For evaluating prompt volume and AI search tracking tools
Many AI visibility tools claim to measure how often people search inside ChatGPT or other LLMs. What they actually depend on is incomplete panel data scraped from Chrome extensions. This creates three structural issues:
This leads to prompt volume figures that are frequently inflated by 10 to 100 times compared to real demand signals.
Tools buy access to datasets collected from browser extensions installed on voluntary users’ machines. Data providers like Similarweb and DDMR estimate traffic patterns based on a sample
Can tell you: High-level directional usage across platforms based on raw text from a narrow segment of ChatGPT and Perplexity sessions.
Cannot tell you: Actual market-level prompt frequency, mobile behavior, app behavior, or representative usage.
Some datasets come from partnerships with consumer-facing security tools like antivirus and privacy software like Norton and McAfee. These panels provide visibility into a small slice of overall browsing behavior.
Can tell you: Very broad directional patterns based on a limited sample of user sessions.
Cannot tell you: True market volume, what people asked inside LLMs, or whether a query had commercial or evaluative intent. These panels are many orders of magnitude smaller than the web’s actual dataset, which leads to artificial scaling and large estimation errors for specific keywords.
Sources like Ahrefs and Semrush use clickstream panels and connected Google Search Console data to estimate Google search volume.
Can tell you: Real demand in traditional search due to less noisey data.