Search intent clustering is the practice of grouping the queries a site already ranks for by the intent behind them — what the searcher is actually trying to do — rather than by the words they happen to share. Do it well and every cluster maps cleanly to one page and one job; do it…
Detect keyword cannibalization programmatically from GSC API data, score each conflict by recoverable traffic, and decide what to consolidate or differentiate.
DuckDB, BigQuery, and pandas benchmarked on the same 25M-row GSC export. Which engine wins for which SEO workload, with working code and a decision framework.
Choosing an embedding model for SEO isn’t a leaderboard question. A practitioner’s framework for OpenAI vs Voyage vs Cohere vs BGE, with a reproducible AUC benchmark, the dimension trade-off, and the cost math at scale.
Spreadsheets break past a few hundred URLs. Use OpenAI embeddings, Search Console overlap, and an n8n workflow to flag cannibalization across thousands of pages, score severity, and pre-classify each pair as merge, differentiate, or canonical.