Capture richer SERP features and build a defensible topical moat by maximizing Entity Salience Scores across your revenue-driving pages.
Entity Salience Score reflects how strongly Google’s NLP model associates a specific entity with your page, directly influencing its ability to rank for that entity’s queries and appear in rich result panels. Monitor the score via the Natural Language API and raise it by tightening on-page focus (clear headings, repeated but natural mentions, supporting entities, schema markup), ensuring priority pages send an unmistakable topical signal.
Entity Salience Score is the confidence value (0-1) Google’s Natural Language model assigns to an entity to reflect its topical dominance within a document. A score above ~0.15 signals to Google Search, AI Overviews, and third-party LLMs that your page is a primary source on that entity, unlocking richer SERP features (Knowledge Panels, “About this result”) and higher query match rates. For brands, that translates into greater visibility for money terms without relying on backlinks alone.
curl -X POST https://language.googleapis.com/v1/documents:analyzeEntities
; log salience
values.alt
, and meta description.schema.org/Thing
, Product
, or Organization
markup. Include sameAs
links to authoritative graphs (Wikidata, Crunchbase).E-commerce: A big-box retailer raised “Dyson V15 Detect” salience from 0.09 to 0.27 by adding comparison tables and FAQ schema. Result: +18 % non-brand revenue in 6 weeks.
SaaS: After optimizing whitepapers (salience 0.04 → 0.19 for “zero-trust network”), a cybersecurity vendor earned a Featured Snippet and saw MQLs rise 22 % QoQ.
Salience measures how central an entity is to the overall passage. A score of 0.03 indicates Google sees “fiber optic internet” as marginal in the text, whereas 0.19 shows the piece is really about “router.” Google therefore thinks the article focuses on hardware, not service providers. First step: rewrite or expand sections to place “fiber optic internet” at the core—e.g., add a definition, pricing tables, provider comparisons—so the entity appears in headings, opening paragraph, and summary. That should lift its salience and align the topic with the target query.
Keyword density counts exact-match occurrences, ignoring context. Entity salience score weighs how important a recognized entity is to the passage based on position (title, intro, headings), syntactic relationships, and co-occurrence with related entities. Google favors salience because it resists spam: repeating a word 30 times might raise density but won’t lift salience if the term is peripheral to the main ideas. Salience also supports entity-based indexing and Knowledge Graph connections, giving Google richer signals for relevance and intent than raw term frequency.
While backlinks are important, for an informational query Google weighs topical relevance heavily. Article A’s higher salience (0.21) tells Google the piece is centrally about electric vehicles, whereas Article B’s lower score suggests diluted topical focus despite stronger link equity. Given comparable authority signals, Article A is more likely to rank because its content clearly centers on the entity matching the query intent. Raising Article B’s salience (e.g., reducing off-topic sections) would make its backlinks work harder.
Create or improve dedicated pages for each related entity. Then, inside the article, link the first in-context mention of “lithium-ion battery,” “regenerative braking,” and “charging station” to those pages using descriptive anchor text. Anchor placement and proximity to key passages elevate each entity’s prominence. This two-way reinforcement (contextual link + authoritative target page) encourages Google to treat these entities as integral subtopics, raising their salience scores and signalling a well-structured topical cluster.
✅ Better approach: Use salience as a comparative signal: run competitor URLs through the same NLP API, note the primary entities Google deems most salient, and adjust your content to close obvious topical gaps rather than force-feeding keywords. Track resulting organic traffic, not just the score.
✅ Better approach: Limit each target entity to natural, context-rich mentions. Replace duplicate phrases with synonyms or pronouns, and fold entities into descriptive sentences that answer user intent. Run a blind read-through: if a non-SEO colleague flags it as awkward, rewrite.
✅ Better approach: Cross-check with multiple NLP services (e.g., OpenAI, Cohere) and export salience data from 5–10 top-ranking pages. Build a simple spreadsheet to surface common high-salience entities across winners, then prioritize those entities in your own outline.
✅ Better approach: Add appropriate schema (e.g., Product, FAQ, HowTo) referencing the same entities, and create hub pages with consistent anchor text pointing to the article. This site-wide reinforcement often moves the needle more than tweaking another mention or two on a single page.
Measure SGE Click Share to forecast AI-driven traffic shifts, prioritize …
Exploit daily SERP volatility to hedge 30% traffic risk, time …
Single metric exposing revenue-draining pages, steering dev sprints to high-ROI …
Activate a single elite citation to ignite cascading backlinks, cutting …
Understand how zero-click share skews traffic forecasts, revealing hidden competition …
Reinforce priority entities to capture rich results, boost CTR by …
Get expert SEO insights and automated optimizations with our platform.
Start Free Trial