Search Intent Tool Using Vector Embeddings

Description

What does a search intent tool look like when it uses vector embeddings instead of keyword matching? In this clip, Robin Tully demos forecast.ing's free Query2Vector tool, showing how it takes your Google Search Console export and clusters queries into prioritized topics by meaning. You'll see: - How raw GSC queries like "payroll provider comparison" and "learning payroll processing" get grouped into parent topics despite different wording - How the tool rolls up impressions, clicks, and CTR across all queries in a topic to show aggregate demand - How the actual vector embeddings are displayed alongside the human-readable cluster visualization - Why ChatGPT and Claude default to keyword matching and TF-IDF when you ask them to cluster queries, and how a structured embedding process is different This clip accompanies our full article: Search Intent Tool https://forecast.ing/solutions/keyword-research-techniques/search-intent-tool forecast.ing is a content marketing research platform that helps content teams find, prioritize, and produce content using demand signals across search, social, news, competitors, and AI citations. Analyze your GSC query data for free using our Query2Vector tool: https://forecast.ing/tools/query2vector