How AI wants discount stores

Discount Stores

Indexed against the average of the 17 indexed discount stores 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. 17 discount stores 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.

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.

Does desire track being recommended?

Rank correlation between a brand's driver index and the share of answers naming it, across the 17 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 discount stores brands the models recommend, the sources they trust and how often a brand page is read but not cited: Discount Stores · The Category Report.

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