Multi-signal cannibalization detection — not just title matches.

Keyword Cannibalization

Keyword cannibalization occurs when multiple pages on your site compete for the same search queries, splitting clicks and confusing search engines about which page to rank. Simple title-matching tools miss most cannibalization. EchoBat uses four independent signal layers — GSC query overlap, content similarity (simhash), metadata collisions, and internal link equity splits — and only flags issues when at least two signals agree, dramatically reducing false positives.

How It Works

EchoBat computes simhash fingerprints during content extraction, extracts metadata for collision detection, analyzes internal link topology for equity splits, and (when connected) pulls GSC query data to identify ranking overlap. The Cannibalization lens runs all four detection layers independently, then cross-references results — only issues confirmed by at least two signals are surfaced. This multi-signal approach dramatically reduces false positives compared to single-signal tools.

Proof Returned in the Report

Every Keyword Cannibalization finding is tied to crawl evidence: affected URLs, the source signal, severity, score impact, and the next action exposed in the portal, CLI JSON, and MCP tools.

Sample Evidence Fields

  • GSC Query Overlap: Multiple pages ranking for the same Search Console queries, splitting impressions and clicks.
  • Content Similarity: Near-duplicate content detected via simhash analysis — pages too similar in body text.
  • Metadata Collision: Identical or near-identical page titles and meta descriptions competing for the same keywords.

Why It Matters

  • Multi-signal detection catches real cannibalization, not just title matches
  • False positive reduction: 2+ signals must agree before flagging
  • Actionable: shows exactly which pages compete and on which queries
  • Works with or without GSC data (degrades gracefully)

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