How AI wants universities
Universities is decided on attraction: University of Oxford indexes 106 there against University of Edinburgh at 94, a 12-point spread. University of Oxford holds the strongest overall profile, indexing 106 against the category average: noticed, not returned to. But the brand assistants name most often is University of Oxford, not the category's market leader: being desired and being named are not the same measurement. Across these 7 brands, affinity tracks being named most closely (rank correlation 0.45).
7 brands · 60 AI answers · 70 dimension scores · measured, never estimated
100 = the average brand in the same category.100 = the average brand across every category in the index.
Universitiesupset
| # | Brand | Attraction | Affinity | Attachment | Summary | Named in |
|---|---|---|---|---|---|---|
| 1 | University of Oxford | 106124 | 106124 | —— | Noticed, not returned toNoticed, not returned to | 22% |
| 2 | University of Cambridge | 106124 | 106124 | —— | Noticed, not returned toNoticed, not returned to | 20% |
| 3 | University of Manchester | 100117 | 100117 | —— | Average on all threeNoticed, not returned to | 8% |
| 4 | University College London | 100117 | 100117 | —— | Average on all threeNoticed, not returned to | 13% |
| 5 | University of St Andrews | 97113 | 100117 | —— | Average on all threeCredible, not first choice | 10% |
| 6 | Imperial College London | 97113 | 94110 | —— | Relied on, not excitingNoticed, not returned to | 13% |
| 7 | University of Edinburgh | 94110 | 96113 | 100110 | Relied on, not excitingAhead on all three | 17% |
Indexed against the average of the 7 indexed universities brands, so 100 is the category average and a brand is read against the rivals it actually competes with.Indexed against the average of every indexed brand in the whole index — 1,605 brands across 61 categories — so 100 is the average brand anywhere here and a number reads the same on every board. 7 universities brands, measured across 60 AI answers in the latest weekly run.
Driver by driver
Where the category actually separates. A wide spread means assistants describe these brands very differently on that driver; a narrow one means it is table stakes.
Attraction
The widest field of the three: this is where the category separates.
Affinity
The tightest of the three: most brands land close together here.
Where each brand stands
Each brand's three indices, summed up in a line: which drivers it is ahead on, which it is behind on, and the one to build next. A brand that is noticed but not returned to has a different problem from one that is relied on but not exciting.
What assistants actually talk about
The sixteen dimensions underneath the drivers, scored 0–10 on how the answers describe each brand. Spread is the distance between the strongest and weakest brand: the widest are where a brand can still separate itself, the narrowest are the price of entry.
Where brands separate
| Dimension | Driver | Category avg | Strongest | Spread |
|---|---|---|---|---|
| Innovation & Technology | Attraction | 8.3 | University of Oxford 9.0 | 2.0 |
| Leadership & Momentum | Attraction | 9.1 | University of Oxford 10.0 | 2.0 |
| Heritage & Authority | Attraction | 8.9 | University of Oxford 10.0 | 2.0 |
| Quality & Performance | Attraction | 9.2 | University of Oxford 10.0 | 1.5 |
| Boldness & Creativity | Attraction | 7.9 | University of Oxford 8.5 | 1.5 |
Where everyone reads alike
| Dimension | Driver | Category avg | Strongest | Spread |
|---|---|---|---|---|
| Value & Pricing | Attraction | 6.0 | University of Oxford 6.0 | 0.0 |
| Lifestyle Fit & Identity | Affinity | 7.8 | University of Oxford 8.0 | 1.0 |
| Style & Aesthetic | Attraction | 7.5 | University of Oxford 8.0 | 1.0 |
| Reliability & Consistency | Affinity | 8.3 | University of Oxford 9.0 | 1.0 |
| Trust & Credibility | Affinity | 9.2 | University of Oxford 10.0 | 1.5 |
Does desire track being recommended?
Rank correlation between a brand's driver index and the share of answers naming it, across the 7 indexed brands in this category. A relationship inside one run's answers — descriptive, never causal, and easily moved by a single brand at this sample size.
Method & limitations
Every week, each category runs a fixed set of unbranded consumer prompts against ChatGPT, Claude and Gemini. A separate model reads the answers and scores every brand named in them across 16 perception dimensions, 0–10. Those dimensions are grouped into the three Drivers of Desire from the Havas Science of Desire framework, and each brand's driver score is indexed against the average brand in its own category — 100 is the category average, 115 is fifteen per cent above it. The Vs all categories switch re-indexes the same scores against the average of every indexed brand across every category, so boards can be read against each other; the ranking inside a category is the same either way. Each category's own average, indexed against the whole index, is its place in the category table. The summary beside each brand describes where it stands on the three drivers against whichever benchmark is showing: above 104 counts as ahead, below 96 as behind.
This measures how AI assistants describe brands in their answers. It is not a survey of people, and it is not an endorsement: the scores are a reading of machine-generated text at the time of the run, and relationships between drivers and how often a brand is named are correlational. A brand needs at least six scored dimensions in a run to be indexed, and a category needs at least four such brands to get a board. See the full methodology.
Read the category report
Which universities brands the models recommend, the sources they trust and how often a brand page is read but not cited: Universities · The Category Report.
Other categories
Where next
This edition measures what AI wants from these brands. These answer what comes next — which brands AI names, which websites feed those answers, and how any of it is counted.