Education,
sub-category by sub-category
2 education categories, each asked the questions real UK consumers ask, every month. Do they behave like one market or several? On the measures below, Education behaves like one market.
They agree on concentration. They split on model agreement.
Each strip is one measure. The grey band is the programme's full range; the gold span is where Education's sub-categories sit inside it, one dot each. A ratio under 1 means they cluster tighter than the programme — a category with a shared logic — and over 1 means they are further apart than categories chosen at random.
The data behind this chart
| Measure | Sub-category | Value | Within-category spread | Programme spread | Ratio |
|---|---|---|---|---|---|
| Concentration | Universities | 33% | 0.0 | 14.4 | 0.00 |
| Online Learning | 33% | ||||
| Leader visibility | Online Learning | 37% | 6.7 | 22.1 | 0.30 |
| Universities | 23% | ||||
| Read → cited | Online Learning | 28% | 0.6 | 11.0 | 0.06 |
| Universities | 27% | ||||
| Model agreement | Online Learning | 100% | 25.0 | 35.6 | 0.70 |
| Universities | 50% |
2 crowns, 2 different heads.
The most-named brand in each sub-category's answers. No brand leads more than one sub-category here — each is its own contest. 1 of the 2 leaders are upsets: not the market leader the programme seeded.
3 sources referee more than one education category.
The independent sources AI cites in the answers — counted only where they appear in the top sources of at least two of Education's sub-categories. gov.uk is trusted across 2 of 2: the place a education brand needs to be seen whatever it sells.
Brand pages convert 28% of reads in online learning, 27% in universities.
When Claude or ChatGPT opens a brand's own page while answering, how often does it make the cited sources? Education's sub-categories convert alike — a shared content problem, or a shared solution.
Every education story, and every other category.
Method: every sub-category runs 20 unbranded consumer prompts monthly against ChatGPT, Claude and Gemini. Alikeness compares each measure's spread within Education to its spread across every category in the programme (population standard deviations); model agreement is the share of models naming the same leader; read→cited uses Claude and ChatGPT only. Correlational observations of model behaviour at the time of each run. Data updated Wed, 02 Sep 2026 11:01:55 GMT.