donottrack.us 63 C
🛡️ SEO 39 🤖 GEO 81 ⚡ Perf 62 🏗️ Arch 53

donottrack.us — Global SEODiff Score 63/100

donottrack.us
📊

donottrack.us shows strong AI visibility with an ACRI of 74/100, outperforming 78% of indexed domains. Its server-rendered architecture ensures AI crawlers receive complete HTML on first request, a key advantage for extractability. A 5.2× bloat ratio is typical for sites in this tech tier — not wasteful, but streamlining could further boost extractability. Zero schema blocks puts this site at a disadvantage in knowledge graph and AI-answer pipelines that rely on explicit structured data. The site maintains an open-door policy for AI crawlers — GPTBot, ClaudeBot, and other major agents are all allowed.

63
C — Global SEODiff Score
Comprehensive search visibility assessment
Strong foundations, but Traditional SEO (39) is your bottleneck.
🎯 Top Fix: Add Organization + WebSite JSON-LD → +5–8 pts
🔬 Automated SEODiff Assessment · Snapshot: Mar 21, 2026 · 📋 API
📈 ACRI Trend 6 snapshots
Feb 23 Mar 21
🔔 Recent AI Indexing Activity
No recent changes detected by adaptive crawler.
Does your site score higher than donottrack.us?
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🧮 Score Transparency — How is this calculated?
🛡️ Traditional SEO (25% weight)39 × 0.25 = 9.8
🤖 AI Readiness / GEO (40% weight)81 × 0.40 = 32.4
⚡ Performance (20% weight)62 × 0.20 = 12.4
🏗️ Architecture & Trust (15% weight)53 × 0.15 = 7.9
Weighted sum = 9.8 + 32.4 + 12.4 + 7.9
Global SEODiff Score = 63 (C)
📊 ACRI Sub-Scores (AI Readiness Detail)
100
Bot Access
avg 92
100
Rendering
avg 93
58
Structure
avg 35
0
Schema
avg 9
70
Tech Stack
avg 63
🔀
Visibility Delta: Google vs AI
Google (Tranco)
Top 29%
Rank #286202
-6 pts
Gap
AI (ACRI)
Top 22%
Score 74/100

donottrack.us is more visible to Google than to AI models. There's room to improve AI discoverability to match your search reputation. ACRI measures technical crawler readiness. Read the methodology →

Why donottrack.us ranks here

Tech stackDrupal
Industry
RenderingSSR
Schema coverage0 blocks
Token bloat5.2×

Fastest improvements

  • Add basic Organization and WebSite JSON-LD to fix “0 schema blocks” (see Schema Coverage).
  • Reduce token bloat (navigation/footer/code) so agents reach your main content faster (see Token Bloat).
  • Create an llms.txt file so AI crawlers can discover your content structure without heavy crawling. Generate llms.txt →
  • Run a full entropy audit to find which DOM regions waste the most tokens. Run Entropy Audit →
🧪

JavaScript Rendering Check

We check what AI crawlers miss when they skip JavaScript execution.

Running headless browser to simulate AI extraction…
🛡️

Traditional SEO

39/100 25 % of Global Score 🟢 High Confidence

📝 Title Tag

45 chars
Good length

Optimal range: 30–60 characters for SERP display.

📋 Meta Description

376 chars
Too long

Optimal range: 120–160 characters for snippet control.

🔤 Heading Hierarchy

  • ✗ Exactly 1 <h1> tag — found 2
  • ✓ Has <h2> headings — found 12
  • ✓ <h2> not before <h1>

🔍 Indexability

  • ✓ Canonical tag present → https://www.eff.org/issues/do-not-track
  • ✓ No noindex directive
  • ✓ Meta viewport set
  • ✓ HTML lang attribute → en
  • ✅ Hreflang tags
  • ✓ Googlebot allowed by robots.txt

🌐 Social / OpenGraph

  • ✓ og:title — Do Not Track
  • ✓ og:description — What is online tracking? Online advertising is big business. According to industry groups, revenues for online ads exceeded $150 billion USD in 2021 and continue to grow every year. Those ads are powered by online tracking, profiling, and targeting: a vast corporate surveillance network that harvests and analyzes our every click, query, and more. The average web page shares data with dozens of third parties. The average mobile app does the same, and many apps collect highly sensitive information like location, even when they’re not in use. Digital tracking also reaches into the physical world: shopping centers use automatic license-plate readers to track traffic through their parking lots; businesses, concert organizers, and political campaigns use Bluetooth and WiFi beacons to perform passive monitoring of people in their area; and retail stores use face recognition to identify customers, screen for theft, and deliver targeted ads. In order to target ads to users based on their online behaviors, the ad-tech industry relies on sophisticated tracking techniques that collect information about users as they browse the web and interact with apps on their mobile devices. Users are tagged with unique identifiers to pinpoint them and categorize their consumer behaviors into cohorts and micro-audiences: users from a certain neighborhood who are interested in used Audi cars, for instance. On mobile devices, advertisers can use an identifier provided by the phone itself, in the form of a unique advertising identifier. Advertisers also use cross-device tracking to link a users’ mobile devices to their workstation and other devices they may have in the home, forming an overall picture of their usage of all the different devices they interact with. Princeton researchers found that the vast majority of online tracking comes from large tech firms such as Google and Facebook, and is disproportionately on sites that rely heavily on advertising revenue like news and arts websites. How does tracking happen on the web? On the web, tracking is most often performed with cookies—specifically third-party cookies, which come from sites that the user doesn’t even directly visit, but are instead loaded by the “first-party” site the user is on. For instance, a news site may include an advertisement or interactive game from an advertiser server that is able to set cookies on a user’s browser. If this advertiser is loaded on multiple sites a user visits, the advertiser will be able to track that user across multiple sites via the cookie they set previously. One way to evade these trackers is to “clear cookies” on your browser. But this won’t always protect from other forms of web tracking. For example, browser fingerprinting uses different characteristics of a person’s browser (such as language, time zone, and fonts) that are not on their own unique, but when combined will uniquely identify a specific browser. What is Do Not Track? A number of initiatives have attempted to limit the scope of online tracking. Originally proposed in 2009, the Do Not Track (DNT) web header sent a signal from a user’s browser to all sites they visited stating that user’s preference not to be tracked. The DNT header initiative suffered from lackluster adoption by browsers and a lack of mechanisms to enforce the user's preference. To give the preference some teeth, EFF introduced our own DNT Policy, which sites could incorporate into their own privacy policies, and thus promise not to track browsers that opted out using the DNT header. In return, tracker blockers such as our own Privacy Badger would not block sites which abided by this policy. Eventually, DNT was superseded by individual browser initiatives to block or limit trackers, and was abandoned by standards bodies such as the W3C. What is Global Privacy Control? In 2020, a new specification titled Global Privacy Control (GPC) was introduced at the W3C. It picks up momentum where DNT began to lag. It also pairs with the newly passed California Consumer Privacy Act (CCPA) and the now well-known GDPR. At its core, it works like DNT: a user’s browser sends a distinct signal to websites it visits that invokes the GPC. But now, the signal is legally binding to companies in places with applicable privacy laws. In California, for example, it allows users to opt out of having their data shared or sold. This automated, “one and done” opt-out tool is far easier for people to use than manually opting-out, one at a time, at all the sites that a person visits.  Tools such as Privacy Badger have incorporated this functionality, coupled with the DNT controls that are already in place. This new specification is tailored to requirements of new laws like CCPA. However, it doesn’t completely protect users from the dangerous advertising and tracking industry, given gaps in current law. How do I prevent myself from being tracked? EFF’s own Privacy Badger is a browser extension that automatically learns to block invisible trackers. It identifies the third-party resources you encounter across the web, determines which are trackers, and prevents those trackers from being loaded. In this way, it allows the useful third-party resources that are needed to display a webpage and help it function properly, while blocking the unnecessary and invasive trackers. Other browser extensions such as uBlock Origin and Disconnect can be used in combination with Privacy Badger to provide a defense in-depth approach, layering different protections against trackers in your browser. Browsers themselves have introduced various protections against web tracking as well. Apple’s Safari browser and iOS use Intelligent Tracking Prevention to protect against the latest tracking technologies. Enhanced Tracking Protection in Firefox uses the Disconnect block list as well as a number of Mozilla’s own techniques to block trackers. Brave is a privacy-focused browser employing a unique set of protections of its own, including protection against browser fingerprinting technologies. Users who wish to protect themselves against not only online trackers but also more advanced threats may consider Tor Browser, which puts an anonymous web-browsing experience above all else. Note: this is an updated page. View the original archived version.
  • ✓ og:image — preview
  • ✓ twitter:card — summary
📐 How the SEO Pillar score is calculated

SEO Pillar = Title (20 pts) + Meta Desc (20 pts) + Heading Hierarchy (20 pts) + Indexability (20 pts) + Social/OG (20 pts)

Each sub-score is derived from the checks above. Canonical tag, lang attribute, og:image, and a single H1 are the highest-impact items.

🤖

AI Readiness / GEO

81/100 40 % of Global Score 🟢 High Confidence

This pillar aggregates citation share, hallucination risk, bot access, schema health, and content extractability. The individual diagnostic sections below contribute to this score.

🚨

Hallucination Risk

Research

Is AI lying about your brand? This panel measures how likely LLMs are to hallucinate facts when extracting information from your page.

Analyzing hallucination risk…

🤖 Bot Access Matrix

GPTBot (OpenAI)
Allowed
ClaudeBot (Anthropic)
Allowed
CCBot (Common Crawl)
Allowed
Google-Extended
Allowed
Googlebot
Allowed

👻 Rendering (Ghost Ratio) Docs

Ghost Ratio 0%
0% — Safe 50% 100% — Risk
Status Server-Side Rendered (Safe)
Rendering Type SSR

📊 Structure & Information Density Docs

Structure Grade 58/100 — Fair
Structured Elements 138 elements (138 lists, 0 rows, 0 headers)
Total Words1359
Raw Density10.1%

🏷️ Schema Health Docs

Organization Schema ❌ Missing
Product / Service Schema ⚠️ Not Found
Total Schema Blocks0 — No JSON-LD detected

Schema Coverage Map

0/7 schema types detected
❌ Organization
❌ Product/Service
❌ Breadcrumb
❌ FAQ
❌ Article
❌ WebSite
💡Organization schema missing. AI models cannot identify your brand entity. Without it, your brand won't appear in Knowledge Panels or be associated with your content.
💡Product / Service schema missing. AI models don't know this is a SaaS product. Add Product or SoftwareApplication schema so AI understands what you offer and can surface pricing/features.
💡BreadcrumbList schema missing. AI cannot understand your site hierarchy or how pages relate to each other.
💡FAQ schema missing. Adding FAQPage schema lets AI models directly extract Q&A pairs for Featured Snippets and chatbot answers.
💡WebSite schema missing. Add WebSite + SearchAction so Google can generate a Sitelinks Search Box for your brand in AI results.

📐 AI Efficiency Metrics Docs

62
AI Extractability
Low
Crawl Cost
None
Blocklist Risk
Extractability62/100 — AI models can partially extract answers from this page
Crawl CostLow (30/100) — efficient for AI crawlers to process
Blocklist RiskNone — 0 of 5 AI crawlers blocked

Token Bloat Research

19%
🗑️ 81%
Useful Content (10.4 KB)Bloat (43.9 KB)
Token Bloat Ratio5.2× — Normal

Multimodal Readiness

Visual Context40% Optimized for Vision
Image Alt Coverage2 / 5 images have alt text

TDM Rights

TDM-Reservation HeaderNot set
X-Robots-Tag: noaiNot set
💡Only 40% of images have alt text. Add descriptive alt attributes so multimodal AI (ChatGPT Vision) can understand your images.

🔥 Structural Entropy Check Research

0 Entropy
Poor Token Bloat: High
Noise Ratio: 80.9% · SNR: 0.24 · Signal: 2662 / Noise: 11242 tokens

🔬 AI-Crawler Simulation

See your website the way AI crawlers do. CSS stripped, structure labeled, content chunked.

🌐
This is what humans see — styled, branded, visual.
Toggle to "AI Agent View" to see what GPTBot, ClaudeBot, and other AI crawlers actually extract from this page.
🤖

AI Answer Preview

NEW

See how AI models summarize your site. Left: your actual content. Right: what the LLM extracts and says about you.

Simulating AI extraction…
🧠

The LLM Interpretation

AI-VERIFIED

SEODiff AI analyzed the extracted content of donottrack.us and produced this structured business intelligence. Fields marked SEMANTIC VOID indicate information the AI could not find — a critical gap in your site’s machine-readability.

Core Offering
This tool helps users prevent online tracking and control their privacy settings.
Target Audience
Privacy advocates, internet users concerned about surveillance
Pricing Model
⚠ SEMANTIC VOID
🏆 Competitive Moat
Focus on user privacy and control, providing a direct solution to online tracking.
📊 Content Depth
3/10
Analyzed by SEODiff AI · 2026-03-13

🔧 Tech Stack

FrameworkDrupal
AI-Readiness Score70/100
Servernginx
CDN
HTTP Status200
Load Time1114 ms
Raw HTML Size54.3 KB
Visible Text Size10.4 KB

Performance & Speed

62/100 20 % of Global Score 🟢 High Confidence

⏱️ Time to First Byte

1114 ms
Slow — bots may time out or deprioritise

Google considers <200 ms "good". AI crawlers may have even shorter timeouts.

📦 Page Weight

556
DOM nodes
54 KB
HTML payload
Lean page — fast for bots and users

🗄️ Cache & CDN

  • ✓ Cache-Control header → public, max-age=1800
  • ✗ CDN cache status
  • ✗ CDN detected

🔬 Tracker Tax

0
tracker scripts
0
third-party domains
0.0%
token overhead
Minimal tracker load — clean signal for bots
📐 How the Performance Pillar score is calculated

Perf Pillar = TTFB (35 pts) + Page Weight (25 pts) + Cache/CDN (20 pts) + Tracker Tax (20 pts)

TTFB <200 ms = full marks. DOM >3000 or payload >300 KB incurs heavy penalties. Tracker scripts beyond 5 reduce score.

🏗️

Architecture & Trust

53/100 15 % of Global Score 🟡 Medium Confidence

🗺️ Sitemap & Robots

  • ✗ Sitemap declared in robots.txt
  • ✓ Googlebot allowed
  • ✓ GPTBot allowed
  • ✓ ClaudeBot allowed

🔗 Linking

81
internal links
92
external links
Good internal linking — helps crawlers discover content

🔒 Security & Trust

  • ✗ HSTS header (Strict-Transport-Security)
  • ✗ Content-Security-Policy header
  • ✓ HTTP status 200 OK (got 200)

♿ Accessibility Signals

  • ✓ HTML lang attribute → en
  • ✓ Meta viewport for mobile
  • ✗ Single H1 for screen readers
📐 How the Architecture Pillar score is calculated

Arch Pillar = Sitemap & Robots (30 pts) + Linking (25 pts) + Security (25 pts) + Accessibility (20 pts)

Having a valid sitemap, allowing AI bots, HSTS, and a good internal link count are the highest-impact items.

🏅 AI-Verified Trust Badge

Your site scores 59/100. Reach 80+ to unlock the green "AI-Verified" badge. Fix the issues below to improve your score.

AI-Verified badge for donottrack.us
Pending Audit — score below 80 threshold
<a href="https://seodiff.io/radar/domains/donottrack.us" rel="noopener"><img src="https://seodiff.io/api/v1/badge?domain=donottrack.us" alt="AI-Verified by SEODiff" width="280" height="52"></a>

💡 Paste in your site footer, GitHub README, or email signature. Badge updates automatically as your score changes.

🔗 Similar Sites

Domains with a similar tech stack, industry, and AI readiness profile to donottrack.us. Compare side-by-side.

Domain ACRI AI Score Tech Stack Token Bloat Schema
donottrack.us (this site) 59 74 Drupal 5.2× 0
vegansociety.com 69 80 Drupal 6.3× 0 Compare →
fra.europa.eu 69 78 Drupal 3.0× 0 Compare →
sgu.ru 69 79 Drupal 2.7× 0 Compare →
sascha.ru 69 78 Custom / Proprietary 6.1× 0 Compare →
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📊 Semantic Share of Voice

How often would an AI cite donottrack.us when users ask about topics in this domain's niche? We run entity queries through our 188k-page search index and measure citation probability.

Analyzing citation landscape…

🩹

Remediation Patches

COPY-PASTE

Auto-generated code fixes tailored to donottrack.us. Copy and paste these into your codebase to improve AI visibility. These patches are mathematically proven to increase extraction accuracy →

Add Organization JSON-LD
High Impact ⏱ 5 min
AI models cannot identify your brand entity without Organization schema. This is the #1 fix for AI visibility.
html
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Donottrack",
  "url": "https://donottrack.us",
  "logo": "https://www.eff.org/sites/all/themes/frontier/favicon.ico",
  "sameAs": []
}
</script>
Add WebSite + SearchAction JSON-LD
High Impact ⏱ 5 min
Enables the Sitelinks Search Box in Google and allows AI to understand your site structure.
html
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "WebSite",
  "name": "Donottrack",
  "url": "https://donottrack.us",
  "potentialAction": {
    "@type": "SearchAction",
    "target": "https://donottrack.us/search?q={search_term_string}",
    "query-input": "required name=search_term_string"
  }
}
</script>
Add FAQ Schema
Medium Impact ⏱ 10 min
FAQ schema lets AI models directly extract Q&A pairs. This is the easiest way to get featured in AI responses.
html
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is Donottrack?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add your answer here — describe what Donottrack does in 1-2 sentences."
      }
    },
    {
      "@type": "Question",
      "name": "How does Donottrack work?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Explain the key features and how users interact with Donottrack."
      }
    }
  ]
}
</script>
📈

Projected Impact

ROI EST.

If you apply the patches above, here's the estimated improvement for donottrack.us:

Current Score
74
Projected Score
90
Improvement
+16 pts
Add Organization schema +6 pts
Add WebSite schema +4 pts
Reduce token bloat +3 pts
Add FAQ schema +3 pts

*Estimates based on SEODiff's scoring model. Actual results depend on implementation quality.

📋 Data Export

Download scores and metadata for audits, client reports, or CI/CD pipelines. Exports contain computed metrics only (no copyrighted content).

All data is generated automatically and updated with each crawl. JSON exports contain scores and metadata only (no copyrighted content).

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🧭 Self-Diffing (Private Layer)

For owned domains, combine this world snapshot with private drift + regression history.
Template Drift
Track in My Site
Drift → Traffic Impact
In development coming soon
Regression Incidents
Track in My Site
Internal Linking
Deep Audit graph
Semantic Structure
GEO view in Deep Audit
Content Quality
Thin/duplicate tracking

🕒 History

Score over timeAvailable in My Site history
Drift eventsTemplate timeline + incidents
Drift → Revenue AttributionComing soon
Schema/rendering/extractability changesTracked per scan in project history
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