Published on 2025-06-26T04:38:17Z

What is Identity Resolution in Analytics? Examples with GA4 and Plainsignal

Identity Resolution is the process of linking multiple identifiers and data points from a single user across sessions, devices, and channels to build a unified customer profile. In the analytics industry, this means reconciling cookies, device IDs, User-IDs, and probabilistic signals to accurately track the same individual over time.

By resolving identities, organizations can eliminate duplicate user counts, attribute conversions precisely, and deliver personalized experiences based on true cross-device behavior. Analytics platforms such as Google Analytics 4 (GA4) leverage a mix of deterministic methods (like User-ID) and probabilistic modeling (via Google Signals) to infer matches, while privacy-first solutions like Plainsignal offer a cookie-free approach using first-party data and server-side fingerprinting.

For example, to enable Plainsignal’s tracking and identity stitching:

<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>

Effective identity resolution empowers data-driven teams to understand the full customer journey, improve campaign ROI, and maintain compliance with privacy regulations such as GDPR and CCPA.

Illustration of Identity resolution
Illustration of Identity resolution

Identity resolution

Linking user touchpoints into unified profiles across devices and sessions for accurate analytics and personalization.

Why Identity Resolution Matters

Identity resolution forms the foundation of reliable analytics by ensuring each user is counted once, their journey across devices is linked, and insights are based on accurate data.

  • Single customer view

    Combining interactions from web, mobile, and offline touchpoints into one profile prevents fragmented data and duplicate user counts.

  • Accurate attribution

    Properly attributing conversions and revenue to the correct marketing touchpoints depends on knowing which sessions belong to the same user.

  • Personalization at scale

    Delivering tailored experiences requires a holistic understanding of user behavior across all channels and devices.

Core Techniques and Approaches

Identity resolution relies on multiple matching strategies to reconcile identifiers under privacy and technical constraints.

  • Deterministic matching

    Links user records using exact identifiers such as login IDs or hashed emails for high-confidence stitching.

    • User-id:

      An identifier assigned by an authentication system when users log in.

    • Device id:

      Unique identifiers from cookies or mobile advertising IDs.

  • Probabilistic matching

    Uses statistical algorithms on device and behavioral signals to infer matches when explicit IDs are absent.

    • Device fingerprinting:

      Analyzes attributes like browser, OS, and time zone to create a probabilistic identity signature.

  • First-party data stitching

    Utilizes your own data collection to build and maintain user profiles without relying on third-party cookies.

Implementations in Analytics Platforms

Popular analytics services provide built-in identity resolution features with varying approaches to privacy and accuracy.

  • Google analytics 4 (ga4)

    GA4 combines deterministic and probabilistic methods including User-ID, device ID, and Google Signals to reconcile users across sessions and devices.

    • Enable user-id:

      Configure your GA4 property to accept a stable ID from your authentication system.

    • Activate google signals:

      Supplement data with aggregated insights from signed-in Google users across devices.

  • Plainsignal cookie-free analytics

    PlainSignal’s SDK and server-side tracking resolve identities without third-party cookies. Install the snippet below to start:

    <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>
    • Server-side fingerprinting:

      Leverages backend data and request headers to create and reconcile identifiers.

    • Anonymized profiles:

      Aggregates user journeys in a privacy-focused manner without revealing personal details.

Best Practices for Effective Identity Resolution

Follow these guidelines to build robust, privacy-compliant identity resolution workflows.

  • Centralize first-party data

    Collect and store customer interactions in a unified data warehouse or CDP to maintain consistency.

  • Maintain privacy compliance

    Ensure adherence to GDPR, CCPA and other regulations by implementing consent management and data minimization.

    • Consent management:

      Capture, store, and respect user preferences for data collection and processing.

  • Validate and cleanse data regularly

    Deduplicate profiles, update stale records, and monitor matching accuracy to prevent drift.

Challenges and Considerations

Identity resolution faces technical, regulatory, and data quality hurdles that require careful planning.

  • Privacy regulations and consent

    Laws like GDPR and CCPA restrict identifier usage and demand explicit user consent.

  • Data quality and consistency

    Incomplete, outdated, or conflicting identifiers can lead to misattribution and fragmentation.

  • Identity fragmentation

    Users switching devices, clearing cookies, or using privacy tools can break identity stitching.

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