by Nikolay Gradinarov
The goal of this series of blog posts is to provide a fairly detailed overview of how Google Analytics (GA) and Adobe Analytics (AA) handle different aspects of digital analytics implementations. This comparison reference is organized in six different categories:
In the case of Google Analytics, I am primarily referring to the paid GA 360 version, but in many places I have tried to call out key differences between paid and free GA accounts. Wherever applicable, I've highlighted differences between legacy GA and App + Web properties.
I originally compiled this information as a self-reference guide to assist me in the cross-over from the world of Adobe Analytics to the world of Google Analytics. I wanted to understand how various analytics requirements I had seen implemented in Adobe Analytics can be translated to the implementation structure of Google Analytics.
There are many blog posts claiming that one or the other tool is hands-down superior. This blog series makes no such claims—instead, it can hopefully serve as a kind of lighthouse for other digital analytics practitioners who find themselves needing to navigate key differences.
A quick review of the top 50 online retailers in the US (ranked by net online sales in 2019) shows that the two products are equally common among retailers—of the 50 sites surveyed, 30 use Adobe Analytics, 27 have Google Analytics, 13 have both, and six have neither. The ratio of GA to AA would probably skew significantly in favor of GA if calculated across the entire population of Internet sites, with the free version of GA having practically a monopoly across smaller websites. However, I feel that the parity of GA to AA implementations in the enterprise ecommerce sector is more indicative of the feature parity that exists between the two products.
This parity also reinforces the point that any conclusions that one tool is flat out better than the other cannot be justified. In my opinion, each has distinct strengths and weaknesses.
In the case of GA 360, I like the consistency of their GTM-driven data collection, offering a standardized approach for populating the Data Layer array, the transparency of their pricing structure, and the built-in integrations for audience sharing and access to marketing activations across a swath of other offerings in the Google ecosystem. On the other hand, Adobe Analytics shines with the customizability of the architecture of reporting variables, the consistency of its schema, and the ongoing innovation of Analysis Workspace, which has made it a front-runner as a reporting platform with features such as drag-and-drop report organization, ad hoc data segmentation, advanced calculated metrics, etc.
The notion of superiority, in my opinion, is only valid when products are evaluated against a short list of specific requirements. In which case, hopefully this reference can be useful to practitioners who embark on such requirement-driven adoption deliberations.
Valid "As Of" ...
Both vendors are consistently innovating. In March 2020, Adobe unveiled plans for a new method of data collection (Alloy) and a new, open-schema, fully-customizable data analytics platform (Customer Journey Analytics). In September 2020, Google Analytics announced a beta version of a GTM container that handles server-side data collection and ongoing developments to Google Web + App properties.
This comparison was compiled in August/September 2020.
This guide was written with the help of many digital analytics professionals and after reviewing many resources (some of them listed in part six of this guide).
QA2L is a data governance platform specializing in the automated validation of tracking tags/pixels. We focus on making it easy to automate even the most complicated user journeys / flows and to QA all your KPIs in a robust set of tests that is a breeze to maintain.