In March 2022, Google announced that it will be sunsetting Universal Analytics (UA) on 1st July, 2023. From this date all standard Universal Analytics properties will stop processing new hits, and users will only be able to access UA properties for historic analysis.
Universal Analytics 360 users will experience the same but will have an additional 3 months of access, ending on 1st October, 2023. Businesses currently using UA are left with two options; migrate to Google Analytics 4 (GA4) or adopt a new analytics solution.
Why is Google Analytics Universal being sunsetted?
Google’s reasoning for sunsetting Universal Analytics is to make way for GA4, which was released over 2 years ago. GA4 is meant to operate across platforms with data privacy at its core – a result of recent GDPR and Schrems II rulings.
- GDPR came into force in 2018. The general aim of the regulation is to protect the personally identifiable information (PII) of data subjects from external threats.
- Then in July 2020, the Court of Justice of the European Union (CJEU) ruled that cloud services hosted in the US cannot comply with EU data laws. This legislation is known as ‘Schrems II’.
- In February 2022, the French CNIL followed suit, finding a French website manager in breach of GDPR. The court ruled that the company in question should stop using Google Analytics under the current conditions.
Despite GA4 having data privacy at its core, there is still on-going uncertainty for Google Analytics users as to whether GA4 is entirely compliant within EU’s regulations. In the absence of a EU-US data transfer agreement (expected with Privacy Shield 2.0), this uncertainty will continue.
For organizations looking for more certainty in how they capture behavioral data, now is the time to consider whether to migrate from UA to GA4, or to invest in a new, future-proof data stack.
Moving From Universal Analytics to GA4: Differences
Reporting Interface
The new version looks different with features being renamed or removed. While you can build customized reports in GA4, it’s a complicated process.
Data model
Universal Analytics captures user behavior as ‘sessions’ and ‘pageviews’, whilst GA4 is based on an event and user data model; all interactions are classed as ‘events’.
Introducing new metrics
Some metrics have been removed, and the way they are reported is different. Bounce rate, for example, has been replaced with metrics such as engagement time and engaged sessions.
‘Goals’ are replaced by ‘Conversion Events’
Goals were typically used to measure events, sales, page views, or funnel performance. Now, you need to tag specific events as goals for reporting purposes.
A greater focus on privacy and compliance
GA4 includes a number of features designed to facilitate compliance with GDPR. These include an improved data deletion mechanism, data retention settings, and updated data processing terms.
Data-driven attribution
GA4 includes ‘data driven attribution’ by default, distributing credit for conversions based on data for each conversion event. You’re also able to retroactively apply attribution models in order to analyze historical data.
Alternative Solutions to Google Analytics
Google Analytics has held the title of industry standard for digital analytics for years, bringing self-serve digital analytics to the masses.
Data teams reliant on Google Analytics as their central source of truth are constantly challenged by the changing privacy and regulatory environments and the accessibility to their data. The modern data team needs to go beyond the Google Analytics UI and use raw behavioral data to understand customer behavior and power wider BI, and AI data applications.
Some alternatives to Google Analytics include Plausible, Fathom, Objective, Jentis, Matomo, and Piwik Pro.
Hosted in the EU, these technologies give more assurance to organizations regarding GDPR compliance and allow them to track (mostly) what they need to. They can also be self-hosted, and some of them allow for data to be exported to data warehouses.
And whilst these technologies facilitate compliance with data privacy measures, they’re often held back by the same issues experienced with GA4. Many of them, for example, struggle at collecting data at high volumes; Matomo still runs on MySQL, which isn’t suitable for high-scale analytics queries.
It’s also worth noting that tools specifically focused on privacy tend to have ‘thin’ analytics data. Relying on cookieless tracking, they lack session and user data, which prevents effective marketing attribution. You might also notice that they tend to lack comparable mobile SDKs.
So while these tools are great alternatives to Google Analytics, it’s important to keep all of these factors in mind when thinking about adopting a new tool.
Snowplow BDP as an Alternative
Businesses are changing their approach to analytics – evolving their data stack to orbit around the cloud data warehouse (such as Snowflake). They recognize the need to extend beyond packaged analytics to create a central source of truth to understand their unique customer behavior and power broader advanced applications.
Rather than relying on data extracted from analytics tools and CRMs (‘data exhaust’), they’re creating the data needed to run specific applications. This means generating, enhancing and modeling best-in-class behavioral data with data cloud capabilities, allowing businesses, their data science teams, and analytics power users to have a platform on which to develop a deep understanding of their users.
Using Snowplow combined with a cloud data warehouse, data teams can spend their time driving value from their data rather than overcoming the challenges faced using Google Analytics and other packaged solutions.
Elevate your analytics with Snowplow BDP, rather than simply migrating to the next Google product.
Why Snowplow BDP?
1. Data Structure
For the modern data team to be successful they need access to granular, rich behavioral data to understand customer behavior and power wider BI and AI use cases.
While Google Analytics is great to use within the platforms (GA4 & 360), data teams that want to answer more complicated questions with their data struggle to do so. Exporting data from its native Google Cloud Storage to other destinations is extremely complex as the data structure is nested. As a result of this heavily nested data structure, dozens of lines of SQL are required to ask even basic questions.
This causes data teams to have to restructure and wrangle the data in order to be able to use it for their more complex advanced applications. 80% of data scientists’ time is taken up by data preparation. This means that more time is spent wrangling data than driving value with data and discovering insights into their customers.
Snowplow’s AI/ ML-ready data is generated in a structured way that allows for data teams to get straight to running their advanced applications without the need for any data preparation or wrangling.
Snowplow offers real-time data streamed directly into your destination of choice so that any time sensitive advanced applications can be running accurately at all times. For example, recommendation engines that rely on what a user has been viewing within a current session will have sufficient data to power accurate recommendations as a result of Snowplow’s low data latency. (We have latency SLAs guaranteeing loading in as little as 15 minutes, depending on your storage location and tier).
2. The Richness Of User Level Data
With Snowplow, each event and entity is automatically collected with 130 properties, with the ability to track as many entities with each event, with as many data points per entity as you like. This stands in contrast to GA4, which offers only a limited amount of properties with every event.
Snowplow’s event tracking is extremely detailed and granular, providing behavioral data that goes far beyond out-of-the-box web analytics platforms. With our technology, you have the flexibility to define custom events that are precisely suited to the needs of your business.
Depending on the conversion event, GA4 provides a lookback window of 30-90 days. While this may seem like a decent amount of time, it doesn’t compare to Snowplow’s 2 year lookback window. This allows businesses to have a much longer overview of what their users are up to, giving them more information to make strategic business decisions.
We natively guarantee real-time collection in Snowflake (and other destinations) to ensure that whatever advanced applications you’re wanting to execute, can be done as soon as possible. Snowplow BDP customers benefit from an uptime SLA of up to 99.99%. Uptime refers to “collector uptime” i.e. the availability of the Snowplow collector, that receives data for processing from different sources.
Read more on our SLAs from our product description page.
Not only is the data Snowplow generates organized in different rows for each event, but data is also loaded into a different table for each day. This provides clear visibility over all the data helping to ensure its accuracy.
Explore what Snowplow data looks like.
3. Privacy And Regulatory Environments
Privacy and compliance is extremely important to Snowplow – understanding the privilege that comes with users consenting to have their data being tracked. Snowplow has more accurate tracking of anonymous users across different browsers that is done in a way that doesn’t exploit a user’s wishes and is not impacted by ITP/ ETP.
With Snowplow, businesses are able to support the anonymization and pseudonymization of users. Pseudonymized data is very valuable from a GDPR perspective. For example, a company collects data to support product development but does not engage in targeted marketing;, a marketer cannot accidentally use thise data to target an individual user.
Consent is a significant factor that Snowplow takes into account, not just from a legal perspective but from an ethical and moral one too. End-to-end private cloud deployment and privacy tooling such as PII pseudonymisation and cookieless tracking enforce governance on the data and socialize it in a compliant manner. Each line of data is enriched with a basis for capture so it is unambiguous how it can be used.
Snowplow also offers full data and model ownership – as the data is streamed directly into your cloud data warehouse, and you have full control over access permission levels.
Futureproof your behavioral data
We believe the sunsetting of Universal Analytics presents organizations with an opportunity to ‘do more’ with data. Rather than migrating to GA4 and experiencing the limitations of a packaged analytics platform, innovative teams can build their own data stack with a suite of best-in-class tools. With Snowplow BDP at its core, they can generate, enhance, and model rich behavioral data to fuel a limitless number of sophisticated data apps.
