How AI wants payments & bnpl
Payments & BNPL is decided on attraction: Apple Pay indexes 120 there against Skrill at 78, a 42-point spread. Apple Pay holds the strongest overall profile at 116 — complete desire. But the brand assistants name most often is Apple Pay, not the category's market leader: being desired and being named are not the same measurement. Across these 24 brands, attraction tracks being named most closely (rank correlation 0.76).
24 brands · 60 AI answers · 360 dimension scores · measured, never estimated
Payments & BNPLupset
| # | Brand | Attraction | Affinity | Attachment | Desire shape | Named in |
|---|---|---|---|---|---|---|
| 1 | Apple Pay | 120 | 116 | 114 | Complete desire | 55% |
| 2 | Mastercard | 107 | 115 | 111 | Complete desire | 7% |
| 3 | Stripe | 118 | 106 | 107 | Complete desire | 12% |
| 4 | Visa | 107 | 115 | 111 | Complete desire | 7% |
| 5 | Revolut | 118 | 101 | 107 | Performance brand | 32% |
| 6 | Google Pay | 107 | 106 | 110 | Complete desire | 55% |
| 7 | Wise | 110 | 103 | 107 | Performance brand | 23% |
| 8 | Monzo | 111 | 101 | 105 | Performance brand | 22% |
| 9 | Klarna | 114 | 98 | 102 | Magnetic but unbought | 7% |
| 10 | Starling Bank | 106 | 103 | 102 | Magnetic but unbought | 17% |
| 11 | Adyen | 103 | 101 | 102 | Category default | 5% |
| 12 | PayPal | 100 | 108 | 102 | Quietly trusted | 45% |
| 13 | Samsung Pay | 103 | 102 | 102 | Category default | 13% |
| 14 | Venmo | 98 | 98 | 98 | Category default | 0% |
| 15 | ClearPay | 98 | 91 | 97 | Magnetic but unbought | 3% |
| 16 | Cash App | 98 | 91 | 94 | Magnetic but unbought | 7% |
| 17 | Payoneer | 92 | 96 | 95 | Fading | 3% |
| 18 | Amazon Pay | 91 | 95 | 94 | Fading | 5% |
| 19 | Zilch | 99 | 85 | 94 | Magnetic but unbought | 2% |
| 20 | Barclaycard | 81 | 103 | 93 | Loved, not chosen | 7% |
| 21 | Santander | 81 | 101 | 93 | Loved, not chosen | 0% |
| 22 | Worldpay | 82 | 96 | 91 | Fading | 3% |
| 23 | Neteller | 78 | 85 | 85 | Fading | 0% |
| 24 | Skrill | 78 | 85 | 85 | Fading | 2% |
Indexed against the payments & bnpl average of 24 brands (100 = the category), 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
Real separation, but not the category’s defining contest.
Attachment
The tightest of the three: most brands land close together here.
The shapes AI leaves these brands in
Three indices make a profile, and profiles fall into recognisable shapes. A brand that is magnetic but unbought has a different problem from a habit nobody loves — and a different thing to build next.
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 |
|---|---|---|---|---|
| Heritage & Authority | Affinity | 7.1 | Mastercard 9.5 | 5.0 |
| Style & Aesthetic | Attraction | 6.8 | Apple Pay 9.0 | 4.0 |
| Lifestyle Fit & Identity | Affinity | 6.8 | Apple Pay 8.7 | 3.7 |
| Boldness & Creativity | Attraction | 6.9 | Klarna 8.5 | 3.5 |
| Differentiation vs Sameness | Attraction | 7.2 | Revolut 9.0 | 3.5 |
Where everyone reads alike
| Dimension | Driver | Category avg | Strongest | Spread |
|---|---|---|---|---|
| Trust & Credibility | Affinity | 8.0 | Apple Pay 9.0 | 2.0 |
| Reliability & Consistency | Attachment | 8.0 | Adyen 9.0 | 2.0 |
| Ease of Use & Onboarding | Attachment | 8.0 | Apple Pay 9.0 | 2.5 |
| Value & Pricing | Attachment | 7.3 | Revolut 9.0 | 3.0 |
| Sustainability & Ethics | Affinity | 6.4 | Starling Bank 8.0 | 3.0 |
Does desire track being recommended?
Rank correlation between a brand's driver index and the share of answers naming it, across the 24 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 of every scored brand in its category — 100 is the category, 115 is fifteen per cent above it.
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.
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.