Better conversion starts with a better understanding of intent.
Every cycle, universities receive thousands of applications. In theory, applications look like a strong signal of demand. But we all know the reality is more nuanced than that.
UG applicants are encouraged to use most, if not all, of their UCAS application choices. It’s a rational response to the system. It creates optionality and it manages risk.
But it also means that not every application represents the same level of intent.
Some are genuine first choices. Some are hopeful stretches. Some are safety nets.
This isn’t new information. Everyone working in the sector understands it. Yet when we look at how applications are treated, a different picture emerges.
Many providers still model conversion using averages or simplistic modelling, and deploy marketing activity broadly, as if all applicants are equally likely to enrol.
So why don’t we act on it?
Treating applications as equal is simple. It’s operationally neat. It fits neatly into reporting and forecasting models. But it doesn’t reflect reality.
And it comes at a cost.
When time, budget and effort are spread evenly across an inherently uneven pool, high-intent applicants don’t always get the attention they need, and lower-intent applicants receive engagement that may not land.
More importantly, the experience for applicants becomes less relevant. People at different stages of decision-making need different types of support. If we treat them all the same, we miss the opportunity to meet those needs properly.
This is not about offer-making strategies. It’s not about fairness or access to opportunity. Equity in admissions decisions is critical and should remain untouched.
This is about how we support applicants after they’ve applied. How we communicate, guide and help them make decisions with confidence.
Done properly, this approach should increase participation, not limit it.
What should we do instead?
The alternative is straightforward in principle: understand intent and respond to it.
Every applicant is different. But across cohorts, patterns emerge. There are commonalities in behaviour, profile and engagement. And where there are patterns, there is the opportunity to model.
By analysing applicant data, it’s possible to identify patterns that indicate differing levels of likelihood to enrol. This creates an initial view of intent based on what is already known.
But intent isn’t static.
As applicants engage, their behaviour provides additional signals, allowing that view to evolve over time. The result is a more dynamic and realistic understanding of the applicant pool, enabling more timely and relevant engagement.
What this looks like in reality.
The University of Suffolk is a good example of this in action.
We’ve built a bespoke predictive model for them using our propensity modelling methodology, trained on multiple years of applicant data. Applicants are then lead scored on a weekly or fortnightly basis throughout the cycle.
These scores are fed into the CRM, opening up a range of options to shape activity:
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Prioritising where more personalised engagement is needed
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Tailoring messaging based on likely intent
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Allocating budget more effectively
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Responding to behaviour as it changes
We’ve also used this insight to inform the targeting of their prospecting activity, increasing the effectiveness of paid media investment by focusing on audiences more likely to convert.
Importantly, this doesn’t lock anyone into an outcome. It doesn’t create bias. It simply responds to signals, helping the university act in a more informed and timely way.
“We have used Propensity Modelling for a couple of years to help us target our advertising and have seen a real impact in terms of reach, engagement, and ROI. For 2026 entry, we have extended this work to include lead scoring of applicants, which has happened weekly or fortnightly throughout the application period up to the January deadline. The data has provided us with a better insight into potential enrolments than our previous predictive methods, but also importantly has opened up the opportunity for more targeted conversion activity and messaging. When budgets are tight, it helps to be able to allocate the budget for paid-for conversion activity to the applicants who have real potential to convert.”
Karen Hinton, Associate Director Marketing
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