Google Analytics vs. Adobe Analytics – a benchmark starting point and guide for people who switch

Can you compare the two oldest and well established Analytics suites? Yes, I think you can compare them.

Can you compare the two oldest and well established Analytics suites? Yes, I think you can compare them.

But can you tell me which one is better? Of course, not, both are good solutions and have specific benefits. So for one business Google Analytics is a great choice, and for another one, it is Adobe Analytics.

So what is this about?

I try to show different features side by side, so users can get an idea of how things work in Google Analytics and how in Adobe Analytics. This analysis can be useful when switching from one solution to another or evaluating both solutions.

Photo by Mert Kahveci on Unsplash


Do I cover each feature of the tools? Of course, not. Do I know everything about each tool? Most likely not.

I know Google Analytics pretty deeply and worked 80% of my time with it. Adobe Analytics I know, ok. And a lot of stuff I have checked in the documentation because it was some time ago I used it on a very operational level.

It’s not a full-fledged benchmark setup where you compare it deeply side-by-side. It is an orientation and a starting point.

The history and how both are influencing each other

Google Analytics and Adobe Analytics are pretty old (in digital tool terms). And both are based on a different tool that Google and Adobe have acquired. Google acquired Urchin and Adobe Omniture (with Sitecatalyst).

what is urchin analytics

Omniture Sitecastanalyst

Omniture Analytics was the defacto standard and premium solution in the 2000er. Most of the business I knew serious about Analytics was using Omniture (and some Webtrends). Since these tools already had pretty high pricing, they were used primarily by bigger enterprises.

When Google acquired Urchin is was far away from the state, it is today. It was a basic analytics tool. But Google introduce an essential new feature: They made it for free.

This was revolutionary since there were no free analytics tools available. All other tools charged based on data volume and could get pretty expensive. At this time, analytics was pretty new, and most of the companies didn’t know it. So it was hard to convince them to pay for analytics. That’s why Google Analytics was a real game-changer since it introduced analytics for every business.

Google invested heavily in GA, and until the 2010er, it surpassed Omniture, at least when it came to user experience. But it also aligned with most of the features and still was free. Google also introduced a paid tier of Google Analytics called Premium. It was mostly for high volume accounts and the few people that would like to have raw data access.

When Adobe acquired Omniture, it invested heavily in UX over time, and it took plenty of good practices from other analytics tools like Google Analytics, Mixpanel, or others.

Google Analytics vs. Adobe Analytics – User Experience

Today both tools are well designed from a UX standpoint. Are they perfect, of course not, but both tools manage it well to be accessible for beginners and powerful enough for pro users. A lot of things are similar in both tools. For some functions, both tools choose different ways to implement them. Adobe provides more filters and settings on the reporting layer. Google instead makes some decisions for you to make working with the tool easier.

Google Analytics vs. Adobe Analytics – Pageviews

Both tools are from the old area where a page view was technically the standard all websites had. So tracking page views is a central report item in both suites. This changes with the new “Web & App” property that Google has introduced with an event-centric approach.

Google Analytics Pageview – Analytics Help Page Views  |  Analytics for Web (analytics.js)  |  Google Developers

Adobe Analytics Page views Compare page views, instances, and occurrences

Google Analytics vs. Adobe Analytics – Events

Tools like Kissmetrics or Mixpanel made event tracking accessible in the 2000er. Google was missing that for some time. And Google still has a very special event implementation by using three-dimension: category, action, label. Do they make sense? Not really, but we learned to work with them. In Adobe an event has simply an event name. That’s it. Additionally, some standard events are used for e-commerce reporting.

In the new “Web & App” property Google changed the event approach to the standard that has been established by other tools. An event name and event properties for context. Finally!

Google Analytics: Set up event measurement – Analytics Help

Adobe Analytics:

Google Analytics vs. Adobe Analytics – Custom Metrics

In free GA properties, you can create up to 20 custom metrics and 360 properties up to 200. So there is plenty of space. Usually, you will not use that many custom metrics. But they are handy for some use cases. In GA, custom metrics can be simple integer ones (counting up) or time ones (counting up seconds).

You can also create calculated metrics that are based on formulas, including all available metrics.

In Adobe Analytics, you can setup calculated metrics in the Workbench area. From my experience, there is no way to define custom metrics in Adobe Analytics. The recommendation is to solve that by using events and event values (which does the same as the integer custom metric in GA). You could also use eVars, to sum up, values.

Google Analytics: Custom dimensions & metrics – Analytics Help

Adobe Analytics:

Google Analytics vs. Adobe Analytics – Custom Dimensions

Like the custom metrics, the same limits are set for custom dimensions (free: 20, 360: 200). You will use custom dimensions a lot more because they add useful context to pages, events, users, or products.

Examples: Newsletter Subscriber, CRM data (scores), Weather, A/B test variants, Login status,…

In GA, you can define the scope of the dimension. It can be on hit, session, user, or product level. This selection sets which metrics will work with the dimension and how long the value persists.

Dimensions in Adobe Analytics work differently. You can set up two types of them: Props or eVars.

Props are similar to the Google Analytics dimension with a hit scope. They save values for a page view or event scope.

eVars are similar to Google Analytics dimensions with a session, user, or product scope. But you can be more specific to define how long the value should persist (a session, a hit, a day, an hour). You can also specify the allocation/attribution of the values (use the first one, the last one,..).

Google Analytics: Create and edit custom dimensions and metrics – Analytics Help Google Analytics Dimensions and Metrics explained in great detail

Adobe Analytics:

Google Analytics vs. Adobe Analytics – Custom reports

Custom reports are my favorite tool in Google Analytics. I use them most of the time because I can create the data views I need for my different use cases.

I can combine dimensions and metrics in flat reports or drill-downs (or in 360 as funnels).

In Adobe Analytics, this is an additional feature called Workspace. Here I can create deep dives or simple reports. Compared to Google Analytics, you have more possibilities than in the custom reports.

The Workspace is more comparable with the new advanced analytics feature in 360 (or the new web & app property) or the Explorer Mode in Data Studio.

Google Analytics: Create and manage Custom Reports – Analytics Help

Adobe Analytics: Introduction to Analysis Workspace in Adobe Analytics | Adobe Learn & Support tutorials

Google Analytics vs. Adobe Analytics – Segments

Segments are the analytics power tool. A solution without a proper segment solution is not worth implementing. Why is that? To figure out issues or optimization potentials, you usually drill down from general data to more segmented data. You want to find a segment that over- or underperforms the average.

In Google Analytics, segments can be created by any dimension or a metric combination. And you can also create sequential segments (a session where users have been on a page a before page b), which is helpful for particular deep dives.

In Adobe Analytics, it is pretty similar to Google Analytics. Even the segment builders are pretty comparable (I like the Adobe Analytics one a bit more). You can also create sequential segments in Adobe Analytics.

Google Analytics: About segments – Analytics Help

Adobe Analytics: Analytics segmentation

Google Analytics vs. Adobe Analytics – GDPR

Both services can be configured to be GDPR compliant (reduce IP, offer opt-out). But both services transfer the data to US servers, which might become more difficult in the future for EU businesses.

Google Analytics: Google Analytics & GDPR | Compliance Checklist | Cookiebot

Adobe Analytics: Data compliance for GDPR | Adobe Analytics

Google Analytics vs. Adobe Analytics – Unsampled data

In free Google Analytics accounts, sampling becomes an issue after you reach a specific data volume (it happens faster than you think). Sampling means that Google Analytics does not calculate your metrics by counting all raw events but using a statistical model to make an assumption based on X% of your raw data. So numbers can be slightly based on the real ones.

In 360 properties, the likelihood to have reported with sampled data is pretty low. So it’s usually no issue here.

In Adobe Analytics, I couldn’t find any information if Adobe Analytics is sampling. I guess not.

Google Analytics: About data sampling – Analytics Help

Google Analytics vs. Adobe Analytics – Data recency

Fresh data – who doesn’t like it? I can remember the days when Google Analytics data took up to 2 days until it arrived.

In 360 properties, the data usually available in 10-60m. Edge cases can cause a delay of four hours. If you go crazy with your hit volume, you can end up with longer delays (>1 billion).

In Adobe Analytics, the data should be available within 2 hours.

Google Analytics: Enhanced data freshness – Analytics Help

Adobe Analytics: Latency

Google Analytics vs. Adobe Analytics – data volume

In Google Analytics 360 properties, there is no technical limit (well, there is undoubtedly one, but you might never hit it). The volume is defined in your contract. So if you need more, you need to pay more.

Same in Adobe Analytics.

Google Analytics:

Adobe Analytics:

Google Analytics vs. Adobe Analytics – Ensure Data quality

Well, one of my most pressing topics. If you have data quality issues, you can simply skip everything you want to do with the data.

Google Analytics and Adobe Analytics are no poster-child for ensuring data quality (this would be Snowplow). But some ways help.

In Google Analytics, you can improve the quality with filters you set on view level (modify all events, campaign data to lower case, exclude sources or IPs). You can also define which URL params should be removed from the reporting. This setting is helpful if your website heavily uses them, and you would like to have not a single page for each session id. You can also activate a bot filter.

In Adobe Analytics, you can exclude bot traffic and specific IP addresses. With processing rules, you can define complex mappings and transformations. But handle them with care.

Google Analytics: About view filters – Analytics Help

Adobe Analytics:

Google Analytics vs. Adobe Analytics – Raw data

Raw data was a nerd feature some time ago but becomes the essential feature now and in the future. Today tracking data is just a part of your data setup, and you want to combine, enrich it with other data. For that, you need raw data.

For Google Analytics 360 (and the new Web & App) properties, you can get your raw data into BigQuery. The setup is straightforward, and it just works.

For Adobe Analytics, you can create data feeds. The data then is loaded on an FTP-server where you can get it from. You can define for the feeds what dimensions and metrics should be included. But you have to take care that the data ends up in your data warehouse.

Google Analytics: BigQuery Export for Analytics – Analytics Help

Adobe Analytics: Data feed overview

Google Analytics vs. Adobe Analytics – rollup properties

Rollup properties are not tracking data themselves, but they combine data from different properties. These properties are beneficial, when you, for example, have a multi-country setup and one property for each country. So in a rollup property, you can aggregate all country data for further analysis.

Google Analytics 360 and Adobe Analytics support rollup properties.

Google Analytics: About Roll-Up Reporting – Analytics Help

Adobe Analytics: Rollup and global report suites

Google Analytics vs. Adobe Analytics – eCommerce tracking

Sure, when you have an eCommerce business, you have specific analytics cases you want to analyze.

Google Analytics is offering the enhanced eCommerce feature (free and 360), which needs an extended implementation. But the reporting you get is still the best out-of-the-box eCommerce reporting you can get.


In Adobe Analytics, you also have an eCommerce reporting, but the setup and the reporting are not comparable with the Google Analytics one.

Google Analytics: About Enhanced Ecommerce – Analytics Help

Adobe Analytics:

Google Analytics vs. Adobe Analytics – Real-time reporting

Is real-time reporting critical? No. Is it helpful to have? Definitely.

The real-time report in Google Analytics always looked awkward because it seems it’s from a different time (also design-wise). You have access to events, pages, and sources in Real-Time. This report helps you see what’s up on your website and debug things in test properties.

In the new web & app property, Google Analytics gets the real-time reporting that Firebase has. This report includes more details and a very much more beautiful view.

The Adobe Analytics real-time report is pretty similar to the Google Analytics one.

Google Analytics:

Adobe Analytics: Real-time

Google Analytics vs. Adobe Analytics – User ID based tracking

In Google Analytics, this was missing for some time. It’s possible to track a user id, but the implementation is still a bit complex (you need a separate property for this feature), but it works.

With the new web & app property, the setup should become more logical and straightforward.

In Adobe Analytics, you can set up a visitorID, also across devices.

Google Analytics: About the User-ID feature – Analytics Help

Adobe Analytics: visitorID

Google Analytics vs. Adobe Analytics – User Explorer

It’s a bit of an exotic report in Google Analytics. In the explorer, you can see all sessions and events for a single user. Sure, you won’t see general trends here, but it is super helpful to understand how users behave in detail. I used it a lot for product research and for debugging weird tracking issues.

I don’t know such a report in Adobe Analytics (please correct me if I am wrong).

Google Analytics: User Explorer – Analytics Help

Google Analytics vs. Adobe Analytics – Marketing channels

For marketing teams, channel grouping is one of the essential features. It adds another layer on top of referral and campaign information by grouping different sources into one channel.

The grouping happens in Google Analytics by setting up a decision tree. You add rules when a source should be added to a channel, and Google Analytics goes through it one by one and assigns the traffic source to a channel.

In Adobe Analytics, this works pretty much similar.

Google Analytics: About Channel Groupings – Analytics Help

Adobe Analytics: Analyze Marketing Channels

Google Analytics vs. Adobe Analytics – Attribution

Well, attribution. Of course, this topic can span across multiple posts. So let’s keep it short here.

In Google Analytics, you have a standard last-non-direct attribution. But the attribution reports let you analyze the performance based on different attribution models (static and dynamic ones).

Same in Adobe Analytics. But as Google, they have a separate service called Attribution IQ that lets you analyze your data with different models.

Google Analytics: Overview of Attribution modeling in MCF – Analytics Help

Adobe Analytics: Adobe Analytics Attribution IQ – The Digital Marketing Architect

Google Analytics vs. Adobe Analytics – Import external data

In a lot of use cases, some additional context data could enrich your analysis. But you don’t have everything in your tracking.

In Google Analytics and Adobe Analytics, you can import external data and extend dimensional data.

Another use case is offline conversions. In some business models, the final conversions happen outside of the tracking (e.g., in a CRM). These conversions can be added later using bulk imports (or the measurement protocol in Google Analytics).

Google Analytics: About Data Import – Analytics Help

Adobe Analytics: Data Sources overview

Google Analytics vs. Adobe Analytics – APIs

Google Analytics has extensive API coverage offering different APIs (Reporting, Management, or Real-Time). You can define metric and dimension combinations and get the data where you need it. There are many 3rd party services that use the API (e.g., Supermetrics).

The Adobe Analytics reporting API is covering the same kind of functionality. I am not sure about a management API. But this kind of API is more an edge case.

When it comes to 3rd party tools, Adobe Analytics is not covered so often (but Supermetrics has an integration too).

Google Analytics: Overview  |  Analytics Reporting API v4  |  Google Developers

Adobe Analytics: GitHub – AdobeDocs/analytics-2.0-apis: Documentation for the Adobe Analytics 2.0 APIs

Google Analytics vs. Adobe Analytics – Cross-Device Tracking

With Google Signals, there is a way to track users across multiple devices using a holistic approach (meaning it is not based on user ids tracked somewhere). When activated, you get additional data in your reports that enable you to do some cross-device analysis.

Surely with the new web & app property when you track website and app data into one property, you get a cross-device setup. But you need to make sure that you identify users based on login data. But you can also activate Signals for the new property type.

Adobe Analytics has a similar feature in development. The last time I checked, it was only available in the Pacific region.

Google Analytics: Activate Google signals – Analytics Help Activate Google signals for App + Web Properties – Analytics Help

Adobe Analytics:

Google Analytics vs. Adobe Analytics – Funnel Analysis

The funnel reporting in the free Google Analytics version was and is a feature no one would use. It’s complicated, and it has flaws. For 360 properties, Google Analytics introduced custom funnels a long time ago that was a suitable replacement.

The free accounts now get proper Funnel reports with the new web & app property. Finally.

In Adobe Analytics, there are standard funnel reports for Conversions, Product views, and Checkouts, and they work better than the free Google Analytics one. In the Workspace, you can also set up custom funnel reports.

Google Analytics: Custom Funnels – Analytics Help Funnel analysis – Analytics Help

Adobe Analytics: Funnel

Google Analytics vs. Adobe Analytics – Cohort Analysis

Google Analytics was pretty late to the cohort game. During that time, other tools like Mixpanel had put cohorts into the analytics focus. Now you can create cohort reports in Google Analytics. But they are still limited, so I can create buckets based on any criteria (which I can in other tools), and the visualizations are limited too.

Again, I know I repeat myself; this will change with the new web & app property. Here you get a lot more ways to create your cohorts.

In Adobe Analytics workspace, you can create cohorts, and you have enough ways to define your buckets and the time scale. So more comparable with the new direction in Google Analytics.

Google Analytics: The Cohort Analysis report – Analytics Help

Adobe Analytics: What is cohort analysis?

Google Analytics vs. Adobe Analytics – AI-based segments

Google Analytics has introduced AI-based segments in Firebase analytics first, and now they are also available in standard Google Analytics. It’s based on the conversions probability metric. Based on that, you can create segments of users with different probabilities.

In Adobe Analytics, there might be such a feature, but I am not aware of it. It makes sense to ask your account manager.

Google Analytics: Conversion Probability – Analytics Help

Google Analytics vs. Adobe Analytics – Anomaly detection

Google Analytics has a rudimentary anomaly detection build in the insight box you see on the home dashboard. It’s still pretty hidden, but I guess that Google might extend that in the future, since this kind of analysis is one of their core competencies.

In Adobe Analytics, you get anomaly detection in the Workspace area. It is built-in in every report you generate. When you have a table with revenue data, Adobe Analytics will show an icon when they have detected an anomaly for a specific value.

Google Analytics: Anomaly Detection – Analytics Help

Adobe Analytics:

Google Analytics vs. Adobe Analytics – The environment

We need to look beyond simply feature to get an idea of how it is to work with the different tools.

The suites

Both tools are part of bigger suites, and it would take us another long post to compare these.

The good thing is that you can use analytics data across other services without any further work.

The focus is slightly different, also based on the history of Google and Adobe.

Google Suite has a focus on performance marketing. With the Google marketing platform, you get what was Doubleclick before. And that includes a suite of performance marketing tools. The other tools are making it easier to extend the analytics data for different use cases. Optimize integrates neatly with Google Analytics. And data studio is a full-fledged data visualization tool, more comparable with Tableau. And then there is BigQuery and all data services in the Google Cloud.

Adobe itself also has a performance marketing tools, that integrate with the Adobe Analytics data. Additionally, they have the experience cloud that is an enhanced CMS. With that, you can build dynamic and personalized content more efficiently based on the analytics data combined with Adobe Target (the AB testing and personalization service).

Job candidates & consulting

You want to hire people for your analytics roles, and ideally, they have plenty of experience with your tool. Hiring for Google Analytics is significantly more comfortable since it is nearly standard for analytics. Usually, all people have at least worked at some projects with Google Analytics. Finding people who are experts in Adobe Analytics is much harder. You might find some who had worked a bit with Adobe Analytics, but pro experts are rare.

The same goes for consultants or agencies. There are plenty of people who can support you with Google Analytics setup. But there are few consultants or agencies that can do this for Adobe Analytics. The good thing about Adobe Analytics is that you can fall back to Adobe consultants. These are usually pretty good and a huge help (but expensive as well).

Documentation & Learning

You will find a whole ecosystem of videos, tutorials, blog posts, video courses, books for working with Google Analytics, and GTM.

For Adobe Analytics, this is much harder. There are some blogs dedicated to Adobe Analytics. But you won’t find much. In comparison, Adobe Analytics has extended internal documentation with tutorials and videos.

The verdict

Well, there is none. I hope the biggest take-away for you is, both are excellent analytics suites, and their features are mostly comparable. And that might stay the same for the future.

Google Analytics is doing right now a big step with the new web & app property. Adobe Analytics might follow.

Both tools have their tool environment they are integrated into deeply. So if you are bought into one, you will stick with the analytics solution for it.

And personally, it comes back to where you are most fluent. I worked most of my time in the Google Analytics setup, and I know the tool pretty good. So I am, of course, faster with Google Analytics. Switching to a different tool would mean to learn the edges to become that fast.

There is a different question in the future. Do you make full analytics suites sense in the future?

There are trends where the whole analytics stack is split up into multiple tool stacks (e.g., Snowplow, DBT, Looker). This is still a very pro setup, but it becomes more present in a different project. But this is something for a future post.

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