Many budding businessmen believe that they have plenty of resources and channels to gather and hoard all relevant data, such as financial systems, marketing platforms, CRMs, and survey tools. However, they fail to realize that having all dossiers at their fingertips isn’t enough. They must be able to analyze them and draw actionable conclusions. Data analytics tools are second-to-none mechanisms that use the available information to fathom consumer behavior and step up shopfloor procedures and customer experience.
The usefulness of this software type is coming home to an ever-increasing number of stakeholders in the e-commerce industry, causing a steady growth of revenues in the sector.
When it comes to analyzing the digital performance of sites, entrepreneurs rely on different tools, the two most popular of which are Google Analytics and Adobe Analytics. The world might not need a new Adobe Analytics vs. GA4 post, but since we haven’t found any sources that provide an impartial comparison, we wanted to provide our take on it. We were also surprised that most posts we found don’t consider the differences between GA4 and Universal Analytics when comparing Adobe Analytics vs. GA4. Since their features and capabilities are hardly similar, they should be compared individually instead of as the same product.
With this in mind, we want to provide a comparison with two goals: make the comparison as fair as possible, and account for feature differences that GA users will find between GA4 and Universal Analytics.
Meet Google Analytics
This brainchild of the Google family was launched in 2005 and has undergone a series of transformations and upgrades to evolve into a powerful tool leveraged by 56.7% of websites and taking up over 70% of the entire market in this niche.
GA4 is the latest version of Google Analytics, while Universal Analytics (UA) is the previous version (some people call it GA3), released at the end of 2012. In case you don’t know, Universal Analytics will be discontinued for free accounts on the 1st of July 2023 and on the 1st of July 2024 for 360 properties. GA 4 has machine learning algorithms built into its core which enables the tool to detect data trends of diverse nature – from predicting customer churn rate to calculating potential revenues.
How does GA 4 work? It monitors all data streams across both apps and websites to expose user interactions via event-based tracking. This capability allows analysts to follow changes in real-time and not draw upon site searches or website tags. When app and web analytics are aggregated in one place, entrepreneurs obtain a complete picture of their marketing efforts and can fine-tune their general business strategy to improve the efficiency of the sector which underperforms.
An undeniable asset of Google Analytics is its privacy-centric design. It means that changes in privacy legislation or cookie usage won’t hamstring its performance. Besides, it makes the tool flexible enough to fill out gaps in incomplete sets of data.
Universal Analytics vs. GA4
There are several key differences between the two that are worth noting. To the core of the changes GA4 introduced is a new tracking approach that aims to consolidate data between websites and applications. This new approach impacts different aspects, from the metrics it uses by default to the measurement model, and it also adds some new exciting features:
Data collection
GA4 uses a new measurement model based on users and events instead of sessions and pageviews. This gives more control over the data tracking and allows a more granular analysis. More importantly, because the new model is compatible between mobile apps and websites, you can track and report across them in a single place.
User-ID tracking
In Universal Analytics, you had to use one property for users and another for sessions, and this will no longer be the case. GA4 uses a built-in User-ID feature, enabling cross-device and cross-platform tracking of users in the same GA4 property.
Engagement
GA4 moves away from using bounce rate and the number of page views to assess traffic quality and introduces the concept of engagement. We are big fans of this metric and are surprised it wasn’t introduced before.
BigQuery integration
Unlike Universal Analytics, the GA4 has a native connector to BigQuery, even in the free version. This is a big deal, as it allows in-depth analyses using SQL and combining the GA4 data with other data sources effortlessly (on the GA4 side, of course).
Explorations
With them, you can generate custom tables and visualizations combining dimensions and metrics in a drag-and-drop interface. It has exciting options like cohort analyses, funnels, segment overlaps, and paths. While the idea is excellent, the UX needs some love: it isn’t super smooth, you need to add the variables before using them, and it isn’t very agile overall. It works for custom reports you want to return to, but it isn’t enjoyable when doing analyses.
On the other hand, some Universal analytics features are not fully supported in GA4 or came short in this first release:
Reporting interface
The set of reports included isn’t as comprehensive as in UA. Many users miss some of the reports that are not readily available in GA4. One of the reasons for this is that you need to set events up to start seeing them in the reporting. Additionally, most events are custom, so GA4 doesn’t provide out-of-the-box reporting for them. The reports available are also harder to customize and offer fewer options, which can be another source of frustration.
eCommerce tracking
Simply put, eCommerce tracking in GA4 is underwhelming. It includes simple reports and doesn’t support enhanced eCommerce. It offers neat graphs as the Items Viewed and Items added to the cart by Item Name, but it is not as complete as Universal Analytics’ enhanced eCommerce. You will miss insightful reports like the Shopping Behaviour, the Checkout Behaviour, and the Product List Performance. We expect Google to update them at some point, but at the moment, they are not as good as they are in Universal Analytics.
Attribution modeling
GA4 is still not very mature. If you are used to the model comparison and multi-channel funnels, you will now be missing them for the time being.
Product-level custom dimensions
They are still not supported in GA4. You can track them, but they won’t appear correctly in the interface. There are ways around it, like re-purposing product dimensions, but it is not an ideal solution. GA4 doesn’t have the equivalent of a session-level custom dimension, but this one is relatively easy to work around using session variables in the data layer or the application. Hopefully, they will add this feature soon.
Many people in the analytics industry consider GA4 to be still in beta or unfinished. There are a few reports, and some features, like product-level properties, are still not supported (although you can track them). We’ve seen Google doing it in the past: they release new products that are not quite there and improve them with quick iterations. After some time, they end up with an excellent product, but early adopters have found all sorts of setbacks along the way. A great example of this process is Google Tag Manager.
In theory, you can replace the Universal Analytics reports using a combination of Explorations and BigQuery raw data. Explorations allow you to create custom, reusable reports combining the dimensions and measures you want. For complex use cases, you can use the raw data in BigQuery to do all sorts of in-depth analyses.
The reality is that this is not what many Google Analytics users are used to. Most learned to navigate the interface, add filters, segments, and breakdowns or create custom reports. Explores fall a little short in agility, user-friendliness, and customizations. On the other hand, moving from using a user interface to view and customize reports to writing complex SQL queries it’s a considerable change; not everyone might be keen to do it, and it requires training and time to learn.
There are many other differences between GA4 and Universal Analytics that you can find in articles by doing a quick search. This is a limited list of the differences we find more critical regarding capabilities and usage. If you want to know more, we have linked to two helpful articles in Optimizesmart and WPBeginner.
Zooming in on Adobe Analytics
Initially known as Omniture, the brand changed hands in 2009 and its name in 2012, so today, it is a part of Adobe Cloud Suite. It is honed for large enterprises and doesn’t limit itself to web analytics only. To draw reports, Adobe Analytics involves third-party software, which allows it to map out an entire customer journey and identify who and why comes to your site, what pages and products they pay attention to, and what the traffic sources are.
The obtained insights enable analysts to assess the company’s marketing policies by understanding which methods work and which don’t. And this, in its turn, shows the value of each content piece. Similar to Google Analytics, Adobe also relies on machine learning to detect anomalies and calculate ROI, as well as create predictive reports that model possible outcomes and customer behavior.
This general overview of both tools doesn’t give enough ground for choosing a better one. We must dig deeper and consider their characteristics to determine the winner. So, let’s move on to the Adobe Analytics vs. GA4 comparison.
Google Analytics vs Adobe Analytics: The Feature Comparison
Juxtaposing the two solutions is possible along the following lines.
Integrations
GA4 is natively integrated with Google Marketing tools (Ads, Ad Manager, and Search Console on the free version; DV360 and SA360 on 360), Ad Manager, Google Optimize and BigQuery. These integrations are powerful, seamless, and easy to set up.
The integration with Google’s marketing tools is handy for looking at behavior, conversions, and ROAS by Ad, Campaign, and other marketing dimensions. It also allows you to automatically optimize your ad spend in Google platforms based on conversions defined in GA4.
The integration with Optimize allows targeting audiences based on website behavior and creating experiences consistent with the campaign and ad a user saw.
Finally, there is the BigQuery integration, which we love. It exports raw daily and intra-day data to BigQuery by switching a button. The raw data isn’t super intuitive, but it is powerful once you get familiar with it. You can run granular, in-depth, and data science analyses, run machine learning algorithms, and combine the GA4 data with other sources with a level of granularity that supports all sorts of breakdowns.
On the other hand, Adobe Analytics has two types of integrations: with the other tools in the Adobe Experience Cloud and with third-party tools.
The integration with the rest of Adobe Experience Cloud is great because they enable a unified view of the users. You can use Target to personalize experiences to a segment of users defined in Adobe Analytics or Audience Manager (AAM); you can analyze the behavior of users in AAM segments in Adobe Analytics or share Adobe Analytics segments to create audiences in AAM for targeting. These integrations are getting even better with the Web SDK, which unifies all the tracking in a single library that enables server-side tracking.
Adobe also integrates with a broader range of third-party tools (over 200 integrations officially). Most integrations allow importing data to Adobe Analytics, where you can then use Analytics Workspace to crunch the numbers. Salesforce, Algolia, SiteSpect, Magento, and Bazaarvoice are just some examples of the integrations it offers.
So, in the integrations department, there is no real winner. Adobe Analytics is much more powerful when you want to seamlessly analyze and target users across digital analytics, onsite personalization, and marketing activities or when you want to import data from other third parties. On the other hand, GA4 integrations are excellent if you measure the ROAS of marketing activities run in Google’s platforms and optimize them based on conversions defined in GA4. The integration with BigQuery is also super helpful. It is an effortless way to feed your digital data to your Data Warehouse and combine it with other sources to gain deeper insights.
Reporting and analysis
GA4 reports are limited in number and flexibility. They give a nice high-level overview of what is going on, but their filtering and segmentation capabilities aren’t great. On the analysis side, it offers Explorations, but as we explained in the previous section, they fall short in flexibility, agility, and customizations.
Adobe Analytics is generally considered more advanced. If you have used Adobe Analytics Workspace, you know what we are talking about. Whenever we go back to using the GA4 interface after using Adobe for a while, we miss Analytics’ Workspace’s power, agility, and flexibility. You can create breakdowns, segments, and calculated measures on the fly, adding graphs, filters, segments, anomaly detection, comparisons, and advanced statistics.
One specific aspect of reporting that is worth mentioning is eCommerce. While it was good in Universal Analytics (although not as good as Adobe’s), it is underwhelming in GA4. There are no product-level properties you can use, the number of metrics is limited, and the out-of-the-box eCommerce reports are minimal. On that front, Adobe offers unparalleled flexibility and options, including merchandising variables (e.g., the context where products are added from), product classifications (think unlimited product dimensions), and combining product and non-product data (think product cart adds by page type, for example).
There are many other aspects where Adobe’s eCommerce reporting outclasses GA4’s. If you are interested in knowing more, Lukas Oldenburg wrote a great article in Medium with a lot of detail on the subject that is still very relevant.
Pricing
Many argue that the most crucial difference between Adobe Analytics vs. GA4 is that the latter is free. This isn’t entirely true. Some advanced features only exist in GA4 360 (the premium version of GA4), and the free version is subject to some volume limitations. In turn, Adobe Analytics costs around $500 a month to make use of.
If you use the 360 version, you might be paying more than you would for Adobe Analytics’ license. This will depend, to a great extent, on the volume of traffic to your digital properties, your tracking (the number of hits processed by the server), and what deal you have.
Suppose you want better data processing and accuracy, enterprise-level analytics support, SLA, higher data limits, enhanced data freshness, more audiences, or more event parameters (that you can convert to custom dimensions and measures. In that case, you will need GA4 360 and should compare the price of both licenses when calculating the costs. If you want to know more about the differences between GA4 and GA4 360, you can find a lot of detail in this article.
Support
In terms of support, GA4 has a massive community of developers and analysts using it and sharing their experience. You will most likely find the answer quickly if you encounter an issue. GA4 is also more accessible because it has a free version, so more people know how to implement and use it. For this reason, finding people to implement and manage GA4 on your properties is easier and cheaper than finding Adobe Analytics experts. There are also hundreds of affordable courses on websites like Udemy. So, even if you can’t find suitable candidates, you can always train your team without hassle.
In general, finding good professionals for Adobe Analytics can be more challenging. You can’t use Adobe Analytics without a license, and it is difficult to familiarize yourself with the tool without using it. Since licenses have a cost that individuals can’t afford, only people working for organizations using Adobe Analytics can access it. This is why a reduced number of professionals and data practitioners know the product well. It is harder to find them, and their salaries tend to be higher. There are training programs and materials that you can use to train your staff, but in general, it is less accessible, and it is harder to validate the knowledge against other people in the industry.
User-friendliness
To start using GA standard tracking, all you have to do is use Google Tag Manager for placing a simple JavaScript snippet on every website page. Additional Custom Metrics and Dimensions can be populated as required. As a result, an intuitive measurement plan becomes available to the personnel. Thus, employees don’t need any special technical or analytical skills to sift through data and build reports, which makes Google Analytics a perfect choice for startups and small companies with their chiefly non-tech staff.
In turn, Adobe Analytics has a high degree of customization, great flexibility of the Workspaces tool, and advanced opportunities. It minimizes chances for people with limited programming and analytical competency to master it quickly. Moreover, Adobe’s 50 sProps and eVars per page and a plethora of various metrics require specific expertise to handle, which means additional implementation and maintenance investments that only big enterprises can afford.
Existing tech
Another thing to consider when choosing between Adobe Analytics vs. GA4 is your tech stack. Implementing and maintaining Adobe Analytics can be relatively easy if you are already using other Adobe Products, like Adobe Experience Manager or Adobe Commerce. There is another scenario if you are already using other related technologies. For example, if you already use Adobe Target or Audience Manager, you can benefit from using Adobe Analytics.
Adobe upholds the same EU-mandated initiatives and also provides request handling by Adobe Trust & Safety Team, offers a DSGVO-compliant data processing agreement, and follows the principles of transparency, storage and purpose limitation, accountability, data minimization, confidentiality, and integrity that enable it to retrain a high-standard of privacy.
Privacy
There are some concerns about how Google uses some of the data gathered using GA4. We have read in several places that it might not be GDPR-compliant. Some publishers worry that Google might build audiences based on tracking their websites, effectively giving their audiences access to other publishers and content creators. Some companies managing susceptible data resist adopting GA4 because they don’t trust how Google uses that data. A legal team should assess this aspect, but in general, Adobe is considered a better alternative in the data privacy department.
Website traffic
In this aspect, both tools are on equal footing. They allow you to analyze the traffic your website gets and discover channels and the volume of this traffic. You can employ them to scrutinize your advertising campaigns in terms of both organic and paid traffic, keep track of bounce rates, and follow other KPIs.
Attribution modeling
The attribution model is defined as a set of rules that allocates and assesses each touch point along the way, the final destination of which is the sale. For example, a person sees your product on social media, then visits your website, and finally makes a purchase via a mobile app. Detecting and tracking all these marketing channels is the task of attribution modeling.
Google Analytics has more extensive capabilities and multichannel dynamic funnels in this sphere, which are even more powerful in GA’s 360 version. Adobe Analytics attribution modeling is rather primitive, focusing only on the identification of First Touch and Last Touch points.
Conversion tracking
This procedure analyzes what actions visitors perform on your site or mobile app. In Google Analytics, the number of events classified as conversions is limited to four plus 30 more that can be set up for conversion tracking. Adobe Analytics has broader opportunities here since the tool can keep track of hundreds of events simultaneously.
Data sampling
Sampling is a procedure of selecting a subset of data to analyze and serve as a symptomatic example of the trends characteristic of the entire data set. Being a viable approach, it may lead to sampling error, though. That is why analytics software is cautious about applying it.
Adobe doesn’t have such a concept at all, running a report query against the whole available dataset and returning a result in a split second via Analysis Workspace. GA displays more variation in sampling treatment. Its default reports involve no sampling, much like Adobe Analytics does. Ad-hoc queries, which comprise events, custom variables, dimensions, and metrics, are subject to several sampling thresholds, depending on the version of the tool. In GA 4, it equals 100M sessions at the view level, whereas, in GA Standard, it is 500M at the property level.
Personal data capturing
To administer targeted campaigns, it is vital for brands to know as much as possible about their clientele. The two tools provide businesses with different types of customer data. Adobe Analytics captures their email addresses, while GA doesn’t give that but furnishes gender, age, and other demographics of site visitors.
Data visualization
In this aspect, both solutions are extremely rich in the features they offer. Users of Adobe Analytics can make use of graphs of several types (bar, bullet, line), various charts (flow, fallout, donut), cohort and freeform tables, treemaps, and more. GA 4 adds to this impressive list also user and path explorations, segment overlaps, scatter plots, user lifetimes, etc.
Data retention
Adobe Analytics stores data for 25 months by default. If you want to prolong this period for up to 10 years and one month, you have to pay an additional sum of money. The default data retention period in Google Analytics is only two months, with an extension option of up to 14 months. The two tools also differ in cookie storage time – 15 years for Adobe users and just one month for GA fans.
Final Thoughts
As you see, each tool has its own fortes and shortcomings, so opting for the best one is a tough call. Typically, GA 4 is the choice of small and medium companies whose non-tech personnel can make the most of intuitive reports and rely on Salesforce Sales Cloud in building loop sales capabilities. Adobe Analytics is just what the doctor ordered for large enterprises that are interested in high-profile analysis capabilities and would like to enjoy broad customization opportunities. It perfectly suits the companies that are already using other services provided by Adobe Experience Cloud.
However, each use case should be treated individually. To make sure you make no mistake in selecting the appropriate web analytics services, address seasoned mavens in this field. Experts of DataCrafts have the necessary qualification and skills to help you choose and implement the best analytics tool that will facilitate the business growth of your organization and enhance its efficiency immensely. Book a free 30-minute consultation to rev up your data analytics practices.