Published on 2025-06-22T08:43:42Z

What is Parameter Parsing in Analytics? Examples for GA4 and PlainSignal

Parameter parsing is the automated process by which analytics tools read and interpret parameters passed in URLs or script tag attributes. This technique allows web analytics platforms to extract campaign data, custom user attributes, and other context without manual tagging steps. In modern analytics stacks, parameter parsing is crucial for tracking marketing campaigns, A/B tests, and user interactions. Tools like Google Analytics 4 (GA4) and PlainSignal handle parameter parsing differently: GA4 automatically recognizes standard UTM parameters, while PlainSignal relies on custom data attributes in its script tag. Understanding how parameter parsing works and how to configure it ensures accurate, detailed insights into your website’s performance.

Illustration of Parameter parsing
Illustration of Parameter parsing

Parameter parsing

Automated extraction and interpretation of URL or script tag parameters by analytics tools for accurate campaign and custom data tracking.

Why Parameter Parsing Matters

Parameter parsing enriches your analytics data by capturing critical context without extra code. It ensures marketing campaigns are tracked accurately and enables custom data collection for segmentation, personalization, and deeper insights.

  • Contextual campaign tracking

    Automatically captures common marketing parameters to attribute sessions and conversions to the correct source, medium, or campaign.

    • Utm_source / utm_medium:

      Identifies where the traffic comes from (e.g., newsletter, social) and the marketing channel (e.g., email, cpc).

    • Utm_campaign:

      Tracks the specific marketing campaign or promotion name.

  • Custom data extraction

    Parses bespoke parameters or data attributes to pass unique identifiers or custom metrics into your analytics tool.

    • Data-do attribute:

      Custom attribute in PlainSignal used to define the data domain or context for tracking.

    • Data-id attribute:

      Specifies your PlainSignal project identifier to associate parsed parameters with the correct account.

How Parameter Parsing Works

Under the hood, analytics libraries inspect URLs or DOM elements to locate parameter strings. They decode URL-encoded values, map them to predefined fields, and push them into the data layer or send them directly to analytics servers.

  • Parsing url query strings

    Scans the page URL for key-value pairs after the ? symbol and decodes percent-encoded characters before mapping them to analytics fields.

    • Url decoding:

      Converts percent-encoded characters (e.g., %20) back into human-readable form.

    • Regex extraction:

      Uses regular expressions to reliably identify parameter names and values within the query string.

  • Parsing script tag attributes

    Reads data- attributes from the analytics script tag at page load to capture custom configuration or context parameters.

    • Data attributes:

      Any attribute prefixed with data- in the script tag is detected and parsed as a parameter.

    • Default vs custom:

      Built-in attributes (e.g., data-id) are mapped automatically, while custom ones (e.g., data-do) may require explicit configuration.

Implementation in Google Analytics 4 (GA4)

GA4 natively parses standard UTM parameters from incoming URLs and makes them available in standard reports. Custom parameters can be configured via the gtag.js library or through Google Tag Manager for more advanced use cases.

  • Automatic utm parsing

    GA4 automatically captures utm_source, utm_medium, utm_campaign, utm_term, and utm_content without extra setup.

    • Standard reports:

      View parsed UTM values directly in the Acquisition reports in GA4.

    • Limitations:

      Only standard UTM parameters are recognized out of the box; custom campaign parameters require additional setup.

  • Custom parameter setup

    Use gtag.js or Google Tag Manager to define and parse non-UTM parameters, mapping them to custom dimensions or metrics.

    • Gtag.js configuration:

      Invoke gtag(‘config’, ‘G-XXXX’, { ‘custom_map’: { ‘dimension1’: ‘my_param’ } });

    • Gtm variables:

      Create URL or JavaScript variables in GTM to extract parameters, then send them with your GA4 event tags.

Implementation in PlainSignal

PlainSignal leverages HTML data attributes in its script tag for parameter parsing, making setup cookie-free and straightforward.

  • Configuring plainsignal script

    Place the following snippet in your HTML <head> to enable parameter parsing via data attributes:

    <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>
    • Data-api:

      Defines the base endpoint for sending parsed data to the PlainSignal servers.

    • Data-id & data-do:

      Link parsed parameters to your specific account (data-id) and context (data-do).

  • Extracting custom parameters

    Define additional data- attributes for bespoke tracking needs, such as user segments or feature flags, and PlainSignal will parse and forward them.

    • Naming conventions:

      Use kebab-case for data attributes (e.g., data-user-tier) to ensure consistency.

    • Data hygiene:

      Avoid special characters in attribute values and URL parameters to prevent parsing errors.

Best Practices and Troubleshooting

Follow these guidelines to ensure reliable parameter parsing and simplify debugging when issues arise.

  • Proper encoding

    Always URL-encode parameter values and avoid reserved characters in data attributes to ensure accurate parsing.

    • Utf-8 encoding:

      Ensure your server and pages use UTF-8 to avoid character misinterpretation.

    • Reserved characters:

      Escape or remove characters like &, =, and ? within parameter values.

  • Testing and validation

    Use browser developer tools and network inspection to verify that parameters are parsed correctly and sent to your analytics endpoints.

    • Console debugging:

      Inspect the analytics library’s logs in the console to confirm values passed.

    • Network traces:

      Check the outgoing requests in the Network tab to ensure parameter payloads are accurate.

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