- Reading time: 3 minutes
- Price: Free download
- Published: 16th September 2026
- Word count: 852 words
- File format: Text
Why do you want to study this course or subject?
I started keeping a spreadsheet because I kept guessing wrong. I resell secondhand trainers and jackets online, and for the first few months I priced things by instinct and wondered why some listings sat for weeks. When I began recording the brand, the asking price, the number of saves a listing got and how long it took to sell, patterns appeared that I would never have noticed otherwise: photographs taken near the window sold faster, and listings posted on Sunday evenings attracted more messages than the same items posted midweek. I cannot prove those are causes rather than coincidences, and that uncertainty is exactly what interests me. Marketing analytics appeals to me because it sits between two things I like separately. In Mathematics I enjoy the parts where a method has to be justified rather than just applied, particularly the statistics work on sampling and correlation. In Business I enjoy arguing about why customers behave the way they do, where the answer is never final. Analytics forces those together: a number is only useful once you can explain what decision it should change. Reading about how companies test different versions of an advert or a webpage showed me that this is a craft with real discipline behind it, including the awkward question of what counts as a fair comparison. I want to study it properly rather than continue guessing from a spreadsheet of a few hundred rows, and I would like to learn the statistical methods, the tools and the ethical limits around how customer data is collected and used.
How have your qualifications and studies helped you to prepare?
My A-levels give me a workable base for this course. Mathematics is the most directly useful: the statistics content on probability distributions, sampling and hypothesis testing has changed how I read claims about customer behaviour, because I now ask how many people were actually asked and how they were chosen. I have found the algebra behind regression easier to follow once I understood it as fitting a line that makes the errors as small as possible, and I am comfortable with the idea that a model is a simplification chosen for a purpose. Business has taught me the vocabulary that analysis has to speak to, especially segmentation and pricing. For a coursework task on a local bakery I built a simple breakeven model and realised how sensitive the outcome was to one assumption about average spend, which made me more careful about presenting single figures with confidence. Geography has been unexpectedly relevant. Fieldwork on retail use of a high street involved designing a recording sheet, collecting data in pairs and then defending our sampling choices, and the statistical tests we used to compare sites were the first time I had applied maths to messy, real information rather than tidy exam data. Outside lessons I have worked through an introductory online course on spreadsheets and pivot tables, and I have started teaching myself the basics of SQL using free practice exercises, mainly writing queries that join two tables and group results. It is slow progress, but I can now read a query and predict roughly what it will return.
What else have you done to prepare outside of education, and why are these experiences useful?
Working Saturdays at a garden centre has given me a clearer sense of where marketing data actually comes from. I operate the till, which means I ask customers whether they want to join the loyalty scheme, and I count stock in the outdoor plant area. Seeing which bedding plants get reduced because nobody bought them, and hearing customers explain what they came in for, has made me sceptical of any dataset that only records completed purchases. It also taught me to be accurate under mild pressure, since a miscounted tray ends up in the following week's order. At college I volunteered on the enrichment committee and helped with one manageable job: a survey asking students which clubs they would attend. Two of us wrote the questions, and my contribution was cutting the number of options and adding a slot for when people were actually free, because our first draft assumed everyone had the same lunch break. We collected 140 responses across three days at a table by the canteen. The clearest finding was that demand for a film club was much higher than the committee expected, and it now runs fortnightly. I am also honest that our sample skewed towards students who walk past the canteen, which we noted when we reported back. Away from all this I play badminton in a local club league, which keeps my weekends busy and has made me better at losing without sulking. I am looking forward to group projects where the analysis has to be handed over to people who will use it, because explaining a result plainly is the part I find most satisfying.
This example has 4,654 characters across the three answers. Use it for ideas and structure. Your own UCAS answers must fit within 4,000 characters in total, including spaces.
Why this example works — strengths and ways to improve
This is a specific, honest and coherent statement. Your reselling spreadsheet, coursework and survey give concrete evidence, and your reflection on causation and sampling bias is genuinely thoughtful. The main problem is length: at 4,654 characters it exceeds the 4,000 limit, so you need to make disciplined cuts, starting with a few weaker lines and some overlap between answers.
Subject motivation
I cannot prove those are causes rather than coincidences
This works because your interest comes from a real problem you met and could not fully solve. Admitting the limits of your spreadsheet shows curiosity about method, not just results. The line about a number changing a decision captures the subject well. The A/B testing reading would be stronger if you named one specific point you learned from it.
Academic preparation
realised how sensitive the outcome was to one assumption about average spend
Your coursework example turns a routine task into evidence of judgement. The Maths, Business and Geography links are each specific. The SQL learning is modest and honestly described. However, sampling and statistics appear in both of your first two answers, so you could trim that point from the first answer to free up space.
Evidence and reflection
our sample skewed towards students who walk past the canteen
This reflection is mature. You report a result, its impact (the film club now runs) and its flaw, which shows real analytical thinking rather than a generic skills claim. The same pattern runs through the whole statement and is its strongest feature. You do not need more activities, just this quality kept consistently.
Relevant experience
made me sceptical of any dataset that only records completed purchases
Using an ordinary garden centre job as evidence works well. Unsold stock and what customers say they came in for give a real insight about missing data. The stock-counting accuracy point is weaker and more generic. The badminton sentence adds little to your case for the subject and could be cut.
Credibility and voice
It is slow progress, but I can now read a query
Your voice is plausible for a school applicant. You claim modest competence and never present yourself as a professional analyst, and phrases like 'one manageable job' sound measured and trustworthy. Keep this tone. Cutting the badminton humour would not cost you your personality, which already comes through in your precise, self-aware observations.
Structure and format
has made me better at losing without sulking
Each answer is clearly over 350 characters and stays on its own question. However, the combined total of 4,654 characters breaks the 4,000 limit by around 650. Good candidates for cutting are the badminton lines, the duplicated sampling material in your first answer and some descriptive setup. Remember that cutting must still leave at least 3,500 characters.
What you’ve done well
- Your reselling spreadsheet gives an original, concrete origin for your interest and leads naturally into questions about causation and fair comparison.
- Your survey account shows what you actually contributed, what you found and the limitations you acknowledged, which is exactly the kind of evidence-based reflection the statement needs.
How this draft could improve
- Cut about 650 characters to get under 4,000. Start with the badminton sentence, the stock-counting accuracy line and the general statistics phrasing in your first answer, which overlaps with your second.
- Make the reading on advert testing more specific by naming one idea it taught you, rather than describing it in general terms. Do this while staying within the reduced character count.
- Consider tightening your closing sentence so it links back to the point about turning analysis into a decision, rather than introducing group projects as a new theme.