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Campaign Tracking: The Limitations of Google Analytics 4 (GA4)

Shae Henrie

Content Strategist @ AdRoll

In the fast-paced digital marketing world, understanding your ad campaign’s true impact is critical for staying competitive. Yet, relying solely on Google Analytics (GA) might leave you with an incomplete picture of your campaign's success. In a landscape where every click, impression and conversion holds the potential to reveal valuable insights and drive growth, the stakes have never been higher. Your ad campaigns are more than just a means to an end; they represent the lifeblood of your online presence, pulsating with the power to enhance brand visibility, foster engagement, and bolster your revenue streams. It's time to look beyond the limitations of GA to unlock the full potential of your marketing efforts and gain a comprehensive understanding of their impact.

Enter streamlined reporting tools to act as the beacon that guides you through the intricate maze of marketing data. These tools provide a clearer, more holistic perspective on your campaigns, enabling you to decipher the secrets to success. So, ask yourself: are you truly harnessing the full power of your ad campaigns or settling for a glimpse of their potential?

The Limitations of Google Analytics

Google Analytics is undoubtedly a cornerstone in digital marketing and website analytics. However, it's imperative to acknowledge that this invaluable tool has limitations, which can occasionally pose challenges in accurately gauging the effectiveness of your advertising campaigns. 

Lack of migration support from Universal Analytics

The absence of migration support from Universal Analytics (UA) to Google Analytics 4 (GA4) remains a prominent challenge for businesses and website owners, especially considering UA stopped collecting new data after July 2023. This limitation has significant implications because the flow of historical data from UA to GA4 is no longer an option. As a result, any insights or patterns embedded within the pre-existing data in UA are permanently severed from GA4, creating a profound hurdle for your organization when seeking a unified view of digital operations. 

This lack of migration makes it taxing to identify and analyze trends, correlations, or changes in user behavior that may span across both datasets. To gain a comprehensive understanding of your audience and the evolution of user interactions, you may need to export data from both systems for separate analysis. This cumbersome and time-consuming process introduces operational complexity, hampering real-time decision-making capabilities as historical and new data streams are isolated, limiting the ability to respond swiftly to evolving market dynamics and user preferences. 

Inconsistent user interface

The introduction of a different user interface in GA4 is a significant change that can disrupt many users' workflows. While the new interface is responsive and adaptable to various screen sizes, it departs from the familiarity of the older version, which can pose challenges, especially for marketers accustomed to the previous layout. The shift in format, menu placements, and overall design can create a learning curve, requiring you to invest time in reorienting to the new system. This transition period can result in temporary disruptions and potentially impact productivity as your brand adapts to the altered interface to perform routine tasks and access the required insights.

One notable feature omitted from the new GA4 interface is resizable columns. In the older version, you had the flexibility to adjust column widths to specific preferences, allowing for a customized data view. However, in GA4, this option is absent, which can be frustrating if you previously relied on this functionality to tailor your analytics workspace to specific needs.

Lack of reporting views

Another noteworthy limitation of GA4 is its departure from the Views functionality that was a staple in UA, affecting how your business can segment and analyze data. Unlike UA, where you could create different views of data to isolate specific segments or dimensions, GA4 adopts a different approach. Instead of views, GA4 encourages users to create additional rules or utilize BigQuery integration to achieve similar segmentation and analysis functionality.

Limits on custom dimensions and metrics

Custom dimensions and metrics play a pivotal role in tailoring Google Analytics to the specific needs of businesses. While GA4 does offer the capability to define custom dimensions and metrics, it enforces strict constraints. GA4 restricts users to 25 user-scoped custom dimensions, 50 event-scoped custom dimensions and a maximum of 50 custom metrics for each property.

These limitations challenge businesses with complex tracking requirements and multifaceted data needs. Many organizations seek to gather granular insights and capture a wide array of user interactions and attributes, but these constraints can be restrictive, potentially forcing your business to prioritize certain data elements over others. In cases where comprehensive data tracking is essential, these limitations may necessitate difficult choices regarding what aspects of user behavior and engagement to measure and which ones to forego. 

Changes to attribution models

GA4's attribution model options also look limited from those offered in UA, reshaping how your business analyzes the paths that lead to conversions. In GA4, the traditional "last-click" attribution model, which assigns the entirety of conversion credit to the last touchpoint before a conversion, has been replaced with a "data-driven attribution" model that relies on advanced machine learning algorithms. 

Explore the impact of GA4's Data-Driven Attribution model on your campaigns and gain insights on how to effectively navigate its effects for your upcoming strategies.

While data-driven attribution is designed to offer a more accurate representation of the various touchpoints that contribute to conversions, this shift has notable implications for marketers. One of the key adjustments is that GA4 no longer provides the option to instantly view conversions using different attribution models, such as first-click or last-click. This feature, available in Universal Analytics, allowed organizations to gain insights into the diverse paths users took before converting, making it easier to fine-tune advertising strategies.

The absence of this feature in GA4 can be impactful, particularly if your marketing team is accustomed to analyzing attribution data through multiple lenses. Different attribution models are often used to assess the effectiveness of various marketing channels, helping make informed decisions about budget allocation and campaign optimization. The shift to data-driven attribution, while promising more accurate insights, requires you to adapt your approach to attribution analysis. You may need to rely more heavily on the machine-generated attribution insights provided by GA4, which may not align with their previous models and methodologies.

Data quota and sampling distortion

The removal of third-party cookies, driven by increasing privacy concerns and evolving industry standards, has compelled GA4 to rely more heavily on data sampling and machine learning techniques to gather insights about user behavior. While these approaches have their merits, they also introduce challenges that can impact the accuracy and granularity of the data collected.

One key issue stemming from the increased reliance on data sampling is the potential for reduced data accuracy, particularly when a website experiences high traffic volumes. GA4 employs data sampling to process vast amounts of data efficiently. However, when a website surpasses 500,000 sessions, GA4 may resort to sampling a portion of the data rather than analyzing every data point, leading to less precise data due to potentially missing some user interactions and patterns. Consequently, if your organization experiences heavy website traffic, the data insights become less granular and less representative of the actual user experience.

Moreover, the increased reliance on machine learning algorithms to compensate for the loss of third-party cookies introduces an element of potential bias in the analysis. While machine learning can help uncover hidden patterns and correlations in user behavior, it also requires ongoing refinement and validation to ensure accuracy and relevance. The absence of manual control over data collection and analysis, as possible with third-party cookies, means that your business must trust GA4's machine learning capabilities, which may only sometimes align perfectly with your specific business goals and objectives.

No dedicated customer support

Another significant limitation of Google Analytics comes from a lack of direct, personalized customer support channels, which can present challenges, especially if your organization is grappling with complex analytics issues.

Like in all aspects of life, digital marketing analytics are not immune to mistakes or errors, and when issues or questions arise, they often necessitate timely and specialized assistance for effective resolution. However, the absence of dedicated customer support means you may not have direct access to experts who can immediately solve your unique challenges. Instead, you’ll have to navigate through extensive online documentation and seek answers on community forums, which can be time-consuming and may not always yield precise solutions to the problems.

The Importance of Customer Journey Tracking

Understanding the complete customer journey is paramount in today's data-driven marketing landscape. Every interaction a customer has with your brand, from their initial engagement to the ultimate purchase decision and subsequent post-purchase interactions, represents a treasure trove of valuable insights. This comprehensive perspective serves as a strategic compass for your business, discerning crucial pain points, customer preferences and untapped opportunities for engagement at every stage of the journey.

Its capacity to unveil the intricate nuances of customer behavior and engagement sets a holistic view apart. With this depth of understanding, you can tailor your strategies and campaigns precisely, crafting experiences that resonate more deeply with individual customers. You can also cultivate stronger customer relationships and foster brand loyalty by delivering personalized and relevant interactions.

Integrating data from various sources

Customers engage with your brand through various touchpoints, from websites and social media to email communications and physical stores. Each touchpoint provides a unique window into the customer's journey and behavior, offering valuable pieces of the puzzle. To truly grasp the complete picture, your business will need to overcome the challenge of data fragmentation by integrating information from all these disparate sources, including breaking down data silos and unifying information. 

When data from various sources is seamlessly integrated, it paves the way for a holistic understanding of customer behavior and preferences. You gain the ability to track customer journeys fluidly across channels, transcending the limitations of isolated data sets and empowering you to uncover cross-channel trends and patterns, revealing how customer interactions on one platform influence actions on another.

Data unification enables your business to allocate resources more effectively. By pinpointing the touchpoints and channels that are most influential in driving customer engagement and conversions, you can tailor your marketing strategies to focus on what truly matters. 

The role of unified data

First and foremost, unified data empowers organizations to conduct in-depth analyses. It's not merely about the quantity of data but the quality and relevance of the information it contains. Robust data sets enable you to dive deep into customer behavior, identifying patterns, preferences and pain points. This understanding is a potent tool for crafting campaigns and strategies that resonate with your target audiences.

Additionally, unified data facilitates precise audience segmentation. Effective marketing relies on delivering the right message to people at the right time. By segmenting audiences accurately based on data-driven insights, you can customize marketing efforts to cater to distinct customer groups' needs and interests. This personalization enhances the customer experience and increases the likelihood of conversion and brand loyalty.

Unified data also allows for the measurement of marketing impact. You can track your business’s key performance indicators (KPIs), assess the return on investment (ROI) and refine your strategies based on real-world data. 

Multi-channel Campaign Tracking Tools vs. Google Analytics

Multi-channel campaign tracking tools, such as AdRoll’s Digital Marketing Dashboard, offer a compelling addition to GA4 for businesses seeking more comprehensive and flexible data tracking and analysis solutions. These tools are a noteworthy consideration if you’re looking to gain a competitive edge, bringing a host of features and benefits to the table such as:

  1. Multi-channel tracking: One of the standout advantages of streamlined reporting tools like AdRoll is multi-channel tracking capabilities. Unlike GA4, these tools seamlessly integrate data from various marketing channels, providing a unified view of user interactions across websites, apps and advertising platforms. This holistic approach empowers you to make data-driven decisions based on a more complete understanding of an audience's journey.

  2. Advanced attribution models: Advanced attribution models are another forte of streamlined reporting tools. While GA4 predominantly relies on data-driven attribution, AdRoll offers multiple attribution models, including first-click, last-click, and multi-touch options. This flexibility lets you fine-tune your attribution strategy to align with unique business goals and customer behaviors.

  3. Real-time data updates: Real-time data updates are imperative to effective decision-making in today's fast-paced digital realm. Streamlined reporting tools often provide near-real-time data updates, enabling your business to respond promptly to changing trends.

  4. Enhanced audience segmentation: These tools enable the segmentation of audiences based on various criteria, from demographics to user behavior, helping you adjust messaging for improved engagement and conversion rates.

  5. Simplified reporting and visualization: Streamlined reporting tools offer user-friendly marketing dashboards, intuitive data visualization options and customizable reporting templates, making it easier for your marketing teams to extract actionable insights from data without the complexities that sometimes come with GA4's interface changes.

How AdRoll Can Improve Your Campaign Tracking 

In a world where data fuels every aspect of decision-making, AdRoll emerges as a reliable and powerful ally for businesses seeking to navigate the intricate landscape of digital advertising. Where Google Analytics falls short, AdRoll is a formidable solution, offering a robust Digital Marketing Dashboard that transcends these constraints.

This centralized hub empowers your business to monitor and analyze campaign performance across different channels, facilitating the identification of cross-channel trends and the effective allocation of resources for optimized strategies. AdRoll's advanced attribution models also provide a deeper understanding of the customer journey. With flexibility in choosing from multiple attribution models, AdRoll ensures accurate crediting of touchpoints that contribute significantly to conversions, enabling more intelligent budget allocation and strategy refinement.

Don't miss out on the opportunity to unlock the true potential of your campaigns! Explore AdRoll's Digital Marketing Dashboard today and embark on a data-driven journey toward success in the ever-evolving digital ecosystem.