Most SEO professionals give Google too much credit. We assume Google understands content the way we do — that it reads our pages, grasps nuance, evaluates expertise, and rewards quality in some deeply intelligent way. The DOJ antitrust trial told a different story.

Under oath, Google VP of Search Pandu Nayak described a first-stage retrieval system built on inverted indexes and postings lists, traditional information retrieval methods that predate modern AI by decades. Court exhibits from the remedies phase reference “Okapi BM25,” the canonical lexical retrieval algorithm that Google’s system evolved from. The first gate your content has to pass through isn’t a neural network. It’s word matching.

Google does deploy more advanced AI further down the pipeline, including BERT-based models, dense vector embeddings, and entity understanding systems. But those operate only on the much smaller candidate set traditional retrieval produces. We’ll walk through where each technology enters the process.

This matters for content optimization tools like Surfer SEO, Clearscope, and MarketMuse. Their core methodology — a mix of TF-IDF analysis, topic modeling, and entity evaluation — maps directly to how that first retrieval stage scores documents. The tools are built on the right foundation. The problem is that most people use them incorrectly, and the studies backing them have real limitations.

Below, I’ll explain how first-stage retrieval works and why it still matters, what the research on content scoring tools actually shows — and doesn’t show — and most importantly, how to use these tools to produce content that earns its way into the candidate set without wasting time chasing a perfect score.

How first-stage retrieval works and why content tools map to it

Best Matching 25 (BM25) is the retrieval function most commonly associated with Google’s first-stage system. 

Nayak’s testimony described the mechanics it formalizes: an inverted index that walks postings lists and scores topicality across hundreds of billions of indexed pages, narrowing the field to tens of thousands of candidates in milliseconds. 

Here’s what matters for content creators:

• Term frequency with saturation: The first mention of a relevant term captures roughly 45% of the maximum possible score for that term. Three mentions get you to about 71%. Going from three to thirty adds almost nothing. Repetition has steep diminishing returns.
• Inverse document frequency: Rare, specific terms carry more scoring weight than common ones. “Pronation” is worth roughly 2.5 times more than “shoes” in a running shoe query because fewer pages contain it.
• Document length normalization: Longer documents get penalized for the same raw term count. All of these scoring algorithms are essentially looking at some degree of density relative to word count, which is why every content tool measures it.
• The zero-score cliff: If a term doesn’t appear in your document at all, your score for that term is exactly zero. Not low. Zero. You’re invisible for every query containing it.

That last point is the single most important reason content optimization tools have value. If you write a comprehensive rhinoplasty article but never mention “recovery time,” you score zero for that entire cluster of queries, regardless of how good the rest of your content is. 

Google has systems like synonym expansion and Neural Matching — RankEmbed — that can supplement lexical retrieval and surface additional documents. But counting on those systems to rescue a page with vocabulary gaps is a risky strategy when you can simply cover the term.

After first-stage retrieval, the pipeline gets progressively more expensive and more sophisticated. RankEmbed adds candidates keyword matching missed. Mustang applies roughly 100+ signals, including topicality, quality scores, and NavBoost — accumulated click data over 13 months, described by Nayak as “one of the strongest” ranking signals. 

DeepRank applies BERT-based language understanding to only the final 20 to 30 results because these models are too expensive to run at scale. The practical implication is clear: no amount of authority or engagement signals helps if your page never passes the first gate. Content optimization tools help you get through it. What happens after is a different problem.

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What the research on content tools actually shows

Three major studies have examined whether content tool scores correlate with rankings: Ahrefs (20 keywords, May 2025), Originality.ai (~100 keywords, October 2025), and Surfer SEO (10,000 queries, July 2025). All found weak positive correlations in the 0.10 to 0.32 range.

A 0.24 to 0.28 correlation is actually meaningful in this context. But these numbers need serious qualification. Every study was conducted by a vendor, and in every case, the vendor’s own tool performed best. 

No study controlled for confounding variables like backlinks, domain authority, or accumulated click data. The methodology is fundamentally circular: the tools generate recommendations by analyzing pages that already rank in the top 10 to 20, then the studies test whether pages in the top 10 to 20 score well on those same tools.

The real question — whether following tool recommendations helps a new, unranked page climb — has never been rigorously tested. Clearscope’s Bernard Huang put it directly: “A 0.26 correlation is not the brag they think it is.” 

He’s right. But a weak positive correlation is exactly what you’d expect if these tools solve the retrieval problem — getting into the candidate set — without solving the ranking problem — beating competitors once there. Understanding that distinction is what makes these tools useful rather than misleading.

Why not skip these tools altogether?

Expert writers are terrible at predicting how their audience actually searches. MIT Sloan’s Miro Kazakoff calls it the curse of knowledge. Once you know something, you forget what it was like before you knew it. 

Clearscope’s case study with Algolia illustrates the problem precisely. Algolia’s writers were technical experts producing genuinely excellent content that sat on Page 9. The problem wasn’t quality. The team was using internal jargon instead of the language their audience actually typed into Google. 

After adopting Clearscope, their SEO manager Vince Caruana said the tool helped the organization “start writing for our audience instead of ourselves” by breaking out of internal vocabulary. Blog posts moved from Page 9 to Page 1 within weeks. Not because the writing improved, but because the vocabulary finally matched search behavior.

Google’s own SEO Starter Guide acknowledges this dynamic, noting that users might search for “charcuterie” while others search for “cheese board.” Content optimization tools surface that gap by showing you the actual vocabulary of pages that have already demonstrated retrieval success. 

You can do everything a tool does manually by reading top results and noting common themes, but the tools automate hours of SERP analysis into minutes. At $79 to $399 per month, the investment is justified when teams publish frequently in competitive niches or assign work to freelancers lacking domain expertise. For a solo blogger publishing once or twice a month, manual analysis works fine.

What about AI-powered retrieval?

Dense vector embeddings are the same core technology behind LLMs and AI-powered search features. They compress a document into a fixed-length numerical representation and can match semantically similar content even without shared keywords. Google uses them via RankEmbed, but they supplement lexical retrieval rather than replace it.

The reason is computational: A 768-dimensional embedding can preserve only so much information, and research from Google DeepMind’s 2025 LIMIT paper showed that single-vector models max out at roughly 1.7 million documents before relevance distinctions break down — a small fraction of Google’s index. Multiple studies, including findings on the BEIR benchmark, show hybrid approaches combining BM25 with dense retrieval outperform either method alone.

The bottom line for practitioners is clear: The AI layer matters, but it sits lower in the pipeline, and the traditional retrieval stage your content tools map to still does the heavy lifting at scale.

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How to actually use content scoring tools

This is where most guidance on content tools falls short. The typical advice is “use Surfer/Clearscope, get a high score, rank better.” 

That misses the point entirely. Here’s a framework built on how these tools actually intersect with Google’s retrieval mechanics.

Prioritize zero-usage terms over everything else

The highest-leverage action these tools identify is a term with zero mentions in your content. That’s a term where your retrieval score is literally zero, and you’re invisible for every query containing it. Going from zero to one mention is the single most impactful edit you can make. Going from four mentions to eight is nearly worthless because of the saturation curve.

When reviewing tool recommendations, filter for terms you haven’t used at all. Clearscope’s “Unused” filter does this explicitly. 

Ask yourself: Does this missing term represent a subtopic my audience would expect me to cover? If yes, work it in naturally. If the tool suggests a term that doesn’t fit your angle — a beginner’s guide doesn’t need advanced technical terminology — skip it. 

A high score achieved by forcing irrelevant terms into your content is worse than a moderate score with genuinely useful writing. As Ahrefs noted in its 2025 study, “you can literally copy-paste the entire keyword list, draft nothing else, and get a high score.” That tells you everything about the limits of chasing the number.

Be selective about which competitor pages you analyze

Default settings on most tools pull from the top 10 to 20 ranking pages, which frequently includes Wikipedia, major media outlets, and enterprise sites with overwhelming domain authority. These pages often rank despite their content, not because of it. Their term patterns reflect authority advantage, not content quality, and they’ll skew your recommendations.

A better approach: Look for pages that rank for a high number of organic keywords on mid-authority domains. 

Ahrefs’ data shows the average page ranking No. 1 also ranks in the top 10 for nearly 1,000 other keywords. A page ranking for 500 keywords on a DR 35 site has demonstrated broad retrieval success through vocabulary and topical coverage, not just backlinks. Those pages contain term patterns proven effective across hundreds of separate retrieval events, not just one. 

In most tools, you can manually exclude specific URLs from competitor analysis. Remove the Wikipedia pages, the Amazon listings, and any high-authority site where you know authority is doing the work. What’s left gives you a much cleaner picture of what content actually needs to include.

Use tools during research, not during writing

The worst workflow is writing with the scoring editor open, watching your number tick up in real time. That pulls your attention toward keyword insertion instead of communicating expertise. Practitioners reporting the worst experiences with these tools tend to be the ones writing to a live score.

The better workflow: Run the tool first. Review the term list. Identify gaps in your outline, especially terms with zero usage that represent subtopics you should cover. Then close the tool and write for your reader. 

Run it again at the end as a sanity check. Did you miss any major subtopics? Add them. Is the score significantly lower than competitors? That’s information worth investigating. But your job is to build the best page on the internet for this topic, not to match a number.

Understand that content is one player in the game

NavBoost, RankEmbed, PageRank-derived quality scores, site authority, click data, and engagement signals all operate on the candidate set that first-stage retrieval produces. Content optimization gets you through the gate. It doesn’t win the race. 

If you optimize a page, push the score to 90, and don’t see ranking improvements, that doesn’t mean the tool failed. It likely means the other ranking factors — backlinks, domain authority, and click signals — are doing more work for your competitors than content alone can overcome.

This is especially important when scoping on-page optimization projects. Be honest about what content changes can and can’t accomplish. If a page is on a DR 15 domain competing against DR 70+ sites, perfect content optimization is necessary but probably not sufficient. 

When a client asks why they’re not ranking after you pushed their score to 95, the answer shouldn’t be “we need more content.” It should be a clear explanation of which part of the problem content solves — retrieval — which parts it doesn’t — authority, engagement, brand — and what the next strategic move actually is.

Focus on going beyond, not just matching

The philosophy behind these tools — structure your content after what top results cover — is sound. You need to demonstrate topical relevance to enter the candidate set. But the goal isn’t to produce another version of what already exists.

The pages that rank broadly, the ones that show up for hundreds or thousands of keywords, consistently do more than match the competitive baseline. They add original research, practitioner experience, specific examples, or angles the existing results don’t cover.

Surfer SEO’s December 2024 study supports this. It measured “facts coverage” across articles and found that top-performing content by keyword breadth had significantly higher coverage scores than bottom performers.

The content that ranks for the most queries doesn’t just include the right terms. It includes more information, more specifically. Use the tool to establish the floor of topical coverage. Then build the ceiling with value the tool can’t measure.

A note on entities

Google’s Knowledge Graph contains an estimated 54 billion entities. Entity understanding becomes most powerful in the later ranking stages where BERT and DeepRank process final candidates. 

Some content tools are starting to incorporate entity analysis, but even the best versions present entities as flat keyword lists, missing the relationships between entities that Google’s systems actually evaluate. 

Knowing that “Dr. Smith” and “rhinoplasty” appear on your page is different from understanding that Dr. Smith is a board-certified surgeon with published research at a specific institution. That relational depth is what Google processes, and no content scoring tool currently captures it. 

Treat entity coverage as an additional layer beyond what keyword-focused tools measure, not a replacement for the fundamentals.

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Retrieval before ranking

Content optimization tools work because they’ve reverse-engineered the vocabulary of the retrieval stage. That’s a less exciting claim than “they’ve cracked Google’s algorithm,” but it’s the honest one, and it’s supported by what the DOJ trial revealed about Google’s infrastructure.

Use these tools to identify missing terms and subtopics. Be skeptical of exact frequency targets. Exclude high-authority outliers from your competitor analysis. Prioritize zero-usage terms over further optimization of terms you’ve already covered. 

Understand that a perfect content score addresses one stage of a multi-stage pipeline and use the competitive baseline as your floor, not your ceiling. The content that ranks the broadest isn’t the content that best matches what already exists. It’s the content that covers what already exists and then goes further.