How To Overcome GA4 Limitations for Better Measurement In Ecommerce

Standard Universal Analytics (UA) properties have ceased processing data, prompting e-commerce marketers to migrate to Google Analytics 4 (GA4), the next generation of website and app measurement. GA4’s privacy-centric, event-based data collection offers a clearer picture of the user journey, crucial for e-commerce businesses facing evolving regulations.

With machine learning insights and direct integration with media platforms, GA4 provides a comprehensive view of marketing performance. Relying on outdated UA frameworks can lead to incomplete data, hindering growth.

This blog explores GA4’s features and limitations and compares UA vs. GA4 to help e-commerce marketers transition smoothly and leverage the latest measurement capabilities.

Understanding GA4

GA4 was created to adapt to evolving measurement standards and help businesses succeed.

Universal Analytics, primarily tailored for desktop-accessible websites, relied on individual sessions and readily available cookie data, which would limit its effectiveness in today’s dynamic online environment.

This approach is now obsolete because there’s a need for safety and precaution from privacy-conscious users.

Key features of GA4 compared to Universal Analytics

To view the range of different and similar features for both, here’s a comparative table for UA vs GA4.

Features	Universal Analytics (UA)	Google Analytics 4 (GA4)
Data Model	Session-based with hit types (pageviews, events)	Event-based
User Metric Point	Considers two types of users: total users and new users	Considers three types of users: active users, total users, and new users
Data Storage	Stores your data forever, and data appears within a day	Stores data for up to 14 months, and data takes two days to show
Data Collection	Relies on cookies (privacy concerns)	Prioritizes privacy with cookieless measurement and data modeling
Platform Focus	Desktop web-centric	Cross-platform (websites & apps)
User Journey Tracking	Limited	More granular tracking of user actions (events)
Machine Learning	No	Leverages machine learning for insights and predictions
Reporting	Pre-defined reports with limited customization	Flexible reporting with exploration tools
Future Support	Is obsolete	Actively developed—the future of Google Analytics

GA4 tracks everything and more than UA ever could. However, transitioning from UA to GA4 is difficult because setting up configurations isn’t beginner-friendly, and the UX is entirely different.

The fundamental distinction between GA4 and UA lies in their data-capturing methods:

• GA4 tracks event-based data, as in everything that happens on your website is tracked as one event
• UA is primarily a session-based model, where interactions are grouped as a whole, missing out on singular events like watching videos, clicking on links, etc.

The data model significantly impacts how data is analyzed and reported. While you can track the entire analytics funnel with both, there is one significant change: how goals and conversions are set up for each.

In UA, any page could be set up as a goal by adding its URL. Any visitor who touched the page was recorded as a conversion. GA4 converts an event into a conversion with the toggle of a switch. This is much more flexible but tougher to set up, as you’ll need to create an event and wait for it to take place so it appears on the list.

GA4 is continuously evolving and isn’t a complete product. Learning its advanced features will help you grasp the technology before it’s too late and the world moves on from using it as a primary analytics platform.

Advantages of integrating mobile app and website tracking

UA’s separate reports created a lot of chaos. Now, mobile app and website tracking reports will be consolidated. This dual integration in GA4 enables a greater understanding of your user journey to drive better marketing strategies. Here’s a detailed breakdown of the key advantages:

• Removal of silos: Website and app data formerly resided in separate platforms, creating a fragmented view of user behavior. GA4 merges data from both sources to see how users interact across platforms
• Cross-platform analysis: Imagine a user browsing your website on their phone and downloading your app later to make a purchase. GA4 tracks this entire journey, from users engaging with your brand across different touchpoints
• Improved attribution modeling: With more accurate attribution models, understand which touchpoints (website ads, app features, first-time visitors) contribute most to conversions so you can allocate marketing budgets accordingly
• Unified audiences: Create audience segments based on user behavior across your website and app to attract more customers
• Personalized experiences: Use user data to personalize the experience across different touchpoints. This helps you to recommend relevant products based on browsing behavior or offer incentives to encourage app downloads
• Reduced complexity: Quit juggling between platforms and access all user data or measurement settings in one place. The accuracy of your insights improves with consistent data collection

GA4’s approach to privacy with first-party cookies and AI-driven insights

Unlike UA, new features like conversion modeling in Google Ads use machine learning to fill in data gaps when users consent to limited data collection. This ensures campaigns are optimized for performance, even in a privacy-first world.

Suppose you’re checking metrics in GA4, but some data is missing due to privacy settings. Modeled data, such as smart estimates, will fill in the gaps. Combining the actual data (observed data) with these estimates (modeled data) gives you a complete view of the consumer journey from start to end.

GA4 will introduce data-driven attribution models powered by Google’s machine-learning insights. This feature is a game-changer, providing a clear understanding of how each touchpoint in your marketing funnel contributes to conversions.

However, Google is actively working on compliance updates and improvements. With GA4, your measurement foundation will be able to meet the changing demands of the marketing ecosystem.

The Limitations of GA4

Even though the benefits of GA4 are undeniable, early adopters and marketing experimenters have noticed Google Analytics limitations within the platform. Fortunately, there are solutions to fill in the gaps.

Many SEOs find GA4 to still be a tremendous downgrade in both data and reporting from the previous version of Google Analytics. Some search marketers noted that commonly used features are buried within the user interface, making it difficult to access. Others are frustrated that GA4 does not have current data and that third-party add-ons still don’t work.

Also, many digital marketers yearn for the return of the old Analytics dashboard. In fact, a recent survey showed that over 75% of SEOs were not happy with the new product.

survey-conduct-by-barry-schwartz-on-google-analytics4-through-linkedin

Challenges with GA4’s privacy compliance across different countries

GA4 is indeed suffering massive roadblocks when it comes to getting on the same page of privacy compliance across different countries, primarily because of evolving rules and regulations across the globe.

It is not a secret that AI and machine learning have exposed people to cybersecurity risks like deepfake imaging, enhanced data extortion, advanced money laundering scams, and more. Any breach or lack of compliance from GA4 toward privacy safety will make a marketer’s job difficult in such a situation.

There is a glaring absence of a single source of truth for data privacy, which poses a significant challenge for GA4 in implementing a universal approach to data collection worldwide.

With Google still making changes to the platform, the complexity and challenges involved in establishing strong, universally compliant regulations with the laws of every country on the continent are evident.

Lack of view-through attribution to understand the full impact of ads

GA4 lacks a built-in view-through attribution, which means you can’t track actions taken by the users who’ve seen an ad but did not click it. You won’t be able to monitor ad effectiveness or know the reason for the drop-off.

Without view-through attribution, sole credit will go to the last click (like a website visit) for a conversion, overlooking the role of earlier ad impressions in influencing the user’s decision.

You can solve this problem with view-through attribution with a workaround like setting longer conversion windows or integrating third-party apps. Consider the additional costs of external integrations until view-through conversions are incorporated in GA4.

The impact of data sampling on marketing analysis

Unlike UA, GA4 is less dependent on sampling data for larger datasets and prioritizes processing data whenever possible. However, sampling can still occur in large datasets or complex reports.

GA4 employs a small data sample to provide quicker insights in real time. Avoid making critical decisions based solely on real-time reports, even if it’s convenient. GA4 employs sampling to ensure efficient processing to gain granular data or use large datasets within custom reports.

Focus on analyzing smaller and well-defined audience segments to lessen the risks of data sampling in GA4. The GA4 report has features to increase data collection limits, reducing the chances of sampling.

Don’t get held up by focusing on specific data points that may be affected by sampling data; instead, look for broader trends and patterns that emerge with time to draw logical conclusions.

The simplification of attribution models in GA4 and its implications

Attribution models available in UA were pre-defined, such as last click, bounce rate, etc. In GA4, a single data-driven attribution model uses machine learning to assign credit for conversions across all touchpoints in a user’s journey.

This simplified approach has improved accuracy, reduced time and effort with automated attribution, and provides a complete view of different marketing efforts and their contribution to conversions.

GA4 also offers customizable attribution models that can be tailored to specific needs. However, GA4’s attribution models still need to be developed further to offer more control, transparency, and flexibility to fine-tune attributions according to marketing goals.

You can create custom attribution models for specific conversion events, but this will require a dedicated technical team to develop, employ, and monitor the mechanisms.

The importance of incrementality in GA4

Let’s understand incrementality in marketing with a simple example.

Say you’re running an ad campaign, and it generates a certain number of conversions (likes, downloads, etc.). But how will you know that your ad solely drives these conversions, or would some of them have occurred anyway (through organic traffic, brand awareness, etc.)?

Incrementality answers this question, as it is the additional value–an incremental lift, that your marketing campaign converts on top of what would have happened without the campaign.

Incrementality makes it easier for marketers to stop overestimating their ad effectiveness, properly allocate marketing budgets, and justify spending.

GA4 has powerful analytical abilities, but it lacks built-in ad effectiveness functionalities like A/B testing or controlled experiments to isolate the impact of ads and understand their true worth.

Advanced data scientists need to be recruited to build specialized statistical modeling techniques to estimate incrementality. At the same time, third-party integrations can be used to account for extra expenses in the marketing budget.

Why Is a Blended MTA and MMM Approach Superior?

Picture this: you are using a bunch of different marketing tools for various purposes. Unified marketing measurement (UMM) offers a blended approach that uses a combination of deterministic and probabilistic methods to give precise and reliable results.

The integration of various marketing analytics and models provides a comprehensive understanding of the effectiveness of marketing campaigns in driving conversions. With unified measurement, marketers can get their hands on the metrics that allow them to optimize marketing spend and get the most out of their campaigns.

These omnichannel marketing measurement methods include surveys, marketing mix modeling (MMM), incrementality testing, last-click attribution, or multi-touch attribution (MTA). While surveys and last-click attribution have provided valuable insights, they can’t beat newer and more advanced techniques that offer better accuracy and a deeper understanding.

UMM combines aggregate data from various methods, such as marketing mix modeling, with person-level data from multi-touch attribution to provide a more accurate and detailed understanding of a marketing campaign’s effectiveness.

With Lifesight’s UMM platform, you’ll get all the tools in one platform plus third-party integrations without spending extra money. Furthermore, marketers can optimize cross-channel performance, discover deeper customer insights for enhanced personalization, make quicker decisions with real-time data, and comprehensively analyze the entire marketing funnel.

This results in a more logical allocation of marketing spend, improved ROI, and a stronger customer connection.

Conclusion

While GA4 focuses on marketing measurement, it is not a holistic marketing measurement tool. The evolution of measurement requires a more comprehensive approach. Lifesight offers an AI-powered unified marketing measurement platform that will change how you drive marketing success in the privacy-first era.

You can see Lifesight in action by booking a demo today.