Every day, players ask AI assistants some version of the same question: which padel racket should I buy? The assistant answers in a few sentences and names two or three brands. Those few sentences are quietly becoming one of the most valuable pieces of shelf space in padel, and unlike a shop shelf, most brands have no idea whether they are on it.
We decided to stop guessing and measure it.
How we measure what AI recommends
Our published research takes a fixed set of realistic buyer questions, puts them to the main AI platforms repeatedly, and records every brand mention in every answer. Aggregated, that produces a share of voice: the percentage of recommendation slots each brand captures for a market. The result is a leaderboard of what assistants actually say, rather than what anyone assumes they say.
The first public edition, our report on padel racket brands in Spain, covers the most mature padel market in the world, with brand mentions measured across OpenAI, Perplexity, and Gemini.
What the data keeps showing
A few patterns repeat across questions and platforms, and they say a lot about how these systems work.
Concentration at the top. A handful of established names capture most of the mentions, and the long tail of smaller brands splits what remains. Assistants compress a crowded market into a short answer, and a short answer has no room for the twelfth brand.
Reviews drive the answer. The brands that dominate are the ones with deep coverage on review sites, comparison articles, and buying guides. Assistants lean on exactly that content when recommending products, so a brand’s AI visibility tends to track its footprint in written, crawlable reviews far more than its sponsorship budget.
Platforms disagree. A brand can rank strongly on one assistant and barely register on another, because each platform retrieves from different sources with different weightings. Averages hide this; brand teams need the per-platform view.
Question phrasing moves the podium. “Best racket for beginners” and “best racket for control” produce different brand mixes. Brands that publish content answering specific player needs win the specific questions, which is where purchase intent is highest.
Why this matters more every month
A recommendation from an assistant lands at the exact moment of decision, with no competing banner ads and no page two. The player who asks “which racket should I buy for intermediate play” and gets three names has, in most cases, just formed a shortlist. Retailers tell us the effect is already visible in what customers walk in asking for. For a brand, being absent from those answers means being absent from a growing share of purchase decisions, silently.
The same logic applies further down the chain. A padel retailer whose buying guides are readable and cited becomes part of the answer itself, capturing buyers regardless of which brand wins the mention.
How a brand can respond
Start by knowing your number. The free Brand AI Visibility Checker reads our published report data and shows where a given brand stands against its competitors: its share of voice, its rank, and the gap to the leader.
Then work the inputs. Get your products into the comparison and review content assistants read. Publish spec and range pages that machines can parse cleanly. Answer the need-specific questions in text on your own domain: power versus control, beginner versus advanced, junior rackets, durability. Make sure your brand entity is consistent everywhere it appears, from your site to retailer listings.
Our equipment brands page covers the full approach, and the pattern from the club side of the market holds here too: the winners are rarely the biggest names by revenue. They are the most legible names.
The assistants have already ranked your market. The only question is whether you have looked at the ranking, and what you plan to do about your position on it.