Observed Signal · May 5, 2026 · Report Release · Source: https://martechseries.com/feed/ · Impact: 3/5 · Sentiment: Positive
Jellyfish Report: Generative Engine Marketing Guides Brands
Jellyfish published a report titled "Brands in the AI Era: Generative Engine Marketing" (May 5, 2026) that defines Generative Engine Marketing (GEM) as a systematic framework for brands to be discoverable, understood and transacted with by large language models (LLMs) and AI agents. The report positions GEM as an end-to-end marketing system — building on Jellyfish’s Share of Model™ platform — that combines technical optimisation, content creation, pre-testing, distribution and measurement to make brand assets interpretable by AI and resonant with both human and machine audiences. Jellyfish cites client results (Gentle Monster and MSC Industrial) showing double-digit improvements in CTR, conversion and revenue after AI-driven optimisations. John Dawson, Vice President of Strategy at Jellyfish USA and co-author of the report, is quoted throughout explaining the need for brands to “train the models” that mediate customer journeys.
The report introduces a concrete framework (GEM) and tooling (Share of Model™) for brands to optimise for LLM-driven discovery and commerce; includes measurable client case studies showing significant performance uplifts, which could influence martech and agency strategies.
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Key Takeaways & Evidence Grounding
- Jellyfish published the report 'Brands in the AI Era: Generative Engine Marketing' on May 5, 2026.
- The report introduces Generative Engine Marketing (GEM) as a framework for making brands legible to LLMs and AI agents.
- Jellyfish's Share of Model™ Platform is presented as a capability to analyse how LLMs perceive brands, products and services.
- Gentle Monster reported a 17% improvement in click-through rate, over 14% uplift in conversion rate, and more than 39% improvement in return on ad spend after applying LLM insights to Google Performance Max campaigns.
- MSC Industrial reported a 45% increase in revenue in the first 30 days and a 758% incremental ROAS after implementing AI-driven optimisations to Performance Max campaigns.
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Brands Navigate AI Search: Visibility in a Chatbot Era
An interview on how brands can stay visible in the era of AI-driven LLM search. Jellyfish executives Frederic Derian and Victor Bastia discuss how chatbots reshape consumer search, reducing emphasis on traditional SEO and complicating attribution. Jellyfish claims its Share of Model tool automatically generates advertising optimisations by leveraging AI brand perception from AI models, connected to LLMs such as ChatGPT and Gemini, to keep brands searchable, visible, and shoppable in the generative-search ecosystem. The tool purportedly helps target niche terms identified by the LLMs, driving higher-quality traffic and conversions. Safeguards include human verification before applying changes, alignment with brand guidelines via Google Ads, and iterative refinement. The article notes risks like AI hallucinations and emphasizes semantic analysis across models. The outlook positions AI-driven brand perception as shaping future e-commerce and product visibility strategies.
Jellyfish Uses LLM Perception to Boost Brand Visibility
Jellyfish describes how its proprietary Share of Model tool uses large language models (LLMs) to analyse brand perception and automatically generate advertising optimisations. The tool connects to LLMs including ChatGPT and Gemini to continuously assess how models and underlying sources perceive a brand, surface keyword and sentiment opportunities, and suggest campaign adjustments. Jellyfish says recommendations are iteratively refined, aligned with clients' brand guidelines (sourced from Google Ads accounts), and always require human verification before application to avoid hallucinations or brand-messaging mismatches. The company reports that tapping LLM-derived perceptions has uncovered lower-cost, high-converting keyword pockets and improved traffic quality for some clients. Jellyfish notes brand perception can vary across different LLMs and positions the approach as a way for brands to remain searchable and shoppable in a generative search ecosystem.
Jellyfish Uses LLMs to Plan Ad Buys
Jellyfish, an agency owned by Brandtech Group, is promoting a product called "Share of Model" that uses large language models (LLMs) to inform media planning. The product measures how often LLMs mention a brand versus competitors, analyzes themes, sentiment and cultural context, and converts those insights into targeting signals for Google’s Performance Max (PMax) campaigns. Jellyfish cites a Project Management Institute (PMI) case where the approach drove a 20% lift in sales volume, a 45% increase in conversions and a 156% improvement in return on ad spend across a 90‑day campaign that concluded in early January. The article frames this as an example of brands moving from using AI assistants for discovery toward using LLM outputs to shape media strategy and targeting.
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