if you aren’t constantly looking at things with a fresh set of eyes, you can be in trouble.

The important part of our jobs that is not data-driven is the role as supervisor, hiring staff, and evaluating staff’s work. Here, you have to be able to do that part of the job in a humanistic way. If you do that part poorly, it is just as bad as not being data-driven when it comes to bringing in the class.

HAI: When determining how many students to admit and predicting yield, what’s the right balance between relying on your expertise and relying solely on data-driven insights?

DS: The first thing that popped into my head is 80/20, data/expertise, although I don’t think you can really put a number on it. Data is going to drive much of what you do, but your experience in the field and at an institution are extremely important. You have to have confidence in the decisions you make because it can be a very stressful job. When I begin working with a new institution, I ask a lot of questions about how things work there. But, I also have a breadth of experience in the field, and that’s helpful wherever I go.

HAI: What advice do you have for a school that wants to introduce something for the first time (e.g., moving to the Common App), and therefore doesn’t have past behavior to help predict outcomes? How can they best prepare for the change?

DS: If you introduce a change that causes a big increase or decrease in your application numbers, the things you need to be looking for are the details in how the characteristics of the pool have changed or stayed the same. What are the indications of an applicant’s interest? What patterns have you seen in past data that can help anticipate the effect of the change (e.g., with a large increase in applicants – do they look as interested as past applicants, etc.)? Yes, you have to make some educated guesses, but having multivariate predictive models for every stage of the student lifecycle can guide you in the right direction when there are major changes.

Overall, I’ve found that it’s crucial to work with someone who has seen changes play out on other campuses and will have the ability to put what you’re seeing into context. That broad view—along with access to large third-party data sets and the creation of custom predictive models—is one of the big reasons I’ve partnered with the HAI.

HAI: Thanks so much, Don!

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