AD0-E212 — Adobe Analytics Business Practitioner

Report Suites and Data Architecture

• A report suite is the fundamental data container in Adobe Analytics; all data collection is tied to a report suite ID (RSID)
• Virtual Report Suites (VRS) allow segmented views of a parent report suite without duplicating data
• Global report suites collect data from multiple sites/apps; rollup report suites aggregate data from multiple report suites
• Report suite settings include time zone, currency, traffic variables (props), and conversion variables (eVars)
• Data governance and GDPR compliance settings are configured at the report suite level in Admin console

Dimensions and Metrics

• Dimensions are attributes or descriptors of data (e.g., Page Name, eVar1, Browser); metrics are quantitative measures (e.g., Visits, Page Views, Revenue)
• Props (Traffic Variables) are session-scoped by default and support pathing; eVars (Conversion Variables) can be scoped to visit or visitor and support attribution
• Default metrics include Page Views, Visits, Unique Visitors, Bounces, Bounce Rate, Time Spent, and Revenue
• Calculated metrics are custom metrics built from existing metrics using a formula editor; they do not require additional data collection
• High-cardinality dimensions (many unique values) may trigger 'Low-Traffic' bucketing when unique value limits are exceeded in a reporting month

Analysis Workspace Fundamentals

• Analysis Workspace is the primary reporting interface; it uses a drag-and-drop canvas with panels, visualizations, and freeform tables
• Projects can be shared with users as 'Can Edit', 'Can Duplicate', or 'Can View' permission levels
• Panels include Freeform, Attribution, Segment Comparison, Media Concurrent Viewers, and Quick Insights
• Date comparisons can be made using the calendar picker, date range components, or by adding a comparison column in a freeform table
• Workspace supports breakdowns by dragging a dimension onto a row to create nested dimension analysis
• Annotations can be added to date ranges in Workspace to document known data anomalies or marketing events

Visualizations

• Key visualizations: Freeform Table, Line, Bar, Horizontal Bar, Scatter, Venn, Histogram, Cohort Table, Flow, Fallout, Map
• Flow visualization shows the paths users take through a site; supports dimensioned nodes and can display top paths in either direction
• Fallout visualization shows sequential step completion rates; touchpoints can be 'eventual' (any point in visit) or 'next hit' (immediately after)
• Cohort Table groups users by a shared characteristic (retention analysis) and tracks them over time intervals
• Histogram displays the distribution of a metric across visitors/visits, useful for understanding engagement depth
• Summary Number and Summary Change visualizations are used in dashboards to highlight single key metrics

Reports and Dashboards

• Scorecards (Mobile Scorecards) are simplified dashboards viewable in the Adobe Analytics mobile app for executives
• Scheduled reports can be delivered via email as PDF, CSV, or Excel on a recurring schedule
• Report Builder is an Excel add-in that allows users to pull Analytics data directly into Excel workbooks
• Data Warehouse provides access to raw, unsampled data exports for large datasets that exceed Workspace processing limits
• Workspace projects can be curated to show only specific components (dimensions, metrics, segments) to end users

eVars and Props Configuration

• eVars support allocation models: Most Recent (Last Touch), Original Value (First Touch), and Linear; allocation determines which value gets credit for a conversion
• eVar expiration can be set to: Hit, Visit, Time Period (custom days), or tied to a specific metric/event
• Props are hit-scoped (expire at end of hit) and can be enabled for pathing analysis (Next Page Flow, Previous Page Flow)
• List Props allow multiple delimited values per hit; List eVars similarly capture multiple values and distribute conversion credit
• Merchandising eVars allow product-level attribution by binding an eVar value to a specific product in the products string

Events and Success Metrics

• Custom events (events1–event1000) track specific user actions as Counter, Numeric, or Currency event types
• Counter events increment by 1 each time the event fires; Numeric events capture a specific numeric value; Currency events capture monetary values
• Participation metric allocates credit for a success event to all values in a visit that preceded the conversion
• Events can be serialized to prevent duplicate counting by passing an event ID that de-duplicates within 24 hours
• The products variable works in conjunction with events to capture product-level data including quantity and revenue

Segments

• Segment containers hierarchy: Visitor (broadest) > Visit > Hit (most granular); container choice determines which data is included
• Visitor-level segments include all hits from any visit where the condition was met; Hit-level segments only include individual hits matching the condition
• Sequential segments use the 'THEN' operator to define ordered steps; supports 'After' and 'Within' time constraints
• Segments can be applied at the report suite level, within a Workspace panel, or as a column in a freeform table
• The Segment Comparison panel identifies statistically significant differences between two segments across all dimensions and metrics
• Segments can be shared with the organization, specific groups, or kept private; only admins can publish segments to the Experience Cloud

Calculated Metrics

• Calculated metrics are built in the Calculated Metric Builder using operators, functions, and existing metrics/segments
• Metric types: Standard (aggregated totals), Filtered (applies a segment inline), and Participation
• Advanced functions include statistical functions like MEAN, MEDIAN, STDEV, PERCENTILE, and regression analysis functions
• Attribution models can be applied within a calculated metric to change how credit is distributed across touchpoints
• Calculated metrics do not require additional tagging; they are computed at report runtime from existing collected data
• Approximate Count Distinct function estimates unique dimension values and is useful for high-cardinality dimensions

Attribution Models

• Attribution IQ offers models: Last Touch, First Touch, Linear, Participation, Same Touch, U-Shaped, J-Curve, Inverse J, Time Decay, and Custom
• U-Shaped model gives 40% credit to first and last touch, distributing remaining 20% equally across middle touches
• Time Decay model gives exponentially more credit to touchpoints closer in time to the conversion event
• Attribution models can be applied to eVars in Admin settings, in freeform table column headers, or via calculated metrics
• The Attribution panel in Workspace allows side-by-side comparison of multiple attribution models on the same metric
• Lookback windows for attribution can be set to Visit, Visitor, or Custom (up to 90 days) in Analysis Workspace

Date Ranges and Calendar

• Rolling date ranges (e.g., Last 30 days, Last 7 days) automatically update relative to the current date
• Custom date ranges can be saved as components and shared across the organization
• Workspace supports comparing current period vs. prior period, prior year, or a custom date range in the same table
• The Analytics fiscal calendar can be configured in report suite settings to support non-standard week/month definitions (4-4-5, etc.)
• Date comparisons show absolute change and percentage change columns when enabled in freeform table settings

Advanced Segmentation Techniques

• Exclude containers within segments allow you to define audiences by what they did NOT do (e.g., visitors who did not purchase)
• Nesting containers enables complex logic: e.g., Visit container nested inside a Visitor container to find visitors who had a specific visit pattern
• The 'exists' and 'does not exist' operators check for the presence or absence of a dimension value regardless of specific value
• Segment stacking in Workspace applies multiple segments simultaneously using AND logic between them
• Published segments are available in other Experience Cloud solutions (e.g., Audience Manager, Target) for activation

Cohort Analysis

• Cohort analysis groups users by a shared inclusion event and tracks a return metric over subsequent time periods
• Retention table: measures how many users who did action A later returned to do action B (same or different metric)
• Churn table (inverse of retention): shows percentage of cohort that did NOT return in each period
• Cohort granularity can be set to Day, Week, Month, Quarter, or Year
• Rolling calculation option in cohort shows retention relative to the previous period rather than the original cohort size
• Latency table in cohort analysis shows behavior before and after the inclusion event to identify lead-up patterns

Segment IQ and Comparison

• Segment Comparison panel automatically runs statistical tests across hundreds of metrics and dimensions to surface key differences
• Outputs include top differentiating metrics, top differentiating dimension items, and a Venn diagram of overlap
• Statistical significance threshold is configurable; default significance level is 95% confidence
• Segment Comparison is useful for comparing converted vs. non-converted users, mobile vs. desktop, or new vs. returning visitors
• The overlap percentage in the Venn diagram shows what portion of visitors qualify for both segments simultaneously

Translating Business Questions to Analytics

• Business questions map to specific Analytics components: 'How many people bought?' = Orders/Revenue metric; 'What brought them?' = Marketing Channel dimension
• KPI frameworks should align metrics to business objectives: Acquisition (Visits, New Visitors), Engagement (Pages/Visit, Time on Site), Conversion (Orders, Revenue, Goal Completions)
• When defining measurement strategy, identify the primary success metric, secondary metrics, and diagnostic dimensions for each business goal
• Stakeholder requirements should drive report suite configuration decisions including which eVars, props, and events to implement
• Data layer design should capture all required business dimensions before implementation to avoid retroactive data gaps

Interpreting and Communicating Insights

• Anomaly Detection uses statistical modeling (Holt-Winters algorithm) to identify unexpected spikes or drops in time-series data
• Contribution Analysis identifies which dimension items statistically contributed to an anomaly (requires Adobe Analytics Premium/Select)
• Intelligent Alerts notify users when metrics exceed thresholds or anomalies are detected, delivered via email or SMS
• When presenting data to stakeholders, use Summary Number visualizations, scorecards, and curated Workspace projects to reduce complexity
• Context is critical: always annotate known events (campaigns, outages, seasonality) to prevent misinterpretation of data fluctuations