Behavioral Targeting and Predictive Analytics in Higher Ed

Big data has many uses in higher education, with many applications transferred over from business uses. Colleges and universities are adopting analytics to improve enrollment and retention. Two of the AI tools in the arsenal for schools are behavioral targeting and predictive analytics. Institutions not yet using these tools can consider getting on the bandwagon to add these practical digital strategies. Data only improve metrics when integrated with the processes on the ground. Thus, institutions need to incorporate data into workflows to use human talent alongside machine learning tools.

Behavioral Targeting Can Help You Focus Your Enrollment Advertising

It’s a simple concept. By analyzing the web browsing patterns of possible prospective students, behavioral targeting helps choose the prospects most likely to respond to your offerings. This method is yet one more way to make your advertising personal and relatable for the audience. Behavioral targeting can combine well with other targeting practices like geofencing to further refine your strategy. Here is where the knowledge within your organization can come together with data insights to help predict which behaviors are likely to signal a good lead. Data professionals may understand overall trends in enrollment marketing, but you and your staff know more detailed information about your school’s enrollment patterns to best use these tools. The collaboration between humans and big data can help these targeted ads reach the right prospects.

An essential part of the process is to make sure the ads you are displaying to these behavioral targets are of interest. You can have the best targeting, but you will miss the mark if you send the wrong ad. For example, an ad touting your great nursing program will not convert if you target people looking for entry-level jobs and training programs in aviation. Each advertisement will benefit from your inside knowledge, matching the needs and desires of prospects to relevant programs and messages. As part of a strategy to nurture leads, the right message goes a long way towards capturing and keeping the interest of prospective students who are likely to succeed at your institution.

Because behaviorally targeted ads are usually pay-per-click, wisely allocating this part of your marketing budget is critical. To explore the options for behavioral targeting as part of your enrollment marketing efforts, feel free to contact one of our specialists.

 

Predictive Analytics for Retention

Another way higher education is increasingly using data analytics is to assist in retention efforts. Certain student behaviors may signal academic, personal, or financial trouble, which could cause them to drop out. By using computers to sift through data, you can find students most likely to need interventions that can help them to persist and stay enrolled.

Identifying student problems before they become serious is beneficial for your institution. You will have the student continuing their studies in future terms, and the student is much more likely to graduate. It is advantageous for the student, too, as dropping out dims their prospects for job success and upward mobility, plus they may be carrying student debt without the degree to help them attain a better-paying job. Increasingly, students who we want to assist into better careers in our workforce have socioeconomic profiles that put them at risk for dropping out, so targeted interventions are key to keeping your institution’s enrollment and graduation rates healthy.

Each institution has unique markers for students heading towards dropping out. By sifting through data from previous years, your team can identify students who dropped out and compare these to students who completed their studies, looking for patterns and trends. For example, late tuition payments could give an early signal that a student is hitting financial bumps, and some assistance could help them stay in school. Another marker might be a “C” grade in first-year English classes, which could signal a student who will likely fail to make it into the nursing program they hope to pursue. In this case, advising to look at other options in the health sciences could help this student better prepare for academic and career success. Accurately interpreting the data can help your campus advisors to intervene in the right way to help students before they drop out, potentially remediating a problem before it becomes severe.

Predictive analytics only helps you retain students if you have the infrastructure and culture to support student needs. But this data can be a powerful tool to help drive planning for student support that can improve retention and graduation rates.

Predictive analytics can also be helpful for fine-tuning your enrollment procedures. For instance, different metrics may help you predict how large your incoming class will be, better preparing you to welcome them and accommodate their needs.

 

The Ethics of Using Data

Like all uses of digital information on students and prospects, both of these uses of data have ethical dimensions. Concerns about data privacy and security are valid in our digitized world. Machine learning applications can have bias programmed into them, which can skew your results and replicate historical inequities; awareness and attention can help mitigate this problem. As with any data project, the impact depends on the implementation and people to improve enrollment and retention at your institution.

 

For more information on incorporating predictive analytics into your enrollment and retention efforts, please make an appointment with one of our specialists to discuss the process.