Using Big Data Analytics to Streamline the College Admissions Process

Several decades ago, the college application process was rigorous, and college admissions teams had mounds of paperwork to sort through for each prospective student. Requirements could include anything from detailed academic transcripts, community service logs and resumes to standardized test scores, a variety of recommendation letters, award and recognition lists and more! This was usually in addition to wordy admission essays about why students should be considered for admission and was often followed by lengthy interviews and the processing of application fees. It was exhausting.

Today, there are still admissions policies in place that must be followed, which among other things, require students to prepare compelling admission essays. However, admissions officers don’t just have to wade through page after page of essays, award and recognition lists and recommendation letters to find likely candidates for admission. Instead, they can turn to a much easier and more convenient location to find good students to complement their routine admissions work.

They can turn to technology.

Big Data Analytics?

When you first hear that colleges are using technology to find the best students, your mind probably goes to social media, and that certainly does play a part. It’s not the only part, though, and not even necessarily the biggest part.

Big Data Analytics in Recruitment – Predictive Analysis

One of the most effective ways schools use data is through a process known as predictive analysis. This is a process in which admissions officers use students’ demographics, educational markers, interests and more to try to “replicate” their best students. Saint Louis University is one school that’s been employing these tactics, and a spokesperson discussed the process with Jeffrey Selingo, writer for The Atlantic.

The process used by Saint Louis is pretty standard in higher education now, and most colleges follow the same steps. They start by identifying the best, most high-ranking graduates from the last several years. Then they look for commonalities those students share. Saint Louis, for example, noticed that a large majority of its most successful students had been Catholic, had largely enrolled in the same few majors and had actively sought out private school educations.

This allowed them to recruit similar students that they believed would also be successful because they fit a certain mold.

Wichita State University uses a similar program called Business Intelligence and Predictive Modeling (BIPM). It, too, tracks information from successful students. It looks at things like students’ family support systems, whether or not they have jobs, how many classes they take each semester and other factors.

A spokesperson from Wichita claims using this type of data yields a 96% accuracy rate in picking out “high-yield applicants.”

In this way, colleges are able to streamline the admissions process by pre-selecting students they already feel will be good candidates and marketing specifically to them.

Targeting High Schools

Another popular way colleges use data is by tracking the high schools of their incoming students. This shows them which schools provide them with the largest matriculation and graduation numbers. If a university determines that High School A provides them with 33% of their successful students while High School B only supplies them with 18%, it’s much more likely to spend money, time and effort into marketing itself to High School A.

The Next Step – Social Media Analysis

Once a college has decided, based on predictive analysis, which students may be good candidates for one of its programs, its admissions officers will then turn their attention to the students’ online presence. Admissions officers can learn a lot about prospective students by looking at their various social media pages.

Pictures can tell them whether the students party a lot or are prone to posting inappropriate images or videos of themselves. Admissions officers can tell if students have large and diverse networks of friends and family. They look at hobbies, interests and career goals. All of these things can be used to determine whether or not a student is a good fit and is likely to do well. Then they’ll begin marketing heavily to the students they feel are most likely to choose and succeed at their universities.

Tracking and Monitoring

Tracking website activity and monitoring email open rates are also frequent activities. Colleges can see which site pages are getting the most traffic and use that to invest in logical improvements. They can track the percentage of people opening their emails and reading their newsletters to see what types of information and headlines grab people’s attention and provide the school with greater visibility.

SEO (Search Engine Optimization) marketing is also huge. Colleges look for the words, phrases, facts and figures that are most often searched for by prospective students, and then they saturate their own websites with those key terms. Then, when students search for things like “best nursing program” or “colleges that don’t require GRE,” the school’s website will turn up in the search results, garnering more web traffic and increasing the chance that a student will select that school.

They also pay close attention to which of their ads are being clicked on the most. This lets them see which sites/ads are bringing in the most traffic and which ones they should eliminate or move somewhere else.

Summing It Up

We’ve all known for years that technology’s the future. Things are never going to become less innovative. As the world changes, more and more of the important work done in higher education will take place online using big data analytics. That’s why it’s so important that schools start implementing these processes now.

A university’s median marketing/recruitment cost per student is about $2,185. That’s a large chunk of money to put into one single student, so colleges are certainly going to try their best to get the most likely candidates. Using big data analytics can help streamline the process and eliminate many of the wasted costs associated with unsuccessful marketing. It can also eliminate admissions teams’ wasted efforts in wading through hours of paperwork for students who never enroll.