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Google Ads Auto-Tagging vs Manual Tagging

Today, we’ll do a Google Ads auto-tagging vs manual tagging comparison.

We’ll see what each tagging option is and what are their pros and cons.

There isn’t a right or wrong choice for every situation.

By the end, you’ll be able to figure out which one will be the best fit for your needs.

In this guide, we won’t go specifically into implementing these tags, but we’ll link to guides that do.

Let’s get right into it.

Understanding Auto-Tagging

Google Ads Auto-Tagging

Auto-tagging in Google Ads is a mechanism designed to simplify the tracking of advertising campaigns. It functions by automatically attaching a Google Click Identifier (GCLID) to the URLs of ads whenever they are clicked. This unique identifier is crucial as it carries detailed data about each click directly into Google Analytics, without any manual input required from the marketer.

The process begins when a potential customer clicks on an ad. Google Ads generates a unique GCLID for that click and appends it to the landing page URL. Once the user reaches the landing page, if Google Analytics is properly set up to recognize GCLIDs, it will parse the GCLID and extract valuable campaign data associated with that click.

This data includes but is not limited to, the source of the traffic (such as the Google search engine or a Google partner site), the specific ad group and campaign the click came from, the search query used, and the geographical location of the user.

One of the most significant benefits of auto-tagging is its ability to provide a wealth of data with minimal effort. Since the process is fully automated, it eliminates the potential for human errors that can occur during manual tagging, such as typos or inconsistent naming conventions.

This automation ensures that the data collected is accurate and reliable, enabling marketers to make informed decisions based on comprehensive insights into campaign performance.

Moreover, auto-tagging facilitates a deeper integration between Google Ads and Google Analytics. By linking these two platforms and enabling auto-tagging, marketers can access detailed reports on post-click behavior, such as which ads lead to conversions, the path users take on the website before converting, and other key performance indicators.

This integration is invaluable for optimizing campaigns, as it allows marketers to understand not just which ads are getting clicks, but which ones are effectively leading to desired actions on their website.

However, it’s important for marketers to be aware that auto-tagging is specifically designed for integration with Google Analytics. If a marketing team uses other analytics platforms or wants to integrate Google Ads data with third-party tools, they might need to use manual tagging or develop a custom solution to parse the GCLID values.

Google’s official documentation on auto-tagging

Understanding Manual Tagging (UTM Tagging)

Google Ads Manual Tagging

Manual tagging, commonly referred to as UTM tagging, stands as a foundational practice in digital marketing for tracking the performance of online campaigns. Unlike auto-tagging, which automates the process, manual tagging requires marketers to append specific parameters to URLs manually. These UTM (Urchin Tracking Module) parameters offer a way to capture detailed information about the traffic sources driving users to a website.

The Basics of UTM Parameters

UTM parameters are text strings added to a URL that track vital information about how visitors arrive at a website. There are five standard UTM parameters:

  • utm_source: Identifies the source of the traffic, such as a search engine or newsletter.
  • utm_medium: Specifies the medium used to deliver the traffic, like email, CPC, or social media.
  • utm_campaign: Names the specific campaign associated with the advertisement.
  • utm_term: Used for paid search to note the keywords for the ad.
  • utm_content: Helps differentiate ads or links that point to the same URL.

By adding these parameters to URLs in your digital ads, you can gain insights into the effectiveness of various campaigns, channels, and content pieces.

Implementation of Manual Tagging

Implementing manual tagging involves constructing URLs with the desired UTM parameters. Marketers can use online tools, such as Google’s Campaign URL Builder, to generate these URLs efficiently. The process includes entering the base URL of the landing page and specifying values for each UTM parameter relevant to the campaign. The tool then combines these elements into a single, trackable URL.

Advantages of Manual Tagging

Manual tagging offers several advantages, particularly in terms of flexibility and data granularity:

  • Customizable Data Collection: It allows for the collection of specific data points tailored to the needs of a campaign or analysis, enabling a more nuanced understanding of traffic sources and user behavior.
  • Cross-Platform Tracking: Unlike auto-tagging, which is optimized for use within the Google ecosystem, manual tagging can be employed across various analytics platforms, providing a uniform method of tracking across diverse marketing channels.
  • No Platform Limitations: Manual tagging is not restricted to Google Analytics; it can be utilized with any analytics tool that supports UTM parameters, facilitating broader data integration and analysis.

Challenges of Manual Tagging

Despite its benefits, manual tagging also presents some challenges:

  • Potential for Errors: The need to manually create and append UTM parameters increases the risk of inconsistencies or errors, such as typos, which can lead to inaccurate data collection.
  • Time-Consuming Process: For large campaigns with multiple ads and targeting criteria, manually creating unique URLs for each variation can be time-consuming and labor-intensive.
  • Management Complexity: Keeping track of numerous manually tagged URLs requires organized management practices, such as maintaining spreadsheets or databases, to ensure that the data remains accurate and accessible.

Manual tagging offers a powerful way for marketers to track the performance of their digital marketing campaigns with precision and flexibility. By understanding how to effectively implement UTM parameters and recognizing the balance between the detailed insights they provide and the effort required to manage them, marketers can optimize their campaigns for better performance and return on investment.

Guide to setting up manual tagging in Google Ads


In the arena of digital advertising, the distinction between Google Ads auto-tagging and manual tagging emerges as a critical consideration for marketers aiming to optimize campaign performance tracking. This detailed comparison sheds light on key areas such as data granularity and accuracy, time and resource efficiency, integration with analytics platforms, and flexibility and customization.

Data Granularity and Accuracy

Auto-tagging in Google Ads is recognized for its capability to automatically append a unique Google Click Identifier (GCLID) to URLs, which directly imports a rich dataset into Google Analytics. This process not only simplifies campaign tracking but also enhances data accuracy, providing a detailed view of user interactions. Auto-tagging’s automated nature ensures consistency in data capture, minimizing human errors and thereby bolstering the reliability of performance insights.

Conversely, manual tagging involves appending UTM parameters to URLs by hand or using tools for bulk editing. While this method offers control over the data collected, it is susceptible to inconsistencies due to potential human error or variations in tagging conventions across different campaigns. The accuracy of data collected through manual tagging hinges on meticulous implementation and adherence to standardized tagging practices.

Time and Resource Efficiency

From a resource standpoint, auto-tagging stands out for its efficiency. By automating the tagging process, it eliminates the need for manual input, saving significant time and resources. This efficiency allows marketers to redirect their focus towards strategic tasks, such as data analysis and optimization, rather than the operational details of campaign setup.

Manual tagging, while offering precise control, requires a considerable investment of time and effort, especially for extensive campaigns. The process of manually creating and appending UTM parameters can become cumbersome and resource-intensive, potentially diverting attention from other critical analytical activities.

Integration with Analytics Platforms

Auto-tagging is seamlessly integrated with Google Analytics, enabling a smooth flow of detailed campaign data into the analytics platform. This integration facilitates an in-depth analysis of campaign performance within the Google ecosystem, offering insights into metrics such as click-through rates, conversion rates, and user behavior post-click.

For organizations relying on a broader set of analytics tools beyond Google Analytics, manual tagging offers the advantage of wider compatibility. Manual UTM parameters can be designed to feed data into various third-party analytics platforms, providing a unified view of digital marketing performance across different channels and tools. However, this broad compatibility requires careful planning to ensure consistent tagging and data interpretation across platforms.

Flexibility and Customization

Manual tagging excels in terms of flexibility and customization. It allows marketers to define their own UTM parameters tailored to specific analytic needs, offering a customized view of campaign performance. This level of control is particularly valuable in complex marketing strategies that span multiple channels and require nuanced performance metrics.

While auto-tagging provides a standardized dataset conducive to detailed performance analysis within Google products, it lacks the same level of flexibility. The predefined parameters of auto-tagging may not align perfectly with all analytic requirements, particularly when integrating data from non-Google platforms.

In conclusion, the choice between auto-tagging and manual tagging in Google Ads should be informed by the specific needs and resources of the marketing team, as well as the analytical tools in use. For those prioritizing efficiency and deep integration within the Google ecosystem, auto-tagging offers a compelling solution. On the other hand, manual tagging provides unmatched customization and flexibility, suited for complex, multi-platform campaigns requiring tailored data tracking. Balancing these considerations will guide marketers in selecting the tagging strategy that best fits their objectives, ensuring effective campaign tracking and optimization.

Pros and Cons



  • Simplicity in Setup: Auto-tagging automates the process of appending tracking parameters, reducing setup time.
  • Accuracy: Eliminates human error, ensuring data consistency and reliability.
  • Seamless Google Integration: Offers deep integration with Google Analytics, enabling detailed performance analysis.


  • Limited Third-Party Compatibility: The GCLID is not interpretable by non-Google analytics platforms, limiting its utility outside the Google ecosystem.
  • Standardized Data Collection: May not meet all analytic needs due to its one-size-fits-all approach to data tracking.

Manual Tagging


  • Customization: Allows for the creation of custom UTM parameters tailored to specific tracking needs.
  • Broad Platform Integration: Data can be integrated with a variety of analytics tools, not just Google Analytics.
  • Control Over Data: Offers greater control over the granularity and specificity of the data collected.


  • Potential for Human Error: The manual process is susceptible to mistakes, leading to data inconsistencies.
  • Time and Resource Intensive: Requires significant effort to manage, especially for large or complex campaigns.
  • Complexity in Management: Handling a large number of custom tags can become cumbersome, increasing the risk of data fragmentation.

Best Practices for Using Auto-Tagging and Manual Tagging

In the strategic domain of Google Ads, the decision between employing auto-tagging, manual tagging, or a hybrid approach is instrumental in shaping campaign outcomes. This section offers insights and guidelines designed to optimize the use of tagging based on campaign objectives and analytical needs, alongside practical advice for maintaining data integrity.

When to Use Auto-Tagging, Manual Tagging, or Both

Auto-Tagging Recommendations

  • Use auto-tagging when your analysis is primarily within Google Analytics. It simplifies the process by automatically importing click data, facilitating a straightforward assessment of campaign performance.
  • Ideal for campaigns aiming for quick setup and minimal maintenance. Auto-tagging requires less oversight, making it a fit for marketers focusing on efficiency.

Manual Tagging Recommendations

  • Opt for manual tagging when your campaigns necessitate data analysis across multiple platforms beyond Google Analytics, such as integrating with CRM systems or third-party analytics tools.
  • When detailed customization of tracking parameters is necessary to align with specific campaign goals or data segmentation needs, manual tagging provides the required flexibility.

Combining Both Methods

  • Consider a combined approach when you need the detailed insights provided by auto-tagging in Google Analytics, along with the flexibility to integrate data with other platforms. This requires enabling auto-tagging for general data collection and using manual UTM parameters for specific tracking needs.
  • Ensure compatibility and prevent data discrepancies by setting Google Analytics to allow manual tagging (UTM parameters) to override auto-tagging (GCLID) when both types of parameters are present.

Ensuring Data Accuracy and Consistency

Maintaining data accuracy and consistency is paramount, particularly when managing campaigns at scale. Adhering to best practices in tagging can dramatically enhance the reliability of your data analytics.

Standardizing Naming Conventions

  • Implement a consistent naming convention for UTM parameters across all campaigns. This should detail campaign source, medium, name, term, and content in a uniform manner, reducing confusion and ensuring easy aggregation of data.
  • Use lowercase for all UTM parameters to avoid discrepancies due to case sensitivity in analytics platforms.
  • Document and distribute your naming convention guidelines among the team to ensure adherence. This minimizes errors and facilitates smoother collaboration and analysis.

Regular Audits and Cleanups

  • Conduct regular audits of your tagging practices to identify and correct inconsistencies or errors in UTM parameter usage.
  • Keep an inventory of all active and past campaigns with their corresponding UTM parameters. This practice aids in historical data analysis and prevents the reuse of tags that could lead to data contamination.

Training and Resources

  • Invest in training for team members responsible for campaign setup and analysis. Understanding the nuances of tagging and its impact on data quality is crucial for all involved.
  • Utilize tools and resources that automate or assist in the creation and management of UTM parameters. Tools like Google’s Campaign URL Builder can simplify the process of generating consistent, error-free tags.


In wrapping up the discussion on choosing between auto-tagging and manual tagging in Google Ads, it’s clear that both approaches offer distinct benefits and limitations tailored to different campaign and analysis needs.

Auto-tagging is highly efficient, reducing manual labor and minimizing errors, making it ideal for advertisers who rely heavily on Google Analytics for tracking and analysis. Manual tagging, on the other hand, offers unparalleled flexibility and customization, essential for campaigns requiring detailed, specific tracking across multiple analytics platforms.

If you liked this comparison, it’s time for another duel: Last-Click VS Data-Driven attribution model.

Michael Schroder

Michael Schroder

Michael Schroder is a Google Ads and SaaS marketing consultant. He has been managing $200k-$300k monthly ad spend and has worked with 200+ SaaS companies. The thing that makes him unique is his data-led approach and his focus on SaaS businesses.