How Universities Are Reverse-Engineering Student Journeys Using Data

Higher Education institutions are now leveraging open source data in order to enhance student journeys, create extraordinary learning experiences, and career paths as well as a means of standing out in an increasingly competitive space
5 min read
How universities are reverse-engineering student journeys using data

In this article we will discuss:

Student data footprints are spotlighting core issues to be addressed 

Although many in the industry do not like to admit it, education is a business, albeit an extremely profitable one. On the one hand, you have students who want to get the best skills possible, at the best price possible, and on the other end are universities who want intelligent, financially viable students alongside diversity. 

In this context, analyzing students from a data perspective:

  • before/during/after the admissions process
  • throughout the academic enrollment cycle
  • as well as over the course of a graduate’s career 

The data is shedding light on trends that help educational institutions better understand their target audiences, evaluate candidate eligibility, troubleshoot mental health issues, and academic/financial challenges as well as building real-time career models based on data feedback loops. 

Key data footprints

Here are some of the datasets that universities are collecting and analyzing in order to become more competitive, and offer students a first-class experience:

  • Information from social business accounts such as on LinkedIn to be able to accurately map out student career journeys. For example which high-schools, universities, and extra curricular programs did people attend that correlate with placement in the top law/consulting firms. This can indicate what these companies are looking for, enabling institutions to tweak curriculums, and give better career counseling and guidance. 
  • Social sentiment is a very valuable thing for management to have especially when it comes to understanding the general ‘vibe’ on campus. By analyzing posts, pictures, discussion groups and the like, universities can discover previously unknown positive/negative trends that need to be addressed such as anti-semitism, racism, sexually predatory behavior, and gender-empowerment such as female leadership. This allows professors, and deans to really address toxic situations before they get out of hand and/or tap into and reinforce positive movements in and out of the classroom. 
  • Search trends which may include initial search queries such as ‘best architecture academic programs’, or help raise red flags throughout the academic period such as ‘how to deal with anxiety, and depression during exam period’. These kinds of posts can assist marketing departments to connect with target audiences, as well as aiding counselors in catching, and addressing mental health issues early on.
  • Enrollment data includes open source information as far as which institutions students are applying/registering for, and/or considering. This can be monitored by looking at the quantity of traffic any given competing institution’s website is receiving during enrollment season, for example. These datasets can be extremely useful for the admissions office, marketing departments, as well as those in charge of structuring programs, allowing them to make real-time changes based on applicant interest. 
  • Content engagement can come in a variety of forms such as advertisements being placed by competing universities, and can be quantified, and analyzed by collecting these ads, comments, click-through rates, and likes. One can also collect information on the messaging/propositions in order to get a better idea of what competitors are stressing – financial aid, academic excellence, or even geographic location. Once analyzed this information can help get a clearer picture of the landscape, in order to make more attractive offers to would-be students. 

Generating positive outcomes for both universities, and students 

All of the above-mentioned data points are but a small fraction of the information available to universities on the internet. Here we will discuss how these datasets can be further put to use by academic institutions: 

Improving the student journey: When you use data to understand current student body backgrounds, and coursework, as well as alumni career placement, you are able to provide students with better career advice and build more efficient curriculums. You may be able to build an in-house algorithmic model that can help you analyze a candidate’s pre-university background, and help guide them to a field they previously would have not considered increasing the probability of post-degree success, and satisfaction.

Creating extraordinary university experiences: Educational institutions are no longer just about learning but also about social interactions, parties, educational trips/excursions. In order to ensure that your students get the full package they were hoping for,  you will want to monitor what experiences other universities are offering, see what students are discussing on social media and search for trending events that you can utilize to uniquely position yourself (e.g. incorporating flash mobs trending on TikTok for choreography students or an ESG-focused investment course for business majors). 

Financial viability: Much in the same way that institutional investors are using alternative credit data to analyze eligibility for loan products, so too can educational institutions use these same tools to analyze monetary viability. Looking at an applicant’s cash flow/loan application/utility bill data can be indicative of candidates who come from a financially stable/unstable background, helping involve financial aid departments early on in the process. Alternative data such as social media posts can also help weed out scholarship-seekers who are living lavish lifestyles yet are still requesting financial aid. 

The bottom line 

The education industry is becoming increasingly more complex and competitive. Every year more and more universities are vying for the attention of would-be students. At the same time, many are struggling with student dropouts, inability to pay tuition, lack of diversity placement, trouble meeting career path development expectations, and the like. Collecting open-source data from across the web is enabling academic institutions to address these issues from a place of strength while utilizing real-time, student-generated datasets. The future of education may very well lie in following the ‘breadcrumb trail’ that students leave online.