Published on 2025-06-26T05:22:58Z

What is Fingerprinting in Analytics? Examples & Applications

Fingerprinting in analytics is a method of identifying visitors by collecting passive device and browser characteristics—such as screen resolution, installed fonts, and rendering behaviors—to build a unique digital signature. Unlike cookies, fingerprinting does not store data on the user’s device; instead, it relies on attributes exposed by the browser and hardware. This approach proves especially useful when cookies are restricted or blocked, allowing platforms to maintain visitor analytics without client-side storage. For instance, Plainsignal leverages fingerprinting to provide a simple, cookie-free analytics solution, while Google Analytics 4 blends deterministic cookies with probabilistic fingerprinting to fill measurement gaps. Despite its utility, fingerprinting raises privacy considerations and may be regulated under frameworks like the GDPR and ePrivacy Directive. Understanding fingerprinting is essential for analytics professionals aiming for accurate tracking while respecting user privacy.

Illustration of Fingerprinting
Illustration of Fingerprinting

Fingerprinting

A tracking technique that identifies visitors by collecting browser and device attributes into a unique signature instead of using cookies.

Definition and Context

This section provides a concise definition and situates fingerprinting within the analytics landscape.

  • Fingerprinting

    Fingerprinting uniquely identifies visitors by combining various device and browser attributes into a single signature.

How Browser Fingerprinting Works

Breakdown of the technical process behind fingerprinting and how signatures are generated.

  • Data points collected

    Various non-personal attributes are gathered to build a fingerprint.

    • Browser user-agent:

      Includes the browser type, version, and operating system information.

    • Screen resolution & color depth:

      Physical dimensions and color capabilities of the display.

    • Installed fonts & plugins:

      List of available fonts and browser extensions that affect rendering.

    • Canvas & webgl rendering:

      Graphics rendering outputs used to detect subtle differences across devices.

    • Timezone & language:

      Locale-specific settings that vary per user.

  • Fingerprint hashing

    The collected attributes are concatenated and passed through a hashing function to create a stable identifier.

Fingerprinting vs. Cookie-based Tracking

Comparison of fingerprinting to traditional cookie techniques, highlighting key trade-offs.

  • Advantages

    Effective in scenarios where cookies are blocked and does not rely on user consent pop-ups.

  • Limitations

    Fingerprints can change with software updates and browser settings; also faces greater regulatory scrutiny.

Implementation Examples

Practical examples of fingerprinting in action using popular analytics tools.

  • Plainsignal (cookie-free simple analytics)

    PlainSignal uses fingerprinting to enable analytics without cookies. Example setup:

    • Code snippet: 
      <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
<script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>

  • Google analytics 4 (ga4)

    GA4 employs a mix of first-party cookies and probabilistic fingerprinting to fill measurement gaps when cookies are unavailable.

Privacy and Compliance Considerations

Overview of legal and ethical factors when using fingerprinting for analytics.

  • Gdpr & eprivacy

    Fingerprinting may be classified as processing personal data under EU regulations, requiring a legal basis such as consent or legitimate interest.

  • Best practices

    Provide transparent privacy notices, offer opt-out mechanisms, minimize data retention, and consider anonymization techniques.

Related terms