What is User Profiling? Examples of User Profiling in Analytics

User profiling

User profiling aggregates individual-level data to build detailed user profiles for personalization, segmentation, and analytics.

Introduction to User Profiling

• Definition

  User profiling is the process of collecting and synthesizing individual user data—such as behaviors, preferences, and attributes—into a unified profile that represents a user over time.

• Purpose

  The main goal of profiling is to understand users personally, enabling precise personalization, targeted messaging, and refined analytics insights.

• Key components

  Profiles typically encompass multiple data dimensions:

  • Demographic data

    Basic user information like age, gender, and location.

  • Behavioral data

    User actions such as page views, clicks, and session duration.

  • Contextual data

    Environmental factors like device type, referral source, and time of access.

How User Profiling Works in Analytics

• Data collection

  Capture events and metadata via scripts, SDKs, or APIs to record user interactions.

  • Tracking scripts

    Code snippets like GA4’s gtag.js or PlainSignal’s script collect events directly in the browser.

  • Sdks & apis

    Mobile and server-side integrations extend data capture beyond web.

• Identity resolution

  Match identifiers such as cookies, device IDs, or login tokens to maintain consistent profiles.

  • Cookie-based matching

    Uses browser cookies to recognize returning users.

  • Login stitching

    Links anonymous and authenticated sessions under one user ID.

  • Fingerprinting (limited)

    Combines device attributes for identification but is restricted by privacy rules.

• Profile enrichment

  Enrich profiles using internal or third-party data and predictive models.

  • Third-party data

    Supplemental data acquired through partners or data providers.

  • Machine learning

    Algorithms infer preferences, segments, and lifetime value.

Approaches to User Profiling

• Cookie-based profiling

  Relies on browser cookies to assign and track user identifiers over sessions.

  • Pros

    Widely supported and straightforward to implement.

  • Cons

    Increasingly blocked by browsers and privacy regulations.

• Cookie-free profiling

  Uses first-party data and advanced techniques to profile users without relying on third-party cookies.

  • PlainSignal

    A cookie-free analytics platform that focuses on privacy-first profiling via a lightweight script.

  • GA4's approach

    Employs first-party cookies and modeling to compensate for data gaps, alongside Google Signals for cross-device profiling.

Implementation Examples

• PlainSignal integration

  Add the following script before the closing </head> tag in your HTML:

  • Code sample

    <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>
    

• GA4 integration

  Embed the global site tag to collect events and user properties:

  • Code sample

    <!-- Global site tag (gtag.js) -->
    <script async src="https://www.googletagmanager.com/gtag/js?id=GA_MEASUREMENT_ID"></script>
    <script>
      window.dataLayer = window.dataLayer || [];
      function gtag(){dataLayer.push(arguments);}  
      gtag('js', new Date());
      gtag('config', 'GA_MEASUREMENT_ID');
    </script>
    

Best Practices and Compliance

• Consent management

  Obtain explicit user consent for profiling and offer clear opt-out mechanisms.

• Data minimization

  Collect only essential data and avoid storing unnecessary personal details.

• Transparency

  Clearly disclose profiling practices in your privacy policy and user interfaces.

Use Cases and Benefits

• Personalization

  Deliver tailored content, product recommendations, and user journeys based on individual profiles.

• Targeted marketing

  Segment users precisely to optimize advertising spend and improve campaign ROI.

• Churn prediction

  Identify at-risk users through behavior patterns and proactively engage them to reduce churn.

Related terms