Student Retention with Advanced Analytics

A Case Study in Student Analytics for Higher Education Let's talk

When one of Australia’s leading universities faced challenges involving unexpected student enrolment and retention numbers, we were engaged to conduct value-adding analysis to uncover insights and help form a strategic plan to improve student experience. 

The Challenge

One problem the university faces is especially challenging: The need to consistently attract a strong student body, balanced with the university’s capacity to provide the highest quality education. Our task was to apply advanced analytics techniques to identify students most likely to drop out of their courses. The objective was to identify, support and retain students at risk of dropping out – before the students themselves even knew there was a problem.

A data warehouse was already established that could address the essential requirements for a single holistic student view. An outstanding requirement was how to demonstrate the value of this data by generating strategic insight into fluctuations in student enrolments over time. Many friction points in the student experience arise through challenges in efficiently servicing the unexpected numbers of students.

The Solution

Utilising a small team of experienced consultants, we analysed socio-demographic and enrolment data of all students university-wide, surfacing insights on a fully functional dashboard supporting drill-down by faculty, school, course, and subject.The analysis of students included library and online learning platforms, so basic metrics for study engagement could be developed for each student. Furthermore, we developed predictive models highlighting students likely to reach a ‘fail’ outcome.

By adding in student engagement and assessment data, the prototype dashboard provided clear measurement of the drivers impacting enrolment, transition to postgraduate, and retention throughout the student lifecycle.

Bringing together data from a range of systems across the university, we built a model of student engagement and demonstrated the correlation between engagement and degree completion. Achieving this level of clarity empowered lecturers and tutors to focus their energies on assisting specific students so that overall pass rates and grades could be substantially improved.


Our model enabled the university to identify students most at risk of leaving their course. This meant they could start taking pro-active actions to mitigate these risks. As an added benefit, the same techniques could also identify the students most likely to go on to postgraduate study.

Through analysis of student subject choices and assessment, we delivered rich insights and recommendations involving the ideal course options for specific outcomes, and optimum pre-requisite learning pathways.

We work with our clients to understand, segment and predict customer behaviour. We generate insights that can be leveraged to optimise customer retention and the value of customer relationships. The Data Scientists in our Advanced Analytics team use a range of sophisticated techniques to develop deep business insight. These techniques include statistical modelling, machine learning and data mining.

We can help you identify and detect customers at risk of leaving so you can start doing something about it. We can also track and forecast customer behaviour and help you better understand customer value so you can optimise your marketing investment.

Let’s Talk.

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