In a prior post, we presented reasons why institutions seek to recruit students outside their traditional feeder markets.
In this follow-up blog, we focus on operationalizing your data with advanced analytic insights and tactics to achieve your enrollment goals through targeted market expansion.
Although proximity to campus is a strong factor in the probability of a prospective student enrolling at your institution, it certainly isn’t the only factor. Given the demographic realities of the next 15 years, institutions must strengthen their position in primary enrollment markets while also efficiently identifying and developing markets that will provide a meaningful flow of students.
Data and advanced analytics are essential in revealing which markets possess the most potential to recruit and enroll the type and number of students to achieve your goals.
Institutions have a wealth of data, including extensive behavioral data if they utilize customer relationship management software (CRM). When leveraged through advanced analytics, these data reveal the variables that impact a student’s likelihood to enroll. That information can help an institution develop and implement marketing and recruitment strategies to recruit students outside of its traditional feeder markets. The information also promotes the optimization of resources, which is essential in tight budget times.
Whether you are trying to grow enrollment, increase net tuition revenue (NTR), expand geographic diversity in admissions, or extend your post-graduate impact, the answers are in your data (you’ll hear this a lot).
To develop new recruitment markets efficiently and effectively, you must understand the answers to these three questions:
The answers to all three questions are in your data.
Identifying where is the essential first step in creating new markets. Macro-market trends are a great source to determine where there is growth or decline in populations. For over 10 years, data and analysis from Western Interstate Commission for Higher Education (WICHE) and, more recently, Nathan Grawe’s book, Demographics and the Demand for Higher Education, have warned that the population of high school graduates will decline in the Northeast and Midwest and, after a period of growth, the population in most states in the South and West would also decline.
Recently, we explored the impact of the demographic cliff on college enrollments in our research report, “Futureproofing Institutions Against the Demographic Cliff.” The findings concluded that it is not who you recruit, but “from where you recruit,” and those institutions who seek to understand the details of the “where” will be more successful in achieving their undergraduate enrollment goals, especially their strategic goals.
It’s not as simple as focusing your recruiting efforts on areas with predicted population growth. Let’s say Florida, California, and Colorado are out-of-state markets for you. Do you attempt to increase your market penetration in all three states, or do you focus on expanding in one, two, or yes, all three markets? Which strategy offers the highest probability of success?
The answer lies in your data!
Using your historical data, you can analyze trends in inquiries, applicants, and enrolls against your recruitment, marketing, and financial aid costs. However, you must recognize how marketing or recruitment tactics you have implemented recently, for instance, waiving the application fee, adopting the Common App, or recently moving to test optional, could skew your data and lead to false conclusions.
For instance, to develop a new market, you waived the application fee for students from that market. Just because you have an increase in applications, does it really mean you will enroll more students? No! Your data can reveal if an increase in applicants may lead to an increase in enrollment or if the applicant increase is artificial and your yield rate will decline.
Advanced analytics can identify where you are gaining a foothold or increasing market share and where additional investment can yield a great return on investment.
Now that you’ve identified markets that hold the most potential, you can begin to change how you focus your resources in those areas on the right students. However, once you are focused on new, emerging markets, there is not a one-size-fits-all approach. To target geographic expansion, you need to consider many attributes about students from these areas to determine who is the best fit for your institution.
In other words, now that you’ve found out where, the question becomes which students. Again, you can find the answer by leveraging advanced analytics and your data.
There are many questions related to “which students,” and here are a few of them:
Every individual student is unique and understanding the interplay of every data point you have at your disposal helps identify what students have the highest likelihood to enroll and persist, who also align with your institution’s mission, vision, and traditions.
For instance, your best fit student might be from 300 miles or less away from campus, live in a rural area, have Catholic affiliation, and a 3.8 high school GPA. Or, your best fit student might be from 400 miles away, from a specific urban location, identify as Hispanic, be first-generation, and be in the top 10% of their class.
It is not efficient or effective to target every student within a large market area. You’ll commit extensive resources to students with zero likelihood to enroll, no matter what you do. In other words, you’ll be making a significant investment of resources with no return – which is never a good strategy!
So, you know where and which students, now, what action will help you accomplish your goals?
Advanced analytics provide the means to really dig deep into your mountains of data and find specific markets and specific students or groups of students within those markets, enabling you to target and optimize your recruitment, marketing, and financial aid resources. But what will be the most effective tactics?
We are beyond the days where the mantra was more, more, more – more search to get more apps, more high school visits, more on-campus programs (or more virtual programs) – we simply don’t have the time nor budget. But we don’t need more, more, more. Predictive and prescriptive analytics provide the information we need to contact the right students, at the right time, with the right tactic(s), to achieve our enrollment goals.
When you start to determine which specific tactics will increase a student’s likelihood to apply and enroll, you are moving from predictive analytics to prescriptive analytics. Prescriptive analytics illuminate which tactics can change the student’s probability of applying and enrolling.
Think about going to the doctor for a health concern. The doctor reads your history and asks you what’s happening, i.e., they are obtaining behavioral information. From your history and how you are currently feeling, they prescribe a course of treatment to improve your condition.
Similarly, prescriptive analytics look at a student’s data, including behavioral data, and prescribes specific tactics to increase the likelihood the student will take the action you need them to take depending on where they are in the funnel. In other words, prescriptive analytics allow you to maximize your ROI.
For instance, we know one type of marketing message does not fit all students. The student is seeking specific information and/or expecting a specific type of recruitment action aligned with their interests and financial information. Messaging and recruitment expectations are established by how they interact with everything in their lives, from retail to social media to entertainment. Increasingly, they are expecting the same type of precise information or contact from colleges and universities. Prescriptive analytics provide the marketing and recruitment experience the student expects, which will have the most impact on the student applying and enrolling, and you achieving your goals efficiently.
Prescriptive analytics allow you to personalize your engagement with the right students.
Here’s a more specific example of how prescriptive analytics can optimize recruitment and marketing resources.
Your historical data may show a virtual tour can increase the likelihood to enroll for students from one of your new markets by 10% on average. The real benefit is understanding that student A’s likelihood to enroll increases by 7% if they participate in a virtual tour, while student B’s likelihood increases 19%, and student C’s likelihood doesn’t increase at all.
Targeting students A and B is the ideal course of action. However, if your resources are constrained, focusing on student B will yield the best ROI.
And for student C, where a virtual tour provides no increase to his/her likelihood score, you can test other tactics, such as incentivizing travel for an on-campus event to see if that increases that student’s likelihood.
Likely, one of the primary goals for expanding your geographic footprint into new markets is to improve NTR. For public institutions, out-of-state students generally provide more NTR per student compared to resident students. For many private institutions, new markets mean hitting your headcount goals, ideally with students who bring in more NTR per student than you currently receive.
Just like marketing and recruitment engagements, institutional aid awards will increase the likelihood to enroll for specific students, but which students and how much aid or additional aid will generate enrollment? The reality is that each student has their own price elasticity. Some students may come from a strong financial background and have limited to no elasticity because aid is not a driver in their decision. Others may see lifts in likelihood scores based on every increase in aid awards.
For example, using prescriptive analytics, you see that student A’s likelihood to enroll increases by 20% if offered $5,000, but there will be no additional increase in likelihood with more aid, although you may still be able to affect the likelihood with a recruitment or marketing tactic.
On the other hand, you see that Student B’s likelihood score may increase by 15% if offered $5,000 and increase by another 30% if offered $15,000.
Having visibility of individual price elasticity is also useful if your institution considers appeals.
It’s April 23, and your enrollment projections are behind where they need to be to accomplish your goals. You have several hundred financial aid appeals.
Do you give $2,000 to all the students? How did you come up with $2,000? “It sounded about right…!” Are you giving some students too much and some not enough? Probably!
Rather, with advanced analytics you can be armed with knowing the precise award that will make a difference, and by the way, likely, many of those students will enroll if you don’t give them any additional aid!
The ability to understand the price elasticity at the individual level can be aggregated to develop improved aid strategies for out-of-state cohorts or where you are trying to build new markets. With that information, you can optimize your financial aid to achieve net revenue and other enrollment goals.
With improvements in artificial intelligence and machine learning, your data is even more powerful to you than ever before, and the science and means to support decisions improves every day. Given demographic realities, enrollment market dynamics, and institutional budget forecasts, the situation demands action now to accomplish your enrollment goals and into the next decade effectively and efficiently.
By taking these approaches outlined above, you will identify markets where there are the students. Within the market, you can identify which students. With prescriptive analytics, you can identify which recruitment or marketing tactics or, with much better precision than you currently have, how much financial aid will increase the likelihood of each student or group of students to apply or enroll.
If you’d like to discuss how you can develop new markets, please contact us at firstname.lastname@example.org.
Zach Varga co-authored the blog.