Know Every Returning Visitor - Without a Single Cookie
Track complete user journeys with up to 99.5% accuracy on the same device/browser—no cookies needed. Based on testing across millions of visitor sessions.
✓ No cookies ✓ Privacy-first ✓ No consent banners needed
Accuracy Definition: 99.5% precision (avoiding false matches) with 98%+ recall (catching return visitors). Accuracy varies by browser diversity—typically 95-99.5% depending on your audience's device/browser combinations.
Accuracy Breakdown by Scenario
Real-world accuracy varies based on device and browser conditions
Same Device, Same Browser
Optimal conditions — highest possible accuracy
Same Device, Browser Updated
ML models adapt to minor version changes
Same Device, Privacy Mode
Reduced accuracy across incognito sessions
Shared Devices
Family computers, kiosks, public devices
Comparison Metrics
- 3–5× more accurate than IP-based identification
- Comparable to first-party cookies without the consent complexity
- Collision rate under 0.1% for sites with diverse traffic
Fingerprint Stability
- ML models identify return visitors even when 20–30% of fingerprint characteristics change
- Behavioral patterns confirm identity when fingerprint partially changes — no single point of failure
- Matching remains robust across minor browser and OS updates
Technical Transparency
Exactly which browser signals we collect, and how fingerprinting works step by step
Browser Signals We Use
Only basic, publicly available characteristics — no invasive methods
Collected
Never collected
Match Confidence
Visitor recognised from previous session
How Fingerprinting Works
Fingerprint Creation
On first visit, browser characteristics are collected and hashed one-way. The hash cannot be reverse-engineered to identify the user.
Fuzzy ML Matching
On return visits, ML models compare fingerprints using fuzzy matching — recognising the same visitor even when 20–30% of characteristics have changed.
Cross-Device Limits
Fingerprinting works per device/browser pair. A laptop and a phone appear as two separate visitors unless the user explicitly logs in.
Privacy & Compliance
Transparent about what we collect, how long we keep it, and your legal obligations
What We Don't Collect
Pseudonymous identifiers only — no personal data
Names, emails, or directly identifying info
We never store any personal identifiers
IP addresses
Anonymized on ingest — never used for identification
Cookies or local storage
Zero client-side data persistence
Cross-site tracking
Fingerprints are entirely site-specific
All identifiers are one-way hashed and cannot be reverse-engineered
GDPR & Privacy Compliance
Important legal note
GDPR applicability depends on how data is used, not just collection method
Fingerprinting for analytics may still require disclosure in your privacy policy
Some regulators treat fingerprinting similarly to cookies under ePrivacy Directive
Compliance varies by jurisdiction — consult legal counsel for your situation
Our recommendation: Disclose fingerprinting in your privacy policy even where not strictly required. Transparency builds user trust.
Privacy Mode Clarification
What “works in incognito” actually means
Fingerprinting works normally inside a single incognito window
99.5%
Accuracy drops because incognito clears fingerprint data between sessions
~85%
Normal sessions (non-incognito) are unaffected by privacy mode settings and maintain full accuracy.
Data Retention
How long we keep visitor identifiers
Visitor data is automatically purged after 24 months of inactivity. Active visitors' data is retained as long as they continue visiting.
Privacy-Preserving Machine Learning
How our ML improves identification accuracy while keeping privacy guarantees intact
What the model actually does
Stability Learning
Models learn which signals stay stable over time — screen resolution rarely changes, but browser version does. Stable signals are weighted higher for matching.
Signal weight
Fuzzy Matching
ML models recognise return visitors even when fingerprints are only 70–80% similar — not just exact matches. Handles browser updates and OS changes gracefully.
Signal weight
Behavioral Patterns
When fingerprint data partially changes, visit frequency, page preferences, and navigation patterns act as a secondary identity signal to confirm returning visitors.
Signal weight
What Makes It Privacy-Preserving
Four non-negotiable guarantees built into the model
Aggregated Pattern Learning
Models train on patterns across all visitors — never on individual data. No raw fingerprints leave your infrastructure.
One-Way Hashing
Identifiers are one-way hashed and cannot be reverse-engineered to reconstruct the original fingerprint or identify the user.
Site-Specific Only
Fingerprints are scoped to your site. We never share them across sites or build cross-site visitor profiles.
No Personal Data in Training
ML models see only anonymous browser characteristics and behavioral patterns — never names, emails, or any personal data.
Real-World Use Cases
Specific scenarios where cookie-free visitor identification drives measurable value
Multi-Session Research Behavior
B2B buyers visit 5–7 times before converting. Without visitor identification, each session looks like a new anonymous user — you miss the full picture.
Average B2B buying cycle
5–7 sessions · 3–14 days
Visitor journey
Cart Abandonment Follow-Up
Identify return visitors who abandoned carts — understand whether they're comparison shopping or have lost interest.
What happens next?
Returns to complete purchase
High-intent — confirm with targeted nudge
Returns, browses different products
Comparison shopping — show social proof
Doesn't return
Lost — review in drop-off analysis
Content Attribution
Which blog posts lead to conversions 3 weeks later? Track the full path from content discovery to purchase.
Attribution path
Without multi-session ID, the blog post gets zero credit for this conversion
Cookies vs. Fingerprinting
How the two approaches compare across key dimensions
| Dimension | Cookies | WysLeap Fingerprinting |
|---|---|---|
| Accuracy (same device) | 98–99% | 95–99.5% |
| Works in private browsing | No | Limited (85%) |
| Requires EU consent | Always | Depends* |
| Survives cookie deletion | No | Yes |
| Cross-browser tracking | No | No |
| Data persistence | User-controlled | Server-side |
* GDPR/ePrivacy interpretation varies by jurisdiction. Consult legal counsel for your specific situation.
Known Limitations
Radical transparency about where fingerprinting falls short — and by exactly how much
Cross-Device Tracking
Cannot link the same user across a laptop and a phone without a login event. Two devices always appear as two separate visitors.
Partially resolvable if users log in to your app — requires custom login event integration
Native Mobile Apps
Fingerprinting is a web browser technology. iOS and Android native apps need device IDs or advertising identifiers instead.
Mobile web browsers (Safari, Chrome) are fully supported
Shared Devices
Multiple users on the same device — family computers, kiosks, public terminals — may appear as a single returning visitor.
Typically affects <5% of B2B and SaaS traffic
Private Browsing
Incognito sessions clear some fingerprint signals between windows, reducing cross-session matching accuracy.
ML behavioral signals partially compensate
Anti-Fingerprinting Browsers
Firefox ETP, Safari ITP, and Brave actively randomise fingerprinting signals. We adapt, but accuracy is reduced for these users.
Affects ~3% of typical web traffic
VPNs & Network Changes
VPN users may appear as new visitors only when significant fingerprint characteristics change alongside their IP address.
Browser fingerprint usually stays stable across VPN changes
For most B2B and SaaS websites, these limitations affect under 10% of total visitor traffic.
The high-impact cases (cross-device, native apps) simply don't apply to standard web analytics. The medium and low cases are rare in typical business audiences — and our ML models reduce accuracy loss wherever possible.
How WysLeap Compares
How fingerprint-based identification stacks up against every alternative
vs.
Cookie-Based Tracking
Comparable accuracy — zero consent friction
- Survives cookie deletion and browser clears
- Works in Safari ITP / Firefox ETP where cookies are blocked
- No consent banner required in many jurisdictions
- 95–99.5% accuracy — on par with cookie tracking
vs.
IP-Based Tracking
3–5× more accurate than IP alone
- Device-level ID vs. network-level — far more precise
- Handles dynamic IPs and mobile networks correctly
- Shared office IPs don't pollute visitor counts
- Collision rate under 0.1% vs. 10–30% for IP
vs.
Login-Required Tracking
100% of anonymous visitors — no auth needed
- Tracks pre-signup visitors across multiple sessions
- Captures the full funnel — not just logged-in users
- Identifies returning anonymous prospects by behaviour
- No friction or signup gate required to start tracking
vs.
Single-Session / No Tracking
Full multi-session journey visibility
- See 5–7 session B2B research journeys end-to-end
- Attribute conversions to first-touch content accurately
- Detect returning visitors and high-intent prospects
- Privacy-first — no personal data, no cookies required
Google Analytics 4
- Relies on cookies — loses tracking when blocked
- Requires consent banners in EU (ePrivacy)
- Users rejecting cookies become invisible
- Strong Google ecosystem integration
WysLeap
- Cookie-free — works even when cookies are blocked
- No consent banner required in many jurisdictions
- Captures visitors who reject GA4 cookies
- Privacy-first visitor journey analytics
Better together
Many teams run GA4 for Google integration and WysLeap for cookie-free journey tracking side-by-side.
Responsible Use
Clear boundaries on what WysLeap is built for — and what it will never be used for
Built for these use cases
Intended and supported
Analytics & Behavioural Insights
Understand how visitors engage with your content across sessions
User Experience Improvements
Identify friction points in journeys and optimise your flows
Multi-Session Journey Tracking
See the complete path from first visit to conversion
Conversion Funnel Analysis
Measure drop-off and attribute conversions to the right content
Explicitly prohibited
Violations result in account termination
Cross-Site Tracking Without Disclosure
Tracking users across different domains without informing them
Selling or Sharing Fingerprint Data
Monetising or transferring visitor data to third parties
Personal Identification
Using fingerprints to identify named individuals or build personal profiles
Privacy Expectation Violations
Any use that exceeds what users would reasonably expect from analytics
Privacy Policy Disclosure Recommendation
We recommend disclosing fingerprinting in your privacy policy even where not strictly required. Transparency builds user trust and helps future-proof your compliance posture as regulations evolve.
Frequently Asked Questions
Everything you need to know about cookie-free visitor identification
Still have questions?
Our team typically responds within a few hours
Already using GA4 or Mixpanel?
WysLeap replaces Google Analytics + cookie-based visitor tracking for one flat price. You keep the journey insights. You lose the cookie banners and consent management overhead.
Free Forever and Paid plans available · Replaces $400+/month in tools
Start Tracking Complete User Journeys
Up to 99.5% identification accuracy without cookies. See multi-session journeys, returning visitors, and funnel paths — all without a consent banner.
Free Forever plan available · Replaces $400+/month in tools