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Supercharging Growth with 1P Data Strategies


As GA4 steps into the spotlight and Universal Analytics moves backstage, publishers are left grappling with the loss of granular, user-level insights that once illuminated the consumer journey. GA4, designed as a direct response to a growing movement towards securing privacy, shifts away from a cookie reliance in favor of event-based data and machine learning to bridge the gaps. Yet, it still falls short in delivering the granular, user-level insights that UA provided – insights that empowered the industry to make informed decisions and monetize effectively at the user level. More critically, GA4 struggles to accelerate time-to-insight, and the elephant in the room is that the transition from UA to GA4 has been less than ideal. 

 

WIth signal loss estimated to strip $10B from the industry, the cost of inaction is clear. This blog post will delve into how decision intelligence is enabling the industry to reclaim lost signals and reinvigorate first-party data strategies for growth.

The GA4 Challenge: Inaccurate Data

Since GA4 doesn’t look at user-level data, it bridges the gap with machine learning. While this appears innovative on the surface, it has lead to data inaccuracies. 

Machine learning models are only as good as the data they are trained on, and as privacy regulations tighten, the inaccuracies of ML predictions will falter if they do not look at user-level data themselves. For publishers, this means an increased risk of basing decisions on incomplete or skewed insights, resulting in missed opportunities, diminished campaign performance, and suboptimal revenue optimization. GA4’s inherent inability to provide precise, granular data undercuts the very foundation of data-driven decision-making, leaving users with less certainty and more guesswork.


The GA4 Challenge: Data Fragmentation

Another critical challenge with GA4 is its event-based model, which fragments data and obscures the complete view of the user journey. While Universal Analytics allowed for detailed, session-based insights that gave publishers a clear understanding of how users interacted with their platforms over time, GA’s focus on events isolatees data into separate silos. This makes it harder to stitch together a cohesive narrative of user behavior, creating blind spots in the customer journey, and introducing gaps in strategy optimization. Without a holistic view at a user level, publishers are left piecing together fragmented data, making it difficult to gain actionable insights or properly monetize.

The GA4 Challenge: Inherent Bias Towards its own Platforms

One of GA4’s most significant shortcomings is clear bias towards Google’s own platforms, making it difficult to obtain an impartial view of performance across non-Google channels. It is especially evident in the default Data-Driven Attribution (DDA) model, introduced in April 2023. A French whitepaper recently confirmed suspicions of under-attribution, revealing discrepancies of up to 80% between affiliate conversion data and GA4 reports. This stands in stark contrast with the 10-25% variance observed with Universal Analytics (GA4 predecessor.) It strongly suggests that GA4 favors traffic from Google-owned sources, such as Google Search and YouTube.

Self-preferencing isn’t new—regulatory scrutiny from the European Commission resulted in previous adjustments. For publishers relying on diverse traffic sources, like social media, this skewed attribution can distort performance insights and hinder bottom-line growth.

By subtly funneling attribution towards its own ecosystem, GA4 dampers its users’ ability to objectively analyze first-party data and make effective, performance-driven decisions.


SCUBA: The Practical GA4 Alternative

With signal loss on the rise and the talk of the town, it’s clear that the time to act is now. GA4 exacerbates signal loss at a time when the media industry can’t afford to wait.

By seamlessly integrating time-series data with tabular and event data and merging it with third-party systems, SCUBA provides real-time processing that allows the industry to drill down into specific user actions and audience segmentation.



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