We’re happy to announce we’ve added User Agent Attributions to help you easily identify if certain warnings affect only certain groups of visitors based on their devices and browsers.
New Attributions
We have added several new attributions to enhance the granularity of your data analysis.
These attributions will help you understand and segment your data more effectively, ensuring that 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.
Practical Applications and Benefits
This update applies to the following areas:
Debug Warnings
We've integrated User Agent Attributions into the Debug Warning View, allowing you to pinpoint if specific factors are influencing your warnings. This means more precise debugging and insightful analytics tailored to user agents, making it easier to address issues that might only affect certain groups of users, ensuring a smoother experience for everyone. You can learn more here.
Tracks Explorer
In the Tracks Explorer, you can now analyze how different browsers and devices interact with your tracks. This helps in discovering user-agent-specific patterns, providing deeper insights into user behavior and improving your data-driven decision-making.
Data Explorer
Within Trackingplan’s Data Explorer, you can also delve into how user-agent-specific data affects your overall analytics. This allows for a more granular understanding of your data, helping to uncover trends and issues that are tied to specific browsers or devices.