Big data influences so many aspects of our personal lives and is intrinsic to business decision making. From the ads we see on social media and the shows Netflix recommends to how our healthcare systems operate, big data reaches far and wide.
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A report published by the EDUCAUSE Centre for Applied Research reveals that 69 per cent of post-secondary schools cite analytics as a priority for “at least some departments, units or programs.” Many universities utilise data for things like department reports, but miss opportunities to synthesise data channels and influence strategic university-wide decisions.
Leveraging big data and analytics can be incredibly valuable at a time when competition is high and enrolment take up is uncertain. In 2018, the Journal of Retailing and Consumer Services published a study that concluded that “students must be considered customers in the development of marketing strategy” in order for a university to be financially successful.
In merging data sources, universities can gain actionable insights and create informed initiatives to drive up application and enrolment rates. However, this approach requires institutions to adopt a streamlined approach to data management. Integrating data is made easy with a cloud-based, all-in-one online admissions platform.
As well as enabling universities to store and segment data, many online admissions platforms also have inbuilt advanced analytics tools that track applications in real time, highlighting issues and identifying trends. This allows admissions departments to see what converts and learn about their prospects’ interests and behaviour.
Higher education professionals can use big data and analytics to assess indicators such as academic history and geographical location to make predictions around which prospective students are more likely to enrol once they’ve been accepted onto a programme of study.
Universities can also use this information to tailor recruitment campaigns and target specific markets and areas. Using big data to understand prospective students in this way enables universities to invest in campaigns more wisely, reducing marketing costs and improving yield rates. Big data also enables them to make the student admissions journey more personalised.
68 per cent of marketers say personalisation based on behavioural data has a high impact on ROI. To provide a customised application experience, universities need to drill down into the data to understand their target market(s) and their goals. Then, they can segment audiences based on ‘student personas’.
There are lots of ways to do this. Behavioural personalisation, for instance, is where a university tracks individuals’ behaviour on their website or application portal and assigns different points to their journey.
When the prospective applicant has completed the predetermined actions, they are assigned to a persona and presented with the most relevant content for their current situation—content that encourages them to take the next step. The “content” could be anything from an application deadline reminder to an invitation to a virtual event.
Those interested in how specific institutions have leveraged big data to target specific students might want to read this 2017 article in The Atlantic. It explores how Saint Louis University used a data-driven approach to admissions to “recruit from new geographic regions and improve the racial and economic diversity of its student body, as well as its retention and graduation rates.”
Aside from the institution's website, universities can also use marketing automation software to keep track of social media engagement to see what works best on different social platforms. Most social media platforms enable the user to choose from a broad range of targeting options to refine the reach of a campaign, making sure its seen by the most appropriate prospects.
Universities can use analytics to discover which channels their audience are engaging with and therefore which are likely to generate the most enquiries.
University retention is another higher education hot topic, and for good reason. According to the Higher Education Funding Council for England (HEFCE), over 8 per cent of undergraduates drop out in their first year of study. This equates to roughly £33,000 per student (in England).
Universities can compare current student engagement metrics with previous data to determine who may be at risk of dropping out. From how often a student utilises the university library to how many times they miss a lecture or seminar, there are a number of factors universities can take into consideration.
In using predictive analytics, higher ed professionals can intervene early on to reduce the risk of the individual dropping out. Of course, it’s important that once they have identified a potential problem, universities employ appropriate strategies to re-engage. Interventions could be anything from personalised learning plans to one-to-one mentoring programmes.
To thrive in today’s competitive and uncertain climate, universities have no choice but to leverage big data and analytics.
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