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Higher Education Institutions (HEIs) worldwide are operating in an increasingly uncertain and competitive environment, responding to economic and social changes whilst maintaining high levels of teaching, learning and student satisfaction.

Complex decision making is needed for dealing with these contemporary challenges, and often the role of data in making these decisions, planning for changes and the future is less understood or even overlooked.

We recently published a report which showed that 4 in 5 marketers are using Google Analytics data to report, rather than to strategise, whilst 68% of marketers are reactive, rather than proactive, in using data to make key decisions. Additionally, a recent Forbes article stated that “more than 95% of businesses face some kind of need to manage unstructured data.”

It’s evident that organisations struggle with data and how to best utilise it. The benefits of a data-driven approach to decision making cannot be underestimated. But, whilst efficient aggregation and cleansing of data can go a long way to identifying recurring behaviours and trends, it’s through predictive analytics that we can access real tangible forward-looking solutions.

Predictive analytics for marketing

The most common method of predictive analytics we undertake for our clients is determining customer preferences and behaviours to create a personalised and streamlined approach to marketing. Predicting what a user’s future behaviour will be, based on their demographic profile and activity history, helps to design and deliver highly personalised and effective content. Not only does this reduce costs when serving this content through advertising, but it also ensures a user isn’t bombarded with irrelevant content which doesn’t match their needs, reducing the overall customer brand experience.

Within Higher Education, we use predictive analytics to follow a prospective student’s enrolment journey beyond the application point. Using past admission data, we build predictive models through which we categorise new applicants in real-time, giving the opportunity for quick responses to nurture relationships and increase conversion and retention.

One of the key aspects of a predictive model is that it doesn’t separate attributes from the individuals who hold them. When simply calculating the proportion of enrolment of a segment of the data, other factors influencing the applicants within that segment aren’t considered. This is where predictive analytics steps up, elevating an approach from just knowing your data to learning from it, uncovering patterns and trends that inform decision making. The ability to fully understand prospects and customers provides you with a competitive edge and allows for cost-effective and more impactful marketing campaigns.

Predictive analytics for decision making

In addition to using predictive analytics for marketing, HEIs carry a large quantity of data for each student throughout their student life at university and can, therefore, utilise data science techniques to predict the following, helping to ensure the best possible student experience:

  • When is a student at risk of dropping out?
  • Which student actions will achieve higher grades?
  • What features of learning and living environments lead to better grades?
  • What grade is a student likely to get without intervention?
  • Should a student be referred to a councillor for help?
  • Which students use facilities in groups or alone?
  • Which assignments and courses are learning choking points?

Beyond this, data can also be used to develop institutional practice, from enhancing user experience to improving academic performance, and respond strategically to global trends with efficient, evidence-based decisions.

Get predicting

Just how important is a data-driven approach to marketing and decision making? Research from BARC suggests that organisations “that use big data saw a profit increase of 8% and a 10% reduction in overall cost,” whilst a Mckinsey survey has revealed that “data-driven organisations are 23 times more likely to acquire customers and six times more likely to retain customers and 19 times more likely to be profitable as a result.”

Whilst it’s imperative to have data at the heart of your organisation, it’s only useful if you can utilise this data to make better future decisions to improve experiences, results and money.

Need some help?

If you’re struggling to make sense of data or want to find out more about how predictive analytics and modelling can help your institution or organisation, get in touch with one of our experts.

Download the Arke data marketing report which analyses how data is used by marketing leaders, the challenges they face and recommendations for greater data-driven decision making.


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