Observed Signal · Oct 1, 2026 · Market Signal · Source: Jellyfish · Impact: 4/5
Jellyfish Launches Share of Model™ Shopping Optimization
AI Shopping upends the rules of brand leadership, price positioning and competition, according to new Jellyfish research. These insights were made possible by Shopping Optimization, a new capability within Jellyfish’s Share of Model™ Platform, that gives brands a clear understanding of the factors driving AI recommendations, so they can focus their optimization efforts and measure the business impact.
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Where brands stand when AI agents do the shopping
New Jellyfish research uncovers how AI is reshaping the shopping journey and which brands are actually showing up on the AI shelf. Powered by new Shopping Optimization capabilities within our proprietary Share of Model™ Platform, the analysis reveals what drives AI recommendations at the product level.
Jellyfish Research: AI Shopping Reshapes Brand Competition
New research from Jellyfish, a global digital marketing company within The Brandtech Group, reveals that AI shopping assistants are dramatically reshaping competitive dynamics. The Share of Model™ tool analyzed AI recommendations across eight categories in the US, UK, Australia, and Singapore, spanning ChatGPT, Google AI Mode, and Amazon Alexa. Key findings show there is no single 'AI shelf'—different assistants recommend vastly different brands and products for the same query. AI can erase brand leadership, as seen in US fashion where the top brand held only 7% of the shelf across 120 brands. AI also ignores traditional price positioning, with men's suits in the UK ranging from £27 to £3,295 in one response. The company launched Shopping Optimization, a new capability within Share of Model, enabling marketers to analyze AI recommendations at the SKU level and optimize for agentic commerce.
AI Shopping Revolutionizes Attribution: Focus on Brand-Building
The article argues that AI shopping agents and large language models are upending traditional digital attribution by reducing the visibility of conventional conversion signals and shifting the emphasis from last-click performance to broader brand-building. As AI curates shopping experiences, the classic multi-touch attribution models lose inputs like organic search clicks, affiliate links, and retargeted ads. Marketers are urged to design for distinctive brand assets and retool attribution to incorporate brand data and LLM-related signals, while also monitoring new machine-driven signals such as LLM mentions. The piece contends that brand-building becomes more important in a world where AI-assisted commerce prevails, calling for long-term investment in durable brand equity and the adaptation of measurement approaches to reflect AI-enabled consumer journeys.
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