As the enrollmentFUEL team talks to enrollment vice presidents around the country, one topic comes up again and again, and that is how to find leads when fewer students take tests. We have researched this topic since 2018, when I wrote an Octane article titled, “360 Degree Digital Advertising Strategies.” There has been an obvious trend for many schools to explore becoming test-optional, and the COVID-19 pandemic has accelerated this timeline. Many enrollment strategists feel we are still a few years away from not being able to buy names from testing agencies. Still, schools are already starting to feel the strain in this cycle because the volume for certain profiles of names has been limited.
At enrollmentFUEL, we have drawn on our experience of finding graduate students and non-traditional students to help schools grow programs. With such campaigns, you often don’t have known lists and may want to budget for specialized and innovative tactics as you plan your future strategies. You also may be pleasantly surprised to learn the outlook for finding leads is good and can cost you less than purchasing names from traditional sources.
Look-alike modeling, combined with digital advertising, is one answer to consider. Why? Because institutions often need to expand their audience and find potential students who profile a certain way and act like your enrolled students.
Building a new audience starts much the same way as a good name buy strategy. Enrollment history and regression tools help us identify attributes desired within qualified populations. Today, and in the future, such attributes inform us how to build the algorithmic models when list purchasing is not an option.
Simply said, we review your enrollment history to find your future students. We can now do that with either known (traditional) lists or with newly modeled names that provide you households—which can then be served digital ads across several channels. Historical data is analyzed using multivariate regression against a large reference set for various traits. Evaluations are made to identify patterns among demographic, geographic, and activity/behavioral characteristics. Once patterns are identified, the software returns a list of suspects that closely mirror your current and most recently enrolled students’ households. In addition to the characteristics mentioned above, these suspects can be further identified based on various household characteristics such as income, buying patterns, and educational level.
While you will not have test scores with this data set, the data is still very rich with information. However, there can be some challenges if you’re not prepared. For example, in a look-alike list, you receive household-level data, so you won’t immediately have access to the student’s name or their email. For these reasons, we recommend when recruiting a look-alike population, you start Student Search with digital advertising (banner ads and social media ads). enrollmentFUEL also recommends tools identifying who visits your website. These tools let you hear the background symphony and will become the bedrock of what we think will drive Student Search campaigns in the future. Your team will work in newly formed Student Search strategies where:
Today, the technology we use to place code on school websites tells us who visited at the household level with good precision. Traditionally, we matched this data against existing purchased lists (enrollmentFUEL’s matchBACK™ solution). For some client-partners, after a student visits their site, we fire off a mail piece. It is printed on demand and sent to the home we identified, with a call to action to visit a specific landing page with a form. Triggered mailings and slowly batched emails help you build on initial interest and capture personalized information.
More than 1,2001 schools have already dropped testing requirements. Some institutions made the choice because administrators decided they were not the most effective way to gauge a student’s potential. For others, it was a response to the pandemic. Whatever the reason, the number of leads traditionally available will shrink over time. In the absence of lists, look-alike modeling can help you build your traditional and non-traditional inquiry pools in a smarter and more economical way.
If you would like to learn how enrollmentFUEL uses look-alike modeling to help enrollment teams reach their goals, contact me, and I will be happy to share more.
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