From Visibility to Influence: How Award-Winning LLM Platforms Shape Buyer Decisions

From Visibility to Influence: How Award-Winning LLM Platforms Shape Buyer Decisions 61.3%. That’s the average brand mention rate across the 2,361 AI prompts we’ve run on platforms like ChatGPT, Google AI Overview, and Perplexity. It means that, on any given query, there’s nearly a 40% chance your brand isn’t even in the conversation. Let that sink in for a second. If you’re invisible in these systems, you’re invisible where decisions start. But here’s the thing: visibility is just the starting line. It’s one thing to show up, quite another to influence the outcome. And that’s where the real game is played. Buyer decision-making AI isn’t just a directory of who’s who; it’s shaping what people choose in the moment that counts. That’s why the focus has to shift from visibility alone to LLM visibility influence . If you’re not influencing, you’re losing. In this piece, we’re going to unpack how leading LLM platforms like ChatGPT are deciding what matters, why some brands are getting recommended consistently, and what you can do to ensure your business doesn’t get left behind. I’ll also share some eyebrow-raising data from our experiments at Contxt and point out what surprised me. Let’s dive in. Person using AI system on laptop Visibility Basics: The Floor, Not the Ceiling Let’s start with the basics. Across our experiments, the mention rate for brands varied wildly depending on the platform. ChatGPT led the way with a 74.4% mention rate, compared to 43.5% on Google AI Overview and 42.7% on Perplexity. The gap is glaring, and it tells us something important: being visible on one platform doesn’t guarantee visibility on another. In fact, our data shows that 39% of the time, ChatGPT and Google AI give entirely different recommendations for the same prompts. Think about that. Nearly 4 in 10 times, a brand visible on one system was completely invisible on the other. Here’s my read on this data: visibility is fragmented. You can’t assume that being optimised for one LLM means