New research from Jellyfish, a global digital marketing leader within The Brandtech Group, reveals that AI shopping is reshaping competitive dynamics in ways that marketers may not expect – with different AI assistants showcasing dramatically different sets of brands and products for the same purchase, collapsing traditional price tiers, and challenging established brand advantages.
Jellyfish’s Share of Model™ tool analyzed how AI shopping systems recommend products across eight categories – including fashion, athletic wear, men’s suits, home appliances and furniture – in the US, UK, Australia and Singapore, spanning ChatGPT, Google AI Mode and Amazon’s Alexa for Shopping. The findings show that an “AI shelf” diverges significantly from the retail or search landscapes that brands know:
- There is no single “AI shelf.”
Ask two assistants the same shopping question and you get two different shelf experiences. Across the eight categories, ChatGPT accounted for roughly 80% of AI product recommendations and Google’s AI Mode around 20% – a four-to-one gap that ranged from under three-to-one to nearly thirty-to-one (in home appliances, 97% versus 3%).
- AI can erase brand leadership.
A single AI shopping question surfaced as many as 290 competing brands. In US fashion, recommendations spanned 120 brands, yet the most-recommended brand held just 7% of the shelf –a category with no leader. Marketers can now see whether AI treats their category as a branded shelf or a commodity scramble.
- AI ignores price positioning.
AI shopping recommendations are also bringing in a broader set of products than a brand might typically compete with. In one AI response, the price range of products shown spanned from £27 to £3,295 for men’s suits in the UK; for gaming chairs in the US, $79 to $3,479. A premium product now sits one line below a budget alternative in the same recommendation.
- Winning on one assistant tells a brand little about its position on another.
Different assistants “shop” at a radically different number of stores. The number of retailers an assistant drew on before recommending ranged from effectively one to more than 170. Asked for toys, Amazon recommended products from 177 brands but sourced them almost entirely from a single store – itself – while, for the same request, ChatGPT drew on 24 retailers and Google’s AI Mode on 37. In US athletic wear, ChatGPT considered 171 retailers and Google’s AI Mode 126.
- The same request can produce a dramatically different field of choice.
Asked to recommend men’s suits in the UK, ChatGPT considered 30 retailers before answering; Google’s AI Mode considered four.
These insights were made possible by Shopping Optimization, a new capability within Jellyfish’s Share of Model tool, which enables marketers to analyze AI shopping behavior at the individual product level and understand how different AI systems evaluate, compare and recommend specific SKUs.
Read more: https://www.jellyfish.com/en-gb/news/jellyfish-launches-share-of-model-shopping-optimization/