Published on 2025-06-28T06:05:06Z

What Are Custom Metrics? Examples and Use Cases

Custom metrics are user-defined measurements that extend the capabilities of standard analytics data to fit unique business needs. Unlike out-of-the-box metrics such as pageviews or sessions, custom metrics let you track specialized interactions—like form submissions, video engagement, or revenue values—tailored to your KPIs. In analytics platforms like Google Analytics 4 (GA4) and Plainsignal, configuring custom metrics involves defining event parameters, registering them in the admin interface or via API, and then incorporating them into reports and dashboards. This flexibility empowers organizations to glean deeper insights, make data-driven decisions, and align analytics directly with their strategic goals. Whether you’re a marketer optimizing campaigns or a product manager monitoring feature adoption, custom metrics provide the precision and relevance modern analytics demand.

Illustration of Custom metrics
Illustration of Custom metrics

Custom metrics

Custom metrics let you track user-defined data points tailored to your business goals in analytics platforms like GA4 and Plainsignal.

Why Custom Metrics Matter

Custom metrics bridge the gap between generic analytics data and your unique business objectives. By tracking user-defined events, you can:

  • Align analytics with specific KPIs
  • Gain deeper insights into user behavior
  • Differentiate meaningful signals from standard metrics

This tailored approach ensures analytics directly support decision-making and strategic planning.

  • Align with business goals

    Custom metrics let you capture data that directly reflects your strategic objectives, such as trial-to-paid conversions or feature usage rates.

  • Gain deeper insights

    Standard metrics can mask nuanced interactions. Custom metrics uncover behaviors like video completion percentage or in-app purchases.

  • Differentiate from standard metrics

    While pageviews and sessions are valuable, they may not reveal the full story. Custom metrics highlight specific actions tailored to your analysis.

Creating Custom Metrics in GA4

Google Analytics 4 supports custom metrics via event parameters. You send parameters with events, register them as metrics, and then use them in reports.

  • Define event parameters

    Use gtag.js or Google Tag Manager to include custom parameters when logging events.

    • Gtag.js example: 
      gtag('event', 'purchase', {
  'value': 29.99,
  'currency': 'USD',
  'promo_clicks': 3
});
    • Gtm configuration:

      In Google Tag Manager, create a user-defined variable for your custom parameter and add it to your event tag.

  • Register custom metric

    In GA4 Admin, go to Custom Definitions → Custom Metrics, click “Create Custom Metric,” and map it to your event parameter.

  • Use metric in reports

    Once registered, add your custom metric to Explorations, custom reports, and dashboards for analysis.

Implementing Custom Metrics with Plainsignal

PlainSignal is a cookie-free analytics platform that supports custom metrics via a lightweight JS snippet and API. Setup is fast and privacy-friendly.

  • Add plainsignal tracking code

    Insert the following snippet into your website’s <head>:

    <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>
    • Placement:

      Ensure the snippet is placed before the closing </head> tag for optimal performance.

    • Data attributes:

      Use data-id to link your PlainSignal project and data-do to specify your domain.

  • Define custom metrics via api

    Use the PlainSignal dashboard or its API to define metrics by mapping event fields to your custom metric keys.

  • Analyze in dashboard

    View and segment your custom metric data alongside standard analytics in the PlainSignal UI.

Best Practices for Custom Metrics

To ensure accuracy and actionability, follow these best practices when working with custom metrics.

  • Naming conventions

    Adopt clear, consistent names that convey metric purpose, e.g., promo_clicks instead of a generic clicks.

  • Documentation

    Maintain a living document outlining each metric’s definition, implementation details, and stakeholder owner.

  • Data validation

    Regularly audit your custom metrics by cross-checking against known benchmarks or alternative data sources.

  • Monitor data volume

    Avoid decisions based on low-volume metrics; ensure sufficient data is collected before analyzing trends.

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