How many keywords per 1000 words? Practical guidance for local SEO audits

Why auditors still measure 'keywords per 1000 words'

Audit context: when density checks help

Auditors measure keywords per 1000 words because it is a simple, repeatable way to compare pages. The metric lets teams spot pages with extreme under use or obvious repetition quickly, which can be useful during a content review cycle. For local projects this quick check can flag pages that need a closer look by intent or metadata.

Search engines now favour helpful, natural writing over mechanical repetition, so a density check is never a replacement for intent matching and structured signals. That principle comes from official guidance that treats keyword stuffing as a negative signal and recommends focusing on useful content Google Search Central.

The occurrence per 1000 words formula is a small instrument inside a larger search architecture. Use it to complement architecture work such as intent driven content organisation, technical SEO, and measurement that ties search to revenue. In audits it is a diagnostic, not a target.

Google's public guidance makes the core point: avoid keyword stuffing and focus on helpful content. That guidance implies there is no engine published density threshold to chase, and that natural phrasing matters more than exact match counts Google Search Central.

In practice the search quality systems reward pages that satisfy user intent and provide clear answers. For local pages that often means accurate location signals, clear service descriptions, and consistent NAP alongside coherent body copy. Editors can prioritise phrasing that reads well for users instead of forcing repeated exact matches.

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Use a clear formula auditors can reproduce: take the number of keyword occurrences, divide by the total word count, and multiply by 1000. That gives occurrences per 1000 words and makes comparisons fair across page lengths. The formula is (occurrences ÷ total words) × 1000, a simple arithmetic step auditors use to compare pages consistently Ahrefs Blog.

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  <div class="side-text"><p>Example methodology, described without invented performance data: pick the keyword you are auditing, count how many exact match occurrences appear in the body, count the words in the body, compute the ratio, and multiply by 1000. Report the result as the metric for that page. Use the same counting rules across the audit set so the numbers are comparable.</p></div>
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Decide in advance whether to count only exact matches or to include phrase variants and semantic terms. For many audits it is useful to report both an exact match occurrence and a semantic coverage note. Count headings and meta separately and report them as signals, not as part of the body density figure unless you explicitly define them into the scope.

What search engines actually say about keyword use

Google's guidance on keyword stuffing

Google's public guidance makes the core point: avoid keyword stuffing and focus on helpful content. That guidance implies there is no engine published density threshold to chase, and that natural phrasing matters more than exact match counts Google Search Central.

Why natural language matters more than exact-match counts

In practice the search quality systems reward pages that satisfy user intent and provide clear answers. For local pages that often means accurate location signals, clear service descriptions, and consistent NAP alongside coherent body copy. Editors can prioritise phrasing that reads well for users instead of forcing repeated exact matches.

The formula: how to calculate keywords per 1000 words

Step-by-step calculation

Use a clear formula auditors can reproduce: take the number of keyword occurrences, divide by the total word count, and multiply by 1000. That gives occurrences per 1000 words and makes comparisons fair across page lengths. The formula is (occurrences ÷ total words) × 1000, a simple arithmetic step auditors use to compare pages consistently Ahrefs Blog.

Example methodology, described without invented performance data: pick the keyword you are auditing, count how many exact match occurrences appear in the body, count the words in the body, compute the ratio, and multiply by 1000. Report the result as the metric for that page. Use the same counting rules across the audit set so the numbers are comparable.

How to handle phrases, variations, and semantic terms

Decide in advance whether to count only exact matches or to include phrase variants and semantic terms. For many audits it is useful to report both an exact match occurrence and a semantic coverage note. Count headings and meta separately and report them as signals, not as part of the body density figure unless you explicitly define them into the scope.

Many practitioners use ranges as a pragmatic guide rather than a rule. A commonly cited operational range for general content is about five to fifteen exact match occurrences per 1000 words, used to avoid under use and to discourage repetition Ahrefs Blog.

Choose a target range based on page purpose and intent. Informational, long form pages can tolerate wider semantic coverage while transactional or landing pages should be concise and place keywords in title, H1, and metadata. Treat the range as editorial balance and favour synonyms and related terms to cover topic breadth without repeating exact matches.

For local SEO pages many teams aim lower on exact match counts, usually around three to eight occurrences per 1000 words, combined with clear placement in title, H1, and metadata rather than pushing body density up arbitrarily Moz.

High impact local placement includes the title tag, H1, meta description, and structured data such as schema.org markup. Consistent NAP across site pages and directories reinforces location signals. For many local pages, improving these placements and adding schema is higher leverage than increasing body density.

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Use a compact checklist during audits. Steps include: apply the occurrences per 1000 words formula, inspect title and H1 for location, verify meta descriptions, run a TF IDF or semantic terms check, and confirm schema and NAP consistency.

Prioritise pages by user intent alignment, traffic and conversion potential, and business criticality. Pages with clear intent mismatch or missing local signals should come first. When in doubt, measure impressions and queries before performing broad rewrites Yoast.

There are two pragmatic approaches to measurement, including free online checkers such as the SE Ranking keyword density tool SE Ranking. Use an occurrence counter or text analysis script for quick audits, and complement that with TF IDF or semantic term reports from site audit tools to understand coverage. The combination prevents overfitting to a single metric and gives a broader view of topical depth Ahrefs Blog.

Track term occurrences, semantic term coverage, impressions, and click data for target queries. Use Search Console to monitor query level performance and to check whether adjustments change impressions or clicks over time. Avoid treating small density shifts as meaningful without corroborating signal changes.

Decision criteria: when to rewrite copy and when to fix signals

Evaluating intent match and page purpose

Decide to rewrite body copy when intent mismatch is evident or when the page does not answer common user questions for the target query. If impressions are low for the expected query and intent appears aligned, then metadata or schema may be the limiting factor.

When local signals matter more than body density

For local pages, missing or inconsistent NAP and absent schema are often larger issues than a low exact match count. Fix metadata and structured data first, then consider measured tweaks to body copy if query performance remains weak Google Search Central.

Common mistakes and pitfalls when using density as a proxy

Mistaking correlation for causation

One common error is assuming density correlations prove causation. Industry analyses can show patterns, but they do not establish that a specific density caused a ranking change. Use density as one of multiple signals during diagnosis and testing.

Over-optimising for an invented target

Another pitfall is chasing a numeric target and producing awkward, repetitive copy. That creates a poor user experience and can trigger search quality systems. Prioritise natural language and semantic breadth over forced repetition Yoast.

Practical examples: calculating density for a local services page

Worked example with methodology

Describe the method with labelled placeholders. First, define the target keyword. Second, extract the page body only and count words, noting whether headings and captions are included. Third, count exact match occurrences in the body. Fourth, compute the formula and report the result. Present both exact match occurrences and a short note on semantic coverage.

When reporting to stakeholders show the computed occurrences per 1000 words alongside checks for title, H1, meta presence, and schema. Label each figure as a diagnostic, and recommend complementary fixes such as a metadata update or schema insertion rather than a body rewrite by itself, or use a quick page analyzer such as the Internet Marketing Ninjas keyword density tool Internet Marketing Ninjas Ahrefs Blog.

How to report results to stakeholders

Keep stakeholder reports short. State the metric, explain what it implies, and list immediate next steps. For local pages that means noting whether location appears in title and H1, whether schema is present, and whether NAP is consistent. Frame recommendations as conditional improvements that depend on measurement quality and constraints.

How to monitor outcomes without chasing density

Which KPIs to watch instead

Primary KPIs are impressions and clicks for the target queries, click-through rate, and user behavior metrics that indicate satisfaction. Search Console provides query level impressions and click data which are more directly tied to user demand than body density alone Yoast.

How long to wait before judging changes

Allow time for search systems to reprocess changes. Measure before and after trends rather than making instant judgments. Avoid simultaneous edits across many variables when you want to attribute an effect to a specific change.

Workflow: embedding checks into content production

Where to run density checks in the editing process

Run density checks at draft review, pre publish audit, and during periodic refresh audits. Integrate the formula into your content checklist so editors can diagnose potential issues rapidly.

Who owns the checks and how to pass them off

Define ownership clearly. Content editors handle phrasing and semantic coverage, SEO or system owners handle metadata and schema, and analytics owners monitor outcomes. Use small automations to count occurrences and populate reporting templates and tools like KWFinder to reduce manual friction Ahrefs Blog.

Conclusion: a pragmatic local SEO policy for copy

Simple policy notes you can implement today

Adopt a simple policy: use the occurrences per 1000 words formula for audits, treat density as a heuristic, and favour three to eight exact match occurrences per 1000 words for local pages while emphasising title, H1, meta, and schema placement Moz.

When to seek deeper diagnostics

If you see persistent low impressions or clear intent mismatch despite adequate metadata and schema, escalate to a diagnostic that maps intent, content gaps, and technical constraints. Systems thinking and measurement clarity are higher leverage than chasing single numeric targets.