Google Analytics vs. Adobe Analytics - Data Processing & Configuration

Schema and Standard Reports

• Adobe Analytics organizes data using a standardized data schema with a hierarchy of Visitor > Visit > Hits.

• Hits can be subdivided into two main categories: Page Views and Non-Page Views (Custom Links, Downloads, and Exit links).

• Data is organized using two major blocks: metrics and dimensions. A variety of built-in dimensions/metrics are available out of the box.

• The Adobe schema allows for data to be cross-tabulated across metrics and dimensions from different schema levels. (e.g. it is possible to freely combine a hit-based dimension with a visitor or a visit-level metric and vice versa a visitor-level dimension with a hit or a session-based metric). 

• Classic Google Analytics organizes data in a similar hierarchy: User > Session > Hit.  

• Hits can be also divided in two categories: Page Views and Events, with a special consideration for events that can be further classified into interaction vs. non-interaction events. The same non-interaction flag can be also applied to Page Views

• The Classic Google Analytics schema has limitations in allowing combinations of metrics and dimensions from different scopes/levels. 

• Built-in GA reports guard against such combinations by disallowing them, while custom reports allow them but require advanced understanding of the scope of different dimensions/metrics for proper data interpretation.

Data Organization

• Adobe uses the concept of report suites to group/organize the data collected from different website(s)/app(s).  

• Virtual report suites allow segments to be applied to the data inside a standard report suite; coupled with granular user access permissions this option makes it easy to curate views of the data that are relevant to a particular business need/unit.

• Virtual report suites are a good alternative to the concept of "multi-suite" tagging providing a good way to organize data from different digital properties into a single standard report suite entity while maintaining the capability to curate different views of the data. 

• Adobe offers a "roll-up" report suite type which can be used to aggregate high-level data from other report suites. Roll-up report suites do not deduplicate visitor/sessions from different report suites, nor is there a way to segment/break out data contained in different custom dimensions. 

• Google Analytics organizes data using properties.  

• Within properties different views can be configured with options for filtering data, applying different reporting configurations, and enabling different access permissions. 

• GA360 accounts have a special category of roll-up properties that allow data from multiple properties to be integrated together while maintaining sessionization/visitorization and  allowing mapping of dimensions/metrics from related properties.

• Data from mobile apps can be sent to the standard "Web" family of properties,  to specialized "App" properties, or to the new style of "Web + App" properties.

• Web + App properties allow different feeds to be configured representing ways to sub-categorize data 

Custom Metrics

• Advanced support for different types custom metrics (counters, numeric, revenue) to capture the success events of digital properties. 

• Built-in capabilities to configure advanced deduplication logic using event serialization. 

• Coupled with advanced segmentation capabilities, metrics can be configured to deduplicate activity across custom visit and visitor scopes. Built-in configurability for event participation settings allowing more advanced analyses. 

• Google Analytics offers the creation of custom metrics with two scopes "Hit" and "Product". 

• While not part of the "Custom Metrics" setup, through the creation of "Goals", Google Analytics makes it possible to capture sessions where a particular success event was accomplished. 

• Goals are natively available for inspection in most standard GA reports, while custom metrics are only available within custom reports. 

Custom Dimensions

• Robust support for different types of dimensions (conversion and traffic variables) allowing to assign meta data at different levels in the schema to meet even the most custom requirements. 

• While ongoing developments in Analysis Workspace have diminished the value of Traffic Variables (props), Conversion Variables (eVars) offer advanced configurability for setting expiration and allocation capabilities.

• Dimensions with multiple values are supported through the use of the reserved s.products variable as well as through list vars. Usually only three list vars are available per report suite.

• List vars do not support full correlation of multiple values in a dimension with multiple values in a corresponding metric. 

• Google Analytics offers a single class of dimensions that can be configured with four different scopes (Hit, Session, User, Product).  

• Google Analytics dimensions capture the last (not most recent) value for Session and User-level dimensions. However, the last allocation is changed to first if the dimension is set via advanced filters.

• Custom Dimensions are available as secondary dimensions in most built-in reports and also available in custom reports. 

• Reserved product-scoped dimensions allow multiple values to be passed into a particular dimension on the same tracking HTTP request. 

• Extensive support with out-of-the-box dimensions and metrics to capture key eCommerce interactions. 

• Specialized dimension settings allowing expiration of credit allocation at the time of a custom eCommerce event (i.e. Add to Cart or Order).

• Merchandising eVars enable advanced analysis techniques, especially in the context of eCommerce-related attribution. The most common example is with attribution of purchases to a particular placement where a product was added to the shopping cart. Multiple blog posts by Adam Greco describe the process and logic behind the use of merchandising vars. 

• Through the introduction of Enhanced eCommerce Google Analytics has created a robust framework for tracking eCommerce interactions similar to capability in what Adobe Analytics offers. The framework comes with a fully instrumented demo site.

• One interesting aspect is that eCommerce tracking has its own scope sitting outside of metrics/dimensions with a scope of Hit and Session.

High-cardinality Dimensions

• Dimensions with a high number of unique values can show a special item labeled "(Low-Traffic)" that groups long-tail dimension values.

• This limitation usually begins to occur after a dimension has reached 500,000 unique items during a given month, but the different limits can be reconfigured. 

• High cardinality tables can also sometimes lead to hash collisions that can make the interpretation of data quite challenging.

• Adobe Data Warehouse and Data Feeds are not affected by cardinality limitations. 

• Google Analytics are also not immune to limits associated with high cardinality. Items beyond the given limit are grouped in a bucket called "other".

• Free and GA360 accounts have different cardinality limits that depend on the type of table that is queried and the selected date range, among other factors.

• Custom tables and queries of raw data in BigQuery are two possible ways to bypass issues with cardinality limits. 

• Adobe Analytics does not perform any sampling and processes all available data. 

• Google Analytics has different sampling thresholds for Analytics Standards and Analytics 360 accounts.

• Adobe offers a built-in dimension slot for tracking campaigns. The slot can be configured (like any other eVar) to fully customize its expiration and allocation settings. 

• Adobe's standard relies on a single campaign query parameter. There is built-in support for "cid", but through javascript modifications or processing rules any parameter(s) can be used.

• The parameter value can be enriched to include other campaign meta data through the use of classification techniques.

• Google Analytics's campaign tracking standard involves the use of five campaign parameters (utm_source, utm_medium, utm_campaign, utm_term, utm_content) and five built-in dimensions that use a "most recent" allocation logic.

• If other parameters are used they can be remapped to the built-in reports/dimensions through the use of advanced view filters. 

• If a session has more than one campaign, Google Analytics will increment a Session count for each campaign instance. 

• Using a waterfall rule-set, Adobe can organize different traffic sources into up to 25 marketing channels.

• Information about specific campaigns/drivers within a marketing channels can be extracted through configuring a separate dimension called "Marketing Channel Detail".

• The built-in configuration generates two dimensions "Last Touch Marketing Channel" (more accurately described as "Most Recent") and a "First Touch Marketing Channel".

• Using the specialized Attribution panel type in Analysis Workspace the default Last and First methods can be changed on the fly to include a variety of other attribution models (Linear, U-shaped, J Curve, Time Decay, etc)

• Advanced settings include an "override" toggle which can determine if a given channel can override other channels, as well as an expiration window. 

• Google Analytics has a similar implementation with Channel Groupings. 

• Multiple Channel Groupings can be configured to build different channel models.

• The Default Channel Grouping integrates most easily with built-in reports. 

• Channel Grouping configurations are specific to each GA view making it highly configurable, but also difficult to deploy and manage across an enterprise setting with multiple properties/views. 

• The primary method for enriching dimension data is through two classification techniques: lookup-based classification (file imports) and rule-based classification (rules that can extract and match patterns/RegEx and return hardcoded values).

• Another method for data enrichment is the use of DB Vista rules.

• Dimension enrichment can be achieved through the Data Imports feature. 

• Using GA Advanced Filters - users can also apply rule-based classification similar to the functionality Adobe offers.

• Unlike Adobe, data fields added through the lookup require their own dimensions.

• Each report suite can be configured with its own time zone, currency, and custom calendar setting.

• Time zone and currency support down to the view level.

• Visitor identification (visitorization) relies on the cookies set through the standard JavaScript tracking libraries. 

• Google Analytics's primary visitorization method also relies on the cookies set via JavaScript. 

• It is also possible to modify the visitorization methodology through specialized views and a custom identifier (user id).

• Bot definitions maintained by the IAB can be enabled on the level of the report suites to filter out known bots.

• Traffic identified as coming from bots does not count towards various metrics/dimensions but is available for inspection in two built-in reports - Bots and Bot Pages.

• IP and User Agent based filters can be custom-built to filter additional traffic. 

• Google Analytics uses a similar process to exclude bot traffic from reports. 

• Custom filters based on IP and other fields can be also built. 

• Processing Rules allow a good deal of flexibility for processing data after it has been transmitted by the tag and before it enters the reports.

• VISTA Rules also allow data preprocessing but come with an additional cost and usually require custom engagements

• Google Analytics can use advanced filtering rules which can be used to execute data cleanup and meta data reassignment similar to the functions performed by Processing Rules in Adobe.

• Through IP lookups, Adobe generates automatically Geo dimensions for Country/State/City/DMA/MSA. 

• Google Analytics uses the same method to generate predefined Geo dimensions. 

• Adobe offers two separate options for masking the visitor's IP (last octet replacement with 0 and IP obfuscation).  

• These settings have consequences on filters relying on IPs well as the the accuracy of Geo-based dimensions.

• Google Analytics provides a special flag "anonymizeip=true" that can be set to remove the last octet of the visitor's IP from the data written to GA.

• Real time data is available within a few seconds. Different dimensions/metrics can be configured for real time data review. eVars are generally not suitable for real time exploration.

• Current data is usually available within 30 minutes after the data has been generated. 

• Fully processed data is usually available an hour or so after the data has been generated. 

• Real time data is available within a few seconds of generation. Real time data is limited to a selection of built-in dimensions/metrics.

• Fully processed data is usually available within 30 minutes after the data has been generated.

• The default data retention period is 25 months but Adobe also offers purchases of extensions.

• The data retention terms depend on the type of GA property as well as on the type of data.

• Settings can range from 2 months to no expiration date for some types of data.

• Adobe provides ways to bulk-manage the configuration of reporting settings across multiple report suites directly in the UI.

• Most reporting settings can be copied from existing report suites to new report suites.

• Selecting the metrics/dimensions across multiple report suites also provides a handy diff of the configurations across different report suites.  

• Google Analytics provides a management API that comes with daily utilization limits.  

• Bulk management through the UI is not available.

• Data organization/processing considerations for Google Analytics App + Web properties are somewhat different from Classic Google Analytics with a focus on an open-ended parameter name/value pair schema. The beta version of the product offers a limited number of parameters that are available directly in the App+ Web UI, but promises a more streamlined and modern approach to data organization. 

• On Adobe's side, legacy products such as Adobe Data Workbench (from the Visual Sciences acquisition) have been able to offer a fully customizable schema for clickstream data and joining of other data sources for over a decade. Adobe's latest developments in this direction include the introduction of the Alloy.js tracking library and the customer journey analytics offering.

  About QA2L

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.

We take a smart, targeted approach to issue discovery via auto-selective site audits and scans and can help your organization on the path of improved data governance and data quality.

Contact Us