Jellyfish Launches Share-Of-Model Solution To Track How LLMs Perceive Brands, Products & Services

Jellyfish has unveiled its Share of Model Platform – a first-of-its-kind solution that enables companies to analyse how different Large Language Models (LLMs) perceive their brands, products and services.



The new platform can identify whether or not brands are optimising their digital presence enough to prompt coveted recommendations from Gen AI models such as ChatGPT, Google’s Gemini and Meta’s Llama, when people tap into them for guidance.

In beta trials, the Share of Model Platform helped brands expand their keyword strategy, driving insight around adjusted website text and image assets to appeal more to each model. It was also successfully leveraged for competitive analysis, to understand how AI models perceive competitors and identify gaps in their strategy to dominate emerging opportunities.

“More than a billion people in 100+ countries engage with Google’s AI Overviews every month,” said Jack Smyth, chief solutions officer for AI, planning, and insights at Jellyfish.

“This marks a transformative moment in the influence and reach of Gen AI. LLMs are no longer just tools; they are a critical part of the customer journey and an audience in their own right. Our Share of Model™ Platform empowers brands to understand how LLMs perceive their category, competitors and content.”

Danone VP, global head of media and integrated brand communication, Catherine Lautier said: “LLMs will increasingly influence customer behavior, and the Share of Model platform enables us to track and compare each LLM’s perception. It gives us insights on how our brand ecosystems’ compare to competitor brands, what drives the interest of consumers online in a given category, and how to develop content on the different platforms analyzed by the different LLMs to stand out.”

And, speaking at a recent Google and The Brandtech Group event, Gokcen Karaca, head of digital and design at Pernod-Ricard-owned Chivas Brothers, which was part of the Share of Model Platform beta program, commented “The biggest challenge for brands is navigating the overwhelming number of choices consumers face when shopping online. Whether through e-retailers, Google Shopping, or even turning to LLMs to ask, ‘Which brand suits my taste profile?’, consumers are seeking clarity in a sea of options. As brands, we must ensure our products are represented in those critical AI-driven responses. Partnering with Jellyfish allows us to actively shape this new frontier and influence the answers our consumers receive. It’s an exciting journey to be on together.”

Results from a new YouGov study further confirm the urgent need for brands to understand how people are using AI tools and their impact and influence on purchase behaviors. Surveying 1,000 U.S. consumers between the ages of 18 – 65 the importance of Gen AI tools such as ChatGPT is shown to be significant.

The research shows that two-thirds (66%) of 18–24-year olds – aka Gen Z – report that they ask AI models for brand, product and service recommendations. More than half (51%) of 25-34-year-olds similarly turn to LLMs, with this usage dropping to 42% for those aged 35-44 and 31% for the 45-54 age group.

As users rely on these tools, their expectations for recommendations are also rising. Half (50%) of 18-24-year-olds expect AI tools to guide them to the best brands, products or services for them, with a slight decline to 47% among 25-34-year-olds, 35% for 35-44-year-olds, and further decline for those 44 and older.

Brandtech Group partner and head of emerging tech, Rebecca Sykes commented, “Essentially we are reverse-engineering a marketing approach this way, picking up signals from an entirely new ‘audience’, who’s seen everything you’ve ever made as a marketer and is showing you where the gaps are in your messaging. It’s one way in which we think marketing is going to be entirely disrupted – we’ll be prompted to make content when the signal strength is weak for a benefit/brand value/data point that we need to win in.”

Also publihed in: B&T