Automotive,
sub-category by sub-category
3 automotive categories, each asked the questions real UK consumers ask, every month. Do they behave like one market or several? On the measures below, Automotive behaves like 3 different markets.
They agree on leader visibility. They split on concentration.
Each strip is one measure. The grey band is the programme's full range; the gold span is where Automotive'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 | Used Cars | 89% | 28.4 | 14.6 | 1.95 |
| Electric Cars | 31% | ||||
| Car Leasing | 26% | ||||
| Leader visibility | Electric Cars | 38% | 15.3 | 22.7 | 0.67 |
| Car Leasing | 7% | ||||
| Used Cars | 5% | ||||
| Read → cited | Used Cars | 50% | 12.1 | 11.8 | 1.03 |
| Car Leasing | 49% | ||||
| Electric Cars | 24% | ||||
| Model agreement | Electric Cars | 100% | 40.8 | 34.1 | 1.20 |
| Used Cars | 50% | ||||
| Car Leasing | 0% |
3 crowns, 3 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 3 leaders are upsets: not the market leader the programme seeded.
3 sources referee more than one automotive category.
The independent sources AI cites in the answers — counted only where they appear in the top sources of at least two of Automotive's sub-categories. gov.uk is trusted across 3 of 3: the place a automotive brand needs to be seen whatever it sells.
Brand pages convert 50% of reads in used cars, 24% in electric cars.
When Claude or ChatGPT opens a brand's own page while answering, how often does it make the cited sources? The spread inside Automotive is as wide as the programme's: what gets cited differs sub-category by sub-category.
Every automotive 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 Automotive 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 05:13:37 GMT.