Schema and Standard Reports
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- 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).
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- 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.
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Data Organization
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- 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.
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- 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
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Custom Metrics
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- Advanced support for different types custom metrics (counters, numeric, revenue) to capture the success events of digital properties.
- 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.
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- 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.
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Custom Dimensions
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- 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.
- 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.
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- 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.
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eCommerce-related Dimensions/Metrics |
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- 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.
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- 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.
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High-cardinality Dimensions
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- Dimensions with a high number of unique values can show a special item labeled "(Low-Traffic)" that groups long-tail dimension values.
- 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.
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- Google Analytics are also not immune to limits associated with high cardinality. Items beyond the given limit are grouped in a bucket called "other".
- Custom tables and queries of raw data in BigQuery are two possible ways to bypass issues with cardinality limits.
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Data Sampling |
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- Adobe Analytics does not perform any sampling and processes all available data.
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Campaign Tracking |
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- 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.
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- 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.
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Marketing Channels |
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- 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.
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- 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.
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Dimension Enrichment |
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- 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).
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- 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.
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Localization |
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- Each report suite can be configured with its own time zone, currency, and custom calendar setting.
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- Time zone and currency support down to the view level.
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Visitor Identifiers |
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- 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).
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Filtering |
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- 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.
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- Google Analytics uses a similar process to exclude bot traffic from reports.
- Custom filters based on IP and other fields can be also built.
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Data (Pre)Processing |
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- 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
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- 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.
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Geo Dimensions |
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- Through IP lookups, Adobe generates automatically Geo dimensions for Country/State/City/DMA/MSA.
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- Google Analytics uses the same method to generate predefined Geo dimensions.
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IP Obfuscation |
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- These settings have consequences on filters relying on IPs well as the the accuracy of Geo-based dimensions.
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Data Latency |
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- 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.
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- 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.
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Data Retention |
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- The data retention terms depend on the type of GA property as well as on the type of data.
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Bulk Management of Reporting Settings |
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- 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.
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- Google Analytics provides a management API that comes with daily utilization limits.
- Bulk management through the UI is not available.
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