Listen: LLM-Based Search Volume Prediction
An analysis comparing Google Gemini's keyword volume predictions against actual Google Search Console data reveals weak-to-moderate correlation and limited accuracy.
Transcript
Can your favorite large language model accurately estimate search volumes? The short answer is no, but it does have a general idea.
We put Google’s Gemini to the test, comparing its monthly search volume predictions to actual data from Google Search Console. We ran top-performing queries through Gemini and matched them against real search impression data.
What we found is that the direct correlation is weak. The artificial intelligence is much better at ranking keywords from high to low than predicting exact numbers. When we grouped the search volumes into five categories, from very low to very high, Gemini only got the exact category right about thirty-five percent of the time. However, it was in the right ballpark—either spot-on or just one category off—nearly seventy percent of the time.
This discrepancy happens because Google Search Console reflects your site's actual visibility and ranking, while the AI relies on broad, web-scale patterns.
The takeaway is simple. Use artificial intelligence for direction, not precision. It is great for spotting big versus small topics and sorting opportunities into tiers, but it is no replacement for your real analytics.