Summary Data in Customer Journey Analytics

Technical Overview of Summary Data Architecture

Summary data in Adobe CJA represents aggregated information that lacks individual person identifiers but provides critical macro-level insights. Unlike event data tied to specific customer IDs, summary data operates at campaign, channel, or time-period levels.

Key Technical Characteristics

• Temporal Granularity: Summary data ingestion supports hourly and daily intervals exclusively

• Schema Foundation: Built on XDM Summary Metrics class within Adobe Experience Platform

• Data Independence: Can function as standalone datasets or integrate with event and lookup data

• Time-Series Structure: Optimized for chronological analysis and trend identification

Primary Use Cases

• Advertising platform metrics (impressions, clicks, CPM, conversion rates)

• Social media performance indicators (engagement rates, reach, follower growth)

• Application analytics (downloads, session duration, crash rates)

• Business objective tracking and KPI monitoring

Implementation Architecture and Data Flow

Schema Design Requirements

The implementation begins with proper XDM schema configuration in Adobe Experience Platform. Three distinct schema types are required:

1. Summary Data Schema (XDM Summary Metrics Base Class)

Fields:

- timestamp (required)

- metric_values (impressions, clicks, cost)

- dimension_identifiers (campaign_id, platform_id)

- aggregation_period (hourly/daily)

2. Event Data Schema (XDM ExperienceEvent Base Class)

Fields:

- _id (unique identifier)

- timestamp

- identityMap (person_id)

- tracking_code

- conversion_events

- web_interaction_data

3. Lookup Data Schema (XDM Individual Profile Base Class)

Fields:

- primary_key (campaign_id/tracking_code)

- descriptive_attributes (campaign_name, audience_segment)

- contextual_metadata

A guide to implement Summary Data in Adobe Customer Journey Analytics (CJA)

The Problem Statement
Modern digital marketing organizations generate substantial volumes of aggregated data that exist independently of individual user identifiers. This non-person-based data, including campaign performance metrics, social media analytics, and business KPIs, presents unique analytical challenges when integrated with traditional customer journey analysis.

The Solution
Adobe Customer Journey Analytics (CJA) addresses this gap through its summary data functionality, enabling organizations to combine aggregated metrics with event-level data for comprehensive cross-channel analysis. This technical guide examines the implementation methodology, schema requirements, and analytical capabilities of summary data within the Adobe Experience Platform ecosystem.

Data Ingestion Process

Step 1: Dataset Creation in Adobe Experience Platform

Navigate to Experience Platform → Datasets → Create Dataset from Schema

For each schema type:

1. Select corresponding XDM schema

2. Configure dataset naming convention

3. Enable dataset for Profile (if applicable)

4. Set data governance labels

Step 2: Data Upload via Batch Ingestion

Using Experience Platform Workflows:

1. Select "Map CSV to XDM Schema" workflow

2. Choose target dataset

3. Configure field mapping to XDM schema

4. Validate data format and delimiters

5. Execute batch ingestion process

Data Format Requirements:

- CSV format with UTF-8 encoding

- Timestamp format: ISO 8601 standard

- Numeric fields: Decimal notation without currency symbols

- Consistent delimiter usage throughout file

Customer Journey Analytics Configuration

Connection Setup

In Adobe CJA, establish connections to all three dataset types:

Connection Configuration Parameters:

- Data Source: Adobe Experience Platform datasets

- Person ID: Not applicable for summary data; use tracking_code for event data

- Timestamp Field: Specify primary timestamp field for each dataset

- Data Import Settings: Configure historical data import range

- Advanced Settings: Enable dataset combination and cross-dataset analysis

Data View Architecture

The Data View configuration determines how summary, event, and lookup data combine for analysis.

Critical Configuration Steps:

• Metric Definition

  • Requires familiarity with Data Prep syntax. No error displayed in the UI for syntax error in the variable path.

  • Import summary metrics (impressions, clicks, cost) as calculated metrics

  • Configure aggregation methods (sum, average, count)

  • Set up derived metrics for calculated values (CTR, CPC, ROAS)

• Dimension Mapping

  • Primary Linking Dimension: tracking_code/campaign_id

  • Summary Data Dimensions: platform, campaign_type, date_range

  • Event Data Dimensions: page_name, conversion_type, user_segment

  • Lookup Data Dimensions: campaign_name, audience_description

• Data Relationship Configuration

  To properly link datasets:

  • Event-Summary Linkage: Use tracking_code as common dimension

    - In Event Data tracking_code dimension settings

    - Enable "Create grouping" option

    - Select Campaign ID from Summary Data Group dropdown

  • Lookup-Summary Integration: Create derived fields for descriptive attributes

    - Create derived field for campaign_name lookup

    - Configure lookup matching: tracking_code → campaign_id

    - Select return values from lookup dataset

• Attribution Model Settings

  Summary data components have specific limitations:

  • No session-based attribution (data lacks session context)

  • No person-based persistence (aggregated data has no individual identifiers)

  • Time-based attribution only (last-touch, first-touch within time windows)

Analytical Capabilities and Reporting

Workspace Implementation

With properly configured data views, Adobe Analytics Workspace enables sophisticated cross-dataset analysis:

• Available Analysis Types:

  • Cohort Analysis: Combine summary campaign data with user retention metrics

  • Attribution Analysis: Multi-touch attribution across summary and event data

  • Trend Analysis: Time-series analysis of campaign performance alongside user behavior

  • Segmentation: Create segments combining summary metrics with user characteristics

• Report Configuration

  • Metrics:

    - Summary: Impressions, Clicks, Cost

    - Event: Page Views, Conversions, Revenue

    - Calculated: Cost per Conversion, Return on Ad Spend

  • Dimensions:

    - Time: Day, Week, Month (from summary data timestamps)

    - Campaign: Campaign Name (from lookup data)

    - User Behavior: Page Name, Conversion Type (from event data)

  • Filters:

    - Date Range: Apply to all data sources

    - Campaign Type: Filter across summary and lookup data

    - User Segment: Apply to event data with summary data correlation

Conclusion

Summary data implementation in Adobe Customer Journey Analytics enables sophisticated analysis combining macro-level campaign performance with individual customer behavior insights. Proper technical implementation requires careful attention to schema design, data relationship configuration, and timestamp management.

The integration of summary data expands analytical capabilities beyond traditional person-based analysis, providing organizations with comprehensive cross-channel performance visibility. Success depends on thorough technical planning, proper data governance, and ongoing performance monitoring.

Through systematic implementation of these technical frameworks, organizations can leverage Adobe CJA's summary data capabilities to achieve more complete and actionable marketing analytics insights.

15+ years of IT work experience as Technical delivery Lead, Analytics Architect, AEP/CJA Implementation Consultant.

Adobe certified expert in Adobe Analytics, Adobe Target, Adobe Experience Platform (AEP), Real-Time Customer Data Platform (RT-CDP), Customer Journey Analytics (CJA), Journey Optimizer (AJO). Well versed with Google Analytics Server-side, Conversion API (CAPI), Privacy & Consent Management (OneTrust).

Deputed to Canada, USA, Netherlands, Germany, UK to work closely with business clients, business analysts, solution architects, solution designers, and other key stakeholders. Passionate to decode the online consumer behaviour by using an analytics data-driven approach.

https://www.linkedin.com/in/pradeep2020/

Pradeep.Jaiswal@ShiftLytic.ca