In our previous article on the higher education sector and data, we looked at where universities could begin to mine insights. It’s also important to look at the next steps to take when it comes to data: using it to cohesively identify and understand the three stages of the student lifecycle.
Universities are beginning to understand that their students are not just those actively enrolled 18- to 22-year-olds. Rather, the life of a student begins before they set foot on campus. It includes their active enrolment and continues as they become alumni and, as learning demands change, lifelong learners.
Each of these stages comes with data, both available to mine from students as well as to mine for students. Data is a business asset – and universities can’t propel forward without increasing their data sophistication.
Data as a (live) asset
Education providers that want to become smart about their data use should follow the lead of industry and treat it similarly. This means understanding that data is an asset in the same way as intellectual property, infrastructure and cash reserves. To do this, disparate streams of data must be brought together and ‘owned’ by a central authority.
This is a good thing too, as the resources needed to then look after that data need to be put in place and are often beyond the capacity or finances of an individual department. Data must be nurtured, enriched, utilised and – very importantly – secured. It needs to be routinely augmented – from internal and external sources – cleaned and the use-cases researched, refreshed and delivered.
Only in this way will universities be able to follow the maturity curve that enterprise has pioneered to get the full benefits of its use. To date, most higher education institutions are not at this stage, which means of course that there is plenty of opportunity and still time to catch up, or be first.
The three kinds of students
With data management in place, universities can truly embrace the power of data, acknowledging the lifecycle of a student and capitalising on the opportunities.
Pre-students
Students should be identified well before they make their way to campus, books in hand. With the wide variety of social media and proclivity of younger generations to use it for communication, it is possible these days to learn a lot about potential customers.
Using data from social media platforms, a university could identify the most likely candidates for its offerings. By combining this data with behavioural insights from actively enrolled students, such as those who are most engaged and what they respond to, content marketing can then be tailored to gain the attention of these potential students.
To capture their data, and move them from an unknown to a known prospect, an incentive for identification data (such as names, emails or phone numbers) needs to be implemented. For instance, offering a prize through a competition, or preferential access to an experience (make sure to check social media platform rules first).
The University of Salford in the UK, for example, is using geo-targeting to focus marketing on mature learners and potential students, who may have specific subject interest or professional needs¹. They also offer students a Tinder-style app, named Match Made in Salford, to match students with the right courses².
Throughout the process of data collection, which could be seen as straightforward lead generation, a picture of the market emerges – including motivations, likes and dislikes, life goals and views on education. All of this is an immensely rich bank of information that can then be used to shape the future of education offerings.
Active students
As soon as a student enrols and begins attending classes, a whole range of new data opportunities unfold.
This means more than just logging enrolment numbers. Information such as how quickly a student completes a learning task, how engaged they are at the library or whether they live and/or work on campus can all affect retention and performance rates. With universities fighting for funding, the ability to present robust stats on student achievement can be crucial.
Once again, it’s in the augmentation of this data that true value is revealed. Analysing a range of different sources of information, including social media and peripheral student data (e.g. from their library card, tutorial participation or digital platform use) could enable a better understanding of student needs.
Finding out how students want to learn, what they want to achieve, the pressure points they feel and even things such as their interests and outside-school commitments can lead to a better learning environment. With the courage to implement new ways of learning based on the analysis, the potential goes up to increase student engagement, achievement and the likelihood of them ‘re-engaging’ post-study.
Moreover, this data can be self-perpetuating. Collecting it can lead to further innovations, such as Deakin University’s Genie³, a virtual assistant or chatbot designed to help students in every aspect of their learning and student life. The data that a chatbot gathers in turn allows it to learn what its students need – and all that data as it is used goes straight back into the larger data pool, continually enriching the asset.
Alumni and continuing students
As the workforce changes, so do the needs of the working population. While it’s hazy what effect technologies such as robotics, automation and artificial intelligence will have on jobs of the future, one thing is clear: to thrive, employees will need to be resilient and continually reskill.
For universities, this is a huge opportunity but it will mean offering the kinds of courses that fit in with what, in effect, will be working students. Understanding the needs of all these different cohorts will be key. It’s important therefore, for higher education institutions to view past students holistically – not just as potential supporters, donors or candidates solely for existing post-grad degrees.
This means following students as they engage with the wider world. Combining industry specific data, geospatial and geolocational, generic demographic data and granular data from online platforms or sampled research will all be needed. These students will continually be entering different stages of their lives and potentially moving between industries. Consequently their needs, and the attractiveness of different university offerings, will also need to adapt and expand.
Boston University provides an example of ways to engage with alumni and potential mature students. By posting on its Facebook page about turning the campus castle into an alumni centre it generated 300 likes, shares and comments. The university used that data to convert those engaged users into donors, but the information could just as easily be used for other ventures, such as gaining knowledge about the needs of potential continuing students4.
Being bold with your data
It’s understandable if this approach to data scares higher education providers. Currently, there are very few using these forms of data well, and it’s a steep curve to reach the point of realising the benefits.
Data needs to be approached as an ongoing investment. To remain competitive, it is a crucial one.
The good news is that there are institutions that have already travelled this journey. Following their learnings will yield results without the need to leap into the complete unknown.
Treating data as an asset – a living one that needs nurturing and protecting – will pay off in the long run. There’s simply no question that it’s time to get serious about it.
