Visitor Analytics System: 99.2% Identification Accuracy

Problem

Standard analytics systems (Google Analytics, Yandex.Metrica) show only the tip of the iceberg:

• Don't accurately identify unique visitors
• Easily bypassed by blockers
• Don't detect bots
• Limited device data

Our Solution

We developed a custom analytics system with deep visitor analysis:

What we collect (50+ parameters):

• Canvas fingerprint
• WebGL fingerprint
• Audio fingerprint
• System fonts
• Browser plugins
• Screen and window resolution
• Timezone and language
• And 40+ more parameters...

Anti-bot system:

• Mouse behavior analysis
• Scrolling patterns
• Time between actions
• JavaScript challenges

Results

Metric	Value
Identification accuracy	99.2%
Fingerprint parameters	50+
Anti-bot accuracy	98.5%
Monitoring	Real-time

What we see now:

1. Unique vs returning — accurate, even without cookies
2. Bots vs humans — 98.5% accuracy
3. Full device info — model, OS, browser, screen
4. Geolocation — country, city, provider
5. Behavior — mouse movement, scrolling, clicking patterns

"Standard analytics show only the tip of the iceberg. Now we see the real picture: who visits, bot or human, what device, whether the visitor is unique." — Client, SEO Agency

Applications

• SEO agencies — traffic quality control
• E-commerce — fraud detection
• Media — real view statistics
• SaaS — user behavior analysis

Technologies

• Custom JS tracker (3KB)
• ClickHouse for storage
• Real-time dashboard
• API for integrations
• GDPR-compliant mode

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Need deep analytics? Let's discuss implementation