Why university admissions data tells an important story

When you start out as a counsellor, most of your guidance comes from instinct, recent memory and a few students you’ll never forget. That’s natural. However, it quietly shapes your advice around the cases that stuck rather than the patterns that hold true. 

So here’s a technique worth learning early: build a picture of your own school’s applications across several admissions cycles. Then what you tell a student rests on evidence, not on the loudest anecdote.

Why collating data matters

The data already exists. Every school sits on years’ worth of Ucas outcomes, yet hardly any of us look across cycles – we glance at last year’s data and move on. 

But a single year can mislead. One unusually strong or weak cohort skews the whole picture, and the real patterns only surface when you compare across cycles. 

Over five entry cycles, I built anonymised workbooks for my own school. They answered questions no single year could. Were our students aiming too low or too high? Which courses showed a stubborn gap between the offers made and the places students actually took up? Was a “safe” insurance choice really safe, given how our own students had converted before?

A counsellor’s job is to be a source of knowledge. And knowledge is more than instinct stacked up over the years. Do this work, and you become that source.

The method

It’s deliberately low-tech. Any counsellor with a spreadsheet and a free afternoon can do it.

1. Pull the basics for every applicant, in every cycle: entry year, course, university, offer type, firm and insurance choice, final outcome. Aim for five cycles – but even three will show you something.

2. Anonymise first, always: Strip the names out before you analyse anything. Anonymising also allows you to share the work with colleagues and other schools.

3. Keep your fields identical across cycles: People get this one wrong more than any other. If your 2022 columns don’t match your 2025 columns, the comparison falls apart. Consistency is key.

4. Run the two or three comparisons that tell you the most: offer-to-acceptance conversion by university; firm versus insurance outcomes; and the course-level patterns where your cohort keeps clustering or struggling.

What it changes 

Picture a Year 12 student weighing up which choice should be their firm and which their insurance. You no longer offer a hunch. You say: “Students with a profile like yours who took this as their insurance choice converted at this rate, and here’s what that suggests for you.” 

The conversation moves from reassurance to evidence. The student feels the difference straight away. That’s the real pay-off. Doing the data work upfront buys you confidence and speed in the moments that actually count.

The invitation

One school’s data is a small sample. A shared method is a big one. The technique gets far more powerful when counsellors compare across each other’s schools, not just across their own years. That only works if we all anonymise properly and pool what we find. Collaboration is key for success. Hold the continuum from secondary school to university to career in view, and a pile of separate workbooks starts to tell one story.

So build your own. Anonymise it. Share the method with colleagues. The evidence we need is sitting in our own files – and it’s been there all along, waiting for us to look at it.