The world of web analytics is shifting rapidly. With the rise of AI, the increasing complexity of data collection, and Google Analytics 4 (GA4) failing to meet expectations, businesses are re-evaluating how they measure performance. In a recent podcast discussion with Eric Baudais, a seasoned expert with over 20 years of experience in digital marketing and six years specializing in web analytics, we explored the evolving landscape of analytics, the challenges businesses face with GA4, the rise of alternative tools, and how AI is changing the way organizations interpret data.
The Evolution of Web Analytics
Web analytics has always been a critical part of marketing strategy, but the tools and approaches used to track user behavior have changed dramatically over the years. Traditionally, businesses relied on Universal Analytics (UA) from Google, which provided detailed insights into website traffic, conversions, and user behavior. However, with the forced transition to GA4, many businesses are struggling to adapt.
One of the biggest changes in analytics today is the shift from pageview-based tracking to event-driven tracking. GA4 is designed to be more flexible and powerful, yet many businesses find it more difficult to use. The loss of intuitive reporting, coupled with the need to export data to BigQuery or Looker Studio, has made web analytics more complex than ever.
According to Eric, many marketers have responded by exploring alternative analytics platforms, such as:
- Amplitude – A strong tool for product analytics, focusing on custom events and user journeys.
- Piwik Pro – A privacy-focused alternative that allows businesses to maintain control over their data.
- Microsoft Clarity – A session recording and user behavior tool that provides insights into user experience rather than traditional web analytics.
The rise of these alternatives suggests that Google’s dominance in analytics is weakening, as businesses search for more accessible and customizable solutions.
GA4 and the Challenges It Presents
GA4 was introduced to provide more flexibility in data tracking, better integration with AI, and improved privacy compliance. However, the transition has not been smooth.
Many marketers have found GA4 to be less user-friendly than its predecessor. Universal Analytics provided clear, structured reports, whereas GA4 requires users to create custom reports and manually configure key metrics. Some of the key challenges include:
- Loss of SEO Data – In previous versions of Google Analytics, businesses could track organic keyword performance, but much of this data has been removed.
- Event-Based Tracking Complexity – Unlike UA, where sessions and pageviews were the standard metrics, GA4 uses event-driven tracking. Businesses now need to manually define and configure custom events.
- Lack of Default Reports – Marketers used to rely on default reports in UA. In GA4, many of these reports no longer exist, forcing users to build their own.
- Integration with BigQuery – GA4 heavily relies on BigQuery for detailed analysis. This requires technical expertise and is not accessible to marketers without data engineering skills.
- Data Retention Limits – GA4 only retains data for 14 months unless exported, making historical comparisons more difficult.
This has led to widespread frustration, as many marketers feel they spend more time trying to understand GA4 than actually analyzing data and making strategic decisions.
The Rise of Alternative Analytics Solutions
Given the challenges with GA4, businesses are increasingly turning to alternative analytics platforms that offer more intuitive reporting and control over their data.
- Amplitude is gaining popularity among SaaS and product-driven companies because of its focus on user behavior tracking. Unlike GA4, which tracks website interactions, Amplitude provides deeper insights into how users engage with digital products.
- Piwik Pro is a great option for businesses that want a privacy-first approach. It offers many of the same features as Universal Analytics but with better GDPR compliance and first-party data tracking.
- Microsoft Clarity is not a full web analytics tool but provides valuable insights into user experience by offering heatmaps, session recordings, and behavioral analytics.
Each of these tools has its strengths and weaknesses, but the growing demand for alternatives suggests that businesses are no longer relying solely on Google for web analytics.
AI in Web Analytics – Hype vs. Reality
AI is transforming digital marketing, but how much of its impact on analytics is real, and how much is just hype?
In theory, AI should make analytics easier by automating data analysis, detecting patterns, and generating actionable insights. However, in practice, AI in analytics is still in its early stages. Eric shared some of the realistic and overhyped aspects of AI in web analytics today:
Where AI is Making an Impact:
- Automated Anomaly Detection – AI can quickly identify trends and anomalies in data, such as sudden drops in traffic or unexpected conversion rate changes.
- Predictive Analytics – AI models can forecast future trends based on historical data, helping businesses make data-driven decisions.
- AI-Powered Reporting – Tools like Google’s Looker Studio and AI-powered dashboards help summarize large amounts of data into digestible insights.
Where AI is Still Lacking:
- Contextual Understanding – AI can process numbers but struggles to understand business context. It often generates insights without considering external factors like seasonality or marketing campaigns.
- Accuracy Issues – AI tools still hallucinate data, making up information or misinterpreting patterns. This means marketers must double-check AI-generated insights before acting on them.
- Lack of Personalization – AI-driven analytics tools often rely on pre-set models that do not fully adapt to specific business needs.
While AI is improving analytics workflows, it is not yet a replacement for human expertise. Businesses still need skilled analysts who can interpret data, ask the right questions, and apply insights strategically.
Attribution Models and Measuring Campaign Success
One of the biggest challenges in analytics is attribution—determining which marketing activities are responsible for conversions.
Traditionally, businesses used last-click attribution, meaning the final touchpoint before conversion got 100% of the credit. However, this model fails to capture the full customer journey, especially in industries with long sales cycles.
Alternative attribution models include:
- First-Click Attribution – Gives credit to the first interaction.
- Linear Attribution – Distributes credit evenly across all touchpoints.
- Data-Driven Attribution – Uses machine learning to assign weighted credit based on historical conversion data.
Eric emphasized that businesses should focus less on perfect attribution models and more on identifying trends. No single model is 100% accurate, but tracking patterns over time can provide actionable insights.
Creating a Data-Driven Culture in Organizations
Beyond just tracking numbers, businesses need to build a data-driven culture where analytics informs decision-making at every level.
Eric outlined the key steps to fostering a data-first mindset in an organization:
- Define a Source of Truth – Companies should establish a primary analytics platform to prevent confusion from conflicting data sources.
- Educate Teams on Data Interpretation – Not all marketers and executives understand analytics. Training sessions and workshops can help bridge the gap.
- Move Beyond Reports – Many analysts focus on generating reports but fail to provide actionable recommendations. The goal should be to connect insights with business strategy.
- Leverage Data for Testing – Analytics should drive A/B testing, UX improvements, and conversion rate optimization (CRO) initiatives.
By integrating analytics into every aspect of marketing and business strategy, companies can ensure they are making informed, data-driven decisions rather than relying on assumptions.
How Businesses Can Adapt to the Changing Landscape of Web Analytics and Make Data-Driven Decisions
Web analytics is undergoing a transformation. GA4 has introduced new complexities, leading businesses to seek alternatives like Amplitude, Piwik Pro, and Microsoft Clarity. AI is playing a growing role in automating data analysis, but human expertise remains essential for contextual decision-making.
The future of analytics lies in simplifying insights, improving attribution, and fostering a data-driven culture. Businesses that adapt to these changes and embrace new tools will gain a competitive advantage in digital marketing.
