chatR LLM Tracking

People are talking to AI…
what is it saying about you?

Consumers are increasingly relying on AI tools to to inform and guide their decision-making process across discovery, research, product comparisons, and purchase. Vital Findings’ LLM tracking gives you insight into this evolving world of AI conversations. Innovated by tracking experts, chatR is a proprietary market research tool that measures how LLMs – individually and in aggregate – recommend and describe your brand when consumers engage with LLMs at any part of their decision journey.

Learn more about ChatR

How it works

Define Relevant Personas

Short definitions of your key marketing segments, steered by relevant attitudes, demographics, and your product focus areas

Generate Real World Conversations

Actual questions each persona would ask an AI assistant when shopping ie. Which shoes are best for running on pavement?

Track Your Presence

Responses are quantified: which brands are recommended, who’s first, how is each brand characterized, and what sources are AIs using to answer

Improve Your Positioning

Uncover sources LLMs are using for their recommendations and messaging improvements to improve your brand’s performance across LLMs

What you can do with it

Understand & track your position across scenarios

Identify messaging or product gaps showing in market

Develop strategies to shift the conversation

Case Study

The Opportunity

A footwear company wanted to understand their performance as a Lifestyle shoe, overall and among 18-24 year olds. Leveraging our LLM Tracking solution – chatR – we were able to provide a clear gap in presence across all ages, but particularly among 18-24 year olds in the Lifestyle category. With this gap identified we elevated messaging and source opportunities for the brand to action on immediately.

Our Approach

First, we defined Core themes underpinning Lifestyle shoe decisions:

  • Everyday Comfort
  • Style & Versatility
  • Performance Cushioning
  • Trends & Cultural

Then, we generated a variety of context driven real-world questions that consumers ask LLMs about Lifestyle shoes. For example:

  • Everyday Comfort Question: Looking for comfortable sneakers I can wear all day — what do you recommend?
  • Style & Versatility Question: What sneakers do you recommend that go with pretty much everything?

A variety of questions were then posed to the 4 most popular AI models, with and without an age filter. Results showed a clear gap in Style & Versatility and clearly lagging brand leaders in Trends & Culture, and this was even more pronounced among 18-24 year olds.

The Results

Based on the results and rationale across AI chats, concrete recommendations were made that address different aspects of the lagging performance:

    1. Seed the market with the desired info. There is a notable absence on the internet for styling the client brand, outfit or occasion versatility, and an absence from trends. AI answers from training data or by searching. The internet must contain something for AI to retrieve it.
    2. Target high impact sources. LLMs pull from specific sources – which we can see. We identified the specific sources LLMs are pulling from so that marketing can target and seed these high impact sources
    3. Strategic decision about the product. For consistently winning brands, AI noticed a pattern. The client brand’s current portfolio does not fit into that pattern. This departure is not breaking through in style or culture at a level that impacts recommendations.