Attribution

What is Trackingplan

Overview

The Trackingplan Way

Automonitoring

EventsPropertiesUser AttributesPagesAcquisition

Automated Alert System

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Getting Started

Installing Trackingplan

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Post-Install Warm-Up

Dashboard

Dashboard

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Destinations

How to…

Manage Warnings

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Explore your Data

Data ExplorerExplore Tracks

Marketing Observability

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Enhance Collaboration

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Extend detection

TagsChrome Extension

Catch pre-launch errors

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Multi-Account Management

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Others

Trackingplan Public API

Supported Trackers

Release Notes

Attribution

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.

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 Attributions

Country 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.

UTMs

• 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 are using when visiting your website (e.g., email, social media, cost-per-click ads). This parameter is key to answer 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.

Tags

While tags are completely customizable, here are some commonly used tag types:

• app_version: This helps you determine if issues are tied to a specific app version, making it easier to isolate and resolve version-specific problems.
• release_version: This can help you quickly identify whether new issues were introduced with a particular update, allowing for faster fixes and more efficient release management.
• source: Helps you pinpoint the origin of your warnings by tagging them according to different environments, such as staging or production.
• build_number: Allows you to associate warnings with specific builds of your application. This helps in investigating issues linked to particular development cycles, facilitating smoother workflows in your CI/CD process.
• test_name: can help you identify if warnings are tied to specific tests in staging or preproduction environments. This makes it easier to determine if an issue is limited to a particular test or is present across multiple test versions.

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 frontend.

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