Attribution

Trackingplan supports a wide array of attributions designed to streamline the filtering and debugging of your analytics data. With these attributions, you can pinpoint exactly where errors compromising the quality of your data have occurred, understand why they happened, and take the necessary steps to resolve them.

🛠

To enjoy the full functionality of Trackingplan Attributions, please ensure that your Web SDK script is updated to the latest version. If you need assistance or have any questions, feel free to contact us for more information and support.

You can check the current version of your SDK by clicking on the tp_sdk_version in your Data Explorer.

Attributions

Page Attributions

Page attributions allow you to filter and analyze data based on specific pages within your website or application. By associating events with individual pages, you can quickly identify which pages are driving conversions, errors, or specific user behaviors.

Domain Attributions

Domain Attributions are especially beneficial for panels that receive traffic from multiple domains, allowing you to filter and analyze data more effectively on a per-domain basis. This is particularly useful for users managing complex analytics across different domains, providing more granular control and insights into each domain's performance.

🛠

As an additional benefit, you can opt-in to use domains as prefixes for page attributions. This will allow you to clearly distinguish which domain a page belongs to. To enable this feature, simply reach out to us at support@trackingplan.com.

Referrer Attributions

Referrer attributions track where users are coming from before arriving at your site. This helps you understand which external sources are driving traffic, whether from search engines, social media platforms like Facebook or Twitter, or other referral sites that link to your content.

By understanding which external sources are driving visitors, you can evaluate which channels are most effective at bringing people to your site and make better decisions for marketing strategies.

Landing Attributions

Landing attributions identify which pages users land on when they first visit your site. These are critical for analyzing the effectiveness of landing pages in converting traffic into leads or sales.

Last Click Attributions

Last Click Attributions help you analyze the final interaction a user had before triggering an event, providing key insights into user behavior.

Last_click_path: Useful for identifying the path clicked when your events are triggered, revealing navigational patterns before an event.

Last_click_text: Identifies which text was clicked before triggering an event. This attribution is particularly useful for pinpointing issues in buttons and CTAs when debugging your warnings.

Country Code Attributions

Country Code Attributions will help you easily identify if certain warnings or issues are affecting specific groups of visitors based on their location.

This is particularly useful for quickly pinpointing if location-specific factors are influencing your warnings, analyzing how different countries interact with your tracks, discovering location-based patterns, and gaining insights into how location-specific data impacts your overall analytics to tailor your strategies to different geographic markets.

Language Attributions

Understanding the language preferences of your users is essential for delivering personalized and localized experiences. By tracking language-related data, you can tailor your content, user interface, and messaging to meet the needs of diverse audiences, ensuring a more seamless and engaging user experience.

navigator.language: Tracks the language preferences of the users interacting with your apps or websites. This information provides valuable insights that help you optimize the delivery of content and services in the right language.

User Agent Attributions (Device and browser attributions)

User Agent Attributions can help you easily identify if your warnings affect only certain groups of visitors based on their devices and browsers.

These attributions will help you understand and segment your data more effectively, ensuring you can identify and act on user-agent-specific trends and issues.

user_agent.original_string: This attribute contains the raw user agent string sent by the browser or device, providing you with the full, unprocessed user agent information for detailed custom parsing and analysis if needed.
user_agent.parsed_string: This is a cleaned-up and standardized version of the original user agent string. A parsed string simplifies the raw data, making it easier to work with and ensuring consistency in data handling and analysis.
user_agent.browser.family: This attribute identifies the family of the browser (e.g., Chrome, Firefox, Safari).
user_agent.browser.version_string: This attribute specifies the exact version of the browser being used. Version-specific information is crucial for identifying bugs, security issues, and performance differences that may exist between different versions of the same browser.
user_agent.device.family: This attribute identifies the general category of the device (e.g., iPhone, Samsung Galaxy, etc.). Understanding the device family helps in tailoring the user experience and identifying device-specific issues or preferences.
user_agent.device.brand: This attribute indicates the brand of the device (e.g., Apple, Samsung, Google, etc.). Brand information can be used for market analysis, user segmentation, and understanding brand-specific behaviors and issues.
user_agent.device.model: This attribute provides the specific model of the device (e.g., iPhone 12, Galaxy S21). Model-specific data allows for detailed analysis and optimization, ensuring the best performance and user experience for different device models.
user_agent.device.type: This attribute classifies the type of device (e.g., mobile, tablet, desktop). Device type classification is essential for responsive design, user interface adjustments, and understanding the context in which users interact with your service.
user_agent.device.is_bot: This boolean attribute indicates whether the device is a bot or a real user (e.g., true for Googlebot). Identifying bots is crucial for maintaining accurate analytics, as bot traffic can skew data and impact performance metrics.

Measurement ID

A measurement ID in Google Analytics is a unique identifier for a web data stream. Its format in Google Analytics 4 is 'G-' followed by a combination of numbers and letters, such as 'G-PSW1MY7HB4', and it acts as a critical link, connecting your website to the corresponding data stream in Google Analytics 4 and ensuring your website data is sent to the right location.

☝🏻

Pro tip: Trackingplan allows you to split the traffic of your destinations by their measurement ID, allowing you to view traffic from each ID separately as a new destination, or even exclude specific account_IDs from any processing to prevent traffic from reaching your dashboard. Learn more about splitting destinations by Measurement ID here.

UTMs

💡

Trackingplan also supports a wide array of UTM attributions to track and easily assess the performance of different campaigns and sources, allowing for more precise optimization of your marketing strategies.

utm_id: Easily track campaign IDs with the new utm_id attribution, giving you greater visibility into your campaign performance and effectiveness.
utm_campaign: This tracks your marketing campaign name, indicating the specific marketing campaign, promotion, or ad that triggered your visitors’ clicks. Given the multitude of campaigns that marketing teams typically handle simultaneously, this parameter provides the necessary detail to track and compare individual campaign performance.
utm_medium: This parameter helps you identify which medium your visitors use when visiting your website (e.g., email, social media, cost-per-click ads). This parameter is key to answering which channels are driving the most valuable traffic to your website.
utm_source: The source parameter complements the medium by offering more granularity about the origin of website traffic. For instance, given that 'social' can represent various platforms, the source parameter is where you’ll be able to narrow down the specific platform your visitors originated from (Facebook, Twitter, LinkedIn, Instagram, etc.).
utm_term: UTM term tracks the specific keywords or terms used in your paid search campaigns.
utm_content: This attribution allows you to monitor different content variations (e.g., different ads or CTA buttons) within the same campaign. It helps you understand which elements resonate best with your audience and refine your messaging for improved engagement.
utm_source_platform: Identify the source platforms driving traffic to your campaigns, enabling you to allocate resources effectively and maximize ROI.
utm_gclid (Google Click Identifier): This parameter tracks individual clicks from your Google Ads campaigns, providing insights into their performance and ROI.
utm_fbclid (Facebook Click Identifier): Similar to gclid, this parameter is added by Facebook Ads to track individual ad clicks, helping you measure the success of your Facebook campaigns.
utm_msclkid (Microsoft Click Identifier): Used to track clicks from Microsoft Ads, this parameter enables you to assess the effectiveness of your Bing or Microsoft-based ad campaigns.
utm_ttclid (TikTok Click Identifier): This attribution tracks clicks from TikTok Ads, helping you analyze the performance of your TikTok campaigns and optimize your strategies for this platform.

Consent Options

Trackingplan helps you efficiently manage consent actions, ensuring compliance with data protection regulations like GDPR or CCPA. By seamlessly integrating with Google's Consent Mode, Trackingplan allows you to keep track of user consent preferences without hassle.

This is particularly useful for analyzing event behavior depending on whether users accept or reject cookies, ensuring you're not sending tracks to events whose consent for storing information has been denied.

Google Consent Settings (gcs): This reflects the overall consent state of the user, summarizing whether they have given or denied permission for data collection
Google Consent Data (gcd): This provides more granular details about what specific types of data the user has consented to collect, such as analytics or advertising data.
Ad Storage: This relates to the user's consent for storing advertising-related data (such as data for personalized ads).
Analytics Storage: This refers to the user's consent for storing analytics-related data.
Ad User Data: This refers to the user's consent for the collection and usage of their personal data for advertising purposes.
Ad Personalization: This refers to whether ads shown to the user are personalized, based on the consent they’ve provided for data collection.

🤔

Learn more about Google’s Consent Mode & Trackingplan here.

Properties

Trackingplan also allows you to explore your data and warnings based on your properties, offering enhanced control over how different properties are tracked in your events.

That way, you’ll be able to delve into the behavior of your events by filtering by the values of their properties.

DataLayer

Similarly, Trackingplan also allows you to explore your data and warnings based on the Data Layer to have a granular view of the data passed between your website and analytics tools.

This will offer you a real data representation with browser-captured context, useful for easily identifying improperly formatted data on your website or identifying whether errors originate from the tag manager or the front end.

Query String

When clients send data to an endpoint (for example, to GA4), a request is made that includes a query string. The query string is part of the URL, and it consists of a set of key-value pairs that convey additional information to the server. These parameters are essential for tracking user interactions and session details.

A typical query string appears after the ? in a URL and is made up of parameters and their corresponding values. In this example, utm_source, utm_medium, and tid are parameters, with values like google, cpc, and G-XG12345.

https://example.com/page?utm_source=google&utm_medium=cpc&tid=G-XG12345

By examining query string parameters, you can gain insights into user behavior, identify potential tracking issues, and ensure accurate data flow.

To help you investigate issues and analyze data, we’ve added the ability to filter by query string parameters in both Tracks Explorer and Data Explorer. This enhancement allows you to filter tracks based on query string values, just as you can filter by attributions, UTMs, consent settings, tags, and more.

☝🏻

In GA4, the tid (tracking ID) parameter is often used in the query string to associate hits with specific property IDs. That’s why filtering your tracks based on query string values would allow you to see all the tracks with a specific GTM container ID ("Show me all tracks where the query string contains tid=G-XG12345").

Post Payload

In POST requests, data is sent to an endpoint as part of the post payload.

The post payload is usually sent as part of the body of a POST request and can be in various formats, such as JSON or URL-encoded form data. Unlike the query string, which is visible in the URL, the post payload is typically not visible in the browser address bar and typically contains more complex, structured data.

For example, in a POST request to GA4, the payload may contain information about an event such as user interactions, session details, or e-commerce transactions that are critical for debugging issues related to data being sent to analytics platforms.

Here’s an example of a POST payload sent to a GA4 endpoint for an e-commerce transaction event:

{
  "en": "purchase",
  "ep.platform": "WEB",
  "ep.business_unit": "trackingplan",
  "ep.page_type": "PLP",
  "ep.page_brand": "Multi Brand",
  "ep.page_system": "Firefly",
  "ep.page_subtype": "shoes",
  "epn.page_total_products": "287",
  "ep.app_version": "WEB",
  "ep.page_sorting": "Recommendations"
}

To make it easier to troubleshoot and refine your data, we’ve added the ability to filter by post payload in both Tracks Explorer and Data Explorer. This allows you to search for specific payload parameters and view detailed information about the actual values that are processed and reported, such as event names, parameters, and custom data points.

☝🏻

If data is malformed or missing in the payload, it can result in inaccurate reporting. If you’re sending custom parameters in a post payload (e.g., a product ID or category), you can filter by those parameters to ensure they’re being correctly included in requests. Another option, when tracking product views or purchases, would be to filter by items in the post payload to verify that the correct product information is being sent, such as the product SKU or price.

Example

In GA4, there’s a parameter called “session_start”, which is sent to indicate the beginning of a session. This information is not part of the event itself but rather technical metadata. Yet, ensuring this parameter is sent correctly is crucial, as failing to include it or sending it incorrectly can break your analytics. For example, if “session_start” is missing or misconfigured, session tracking will be inaccurate, leading to flawed analytics reporting.

This issue can occur for many reasons, such as misconfigurations in your GTM setup or errors in how the parameter is implemented. With Trackingplan, you can now easily verify if “session_start” is being sent correctly, proactively identifying and fixing potential problems in your session tracking.

← Previous

Debug Warnings

Customize Warnings

Attribution

Attribution

Attributions

Page Attributions

Domain Attributions

Referrer Attributions

Landing Attributions

Last Click Attributions

Country Code Attributions

Language Attributions

User Agent Attributions (Device and browser attributions)

Measurement ID

UTMs

Consent Options

Tags

Properties

DataLayer

Query String

Post Payload

Example