The Role of Data Analytics in Higher Education: A Comprehensive Look Across the Student Lifecycle

A variety of sectors – not least of all higher education – use data analytics every day to gain valuable insights and inform decision-making processes.

Utilising data in this way can not only help universities to streamline their operations, but encourage growth and a higher return on investment.

From marketing to financial aid, data analytics can be used across the student lifecycle and by many different departments in the institution. Let’s take a closer look at how data analytics is helping to shape the higher education sector today.

1. Data analytics in marketing

University marketing teams around the world are harnessing insights gleaned from data analytics to craft increasingly personalised and targeted recruitment campaigns. Here are some of the main ways they’re achieving personalisation:

Student segmentation

University marketing teams can segment prospective students based on variables such as their academic interests and geographical location. In doing so, they are better equipped to customise their messaging more acutely for different groups.

Personalised content

Generic emails are a thing of the past. After segmenting the data, marketing teams can use data analytics to suggest programs or events to prospective students; incentives that chime with their interests, academic path or career goals.

Campaign performance analysis

As well as informing the content of campaigns, data analytics can evaluate the success of a website, social media, email or advertising campaign after it’s live, enabling marketing staff to determine which strategies are the most – and least – effective.

Cost-per-enrollment analysis

Data analytics is harnessed in the budgeting process too. For instance, it can be used to work out the cost-per-enrollment for different marketing channels – or on a more granular level, individual campaigns. This helps universities allocate resources more effectively.

2. Data analytics in admissions

Data analytics-driven systems and methods are being utilised by university admissions teams for a variety of purposes. Here are some key ways universities can use data analytics to enhance the admissions cycle:

Predictive modelling

Admissions teams can use historical data to create predictive models that forecast the likelihood of a candidate’s acceptance or success. Predictive models can look at test scores, academic performance, extracurricular interests, and more.

Application screening

Application screening – combined with human evaluation – can save admissions teams a lot of time. An admissions system using data analytics can assess applications at speed and filter candidates who might not fit the criteria.

Diversity and inclusion

Data analytics can also help promote diversity and inclusion. Universities can evaluate information pertaining to demographics, and use this data to identify any potential biases or inequalities in the admissions process.

Application Feedback

Like marketing, data analytics comes in useful after recruitment has taken place. It can collect and evaluate applicant feedback, enabling admissions teams to understand the process from an applicant’s perspective and improve outcomes.

3. Data analytics in financial aid

Data analytics can play a key role in ensuring students’ financial stability, affordability and wellbeing. It can also be used to help determine financial aid eligibility. Here are some of the ways financial aid teams are using data analytics:

Need-based aid allocation

Data analytics can be used to assess the financial needs of individual students. It can analyse information on family income, assets and other information that is used to determine the level of financial aid a student is eligible for.

Merit-Based aid allocation

Data analytics can also analyse things like standardised test scores, GPA and extracurricular activities to assist in the allocation of merit-based scholarships.

Aid package optimization

As well as determining how much aid an individual should receive, data analytics can be used to optimise financial aid packages. The goal of aid optimization is to ensure that each student receives the correct combination of grants and loans, etc.

4. Data analytics in student success and retention

Even after a student has accepted and enrolled, data analytics can play a significant role in ensuring their success. Data can be leveraged to predict student retention risk factors or academic difficulties, for instance. By identifying potential roadblocks early, universities can intervene before the problems are likely to escalate.

Early warning systems

When it comes to developing an early warning system, universities could consider data relating to attendance, grades and engagement. If attendance is low, for instance, the system could trigger a student support intervention.

Course engagement analytics

Together with information on attendance and attainment, data analytics can provide some interesting insights into student engagement levels, including engagement with course materials and participation in class discussions and debates.

Retention modelling

Predictive models can also be used to help forecast student retention figures. By analysing historical data relating to retention, universities can further develop their student retention approaches and strategies and make them more effective.

Graduation pathway analysis

Data analytics engines can analyse data to create personalised course recommendations and pathways to graduation, helping students to stay on track and make informed decisions about their academic journey.

5. Data analytics in alumni engagement

Finally, data analytics is being used at alumni level to keep students engaged after they’ve graduated. Universities can leverage data to enrich the alumni experience through networking and career development opportunities, and more. Here’s how:

Alumni data management

At a more basic level, data analytics can enable universities to establish a centralised database of alumni information. This can include information relating to an individual's academic journey and achievements, as well as their contact data.

Segmentation and targeting

After this data has been established, universities can begin to segment alumni based on various factors, and then, like at the admissions stage, start to customise communications accordingly for optimal engagement.

Event planning and promotion

Personalised communications at aulani level could include invitations to events that are based on interests, engagement or even their donation journey, if applicable. Data analytics can identify the events that attract the most alumni engagement.

Alumni career services

There’s so much potential for universities to engage their alumni. Data analytics can help inform strategies for career development services, mentoring programmes and networking opportunities for existing students as well as alumni.

Ethical considerations when using data analytics

It is important to undergo any data analytics-informed work both responsibly and ethically to ensure the fair use of data. Data protection regulations vary from country to country, however here are six factors universities should consider:

• Data privacy - Universities must ensure they comply with the relevant data privacy regulations in order to protect student, staff, alumni and faculty data. One example is the General Data Protection Regulation (GDPR).

• Bias - If data analytics is used to inform algorithms, it’s important to test and monitor them to prevent bias and discrimination; failing to do so could exacerbate discrimination.

• Transparency - Universities should operate transparently and make it clear to students (and all other stakeholders) how their data is collected and used.  

• Informed consent - Universities, like businesses and other organisations, must get informed consent from individuals before collecting their data, and there should always be the option to ‘opt out’.  

• Anonymisation and de-identification - Prior to data being analysed or shared, anything that makes it personally identifiable should be removed or anonymised in order to safeguard individuals' identities.

• Data ownership and control - Universities should clarify who owns and controls data, as well as how it can be used, especially when third-party vendors or contractors are involved.

As we’ve seen, data analytics can have a huge impact on higher education, from the moment prospective students engage with a marketing campaign through to graduation and beyond. The responsible utilisation of data can result in higher yield rates, greater engagement and a more enriching university experience.

However, it is also important to acknowledge the responsibilities universities and institutions have around privacy, bias and transparency, and the need to create environments – both offline and online – that are inclusive and free from bias.