Customer Journey Analytics vs Adobe Analytics: what actually changes

Why this matters

This matters because analysts risk misinterpreting past data if they don’t understand how settings in Customer Journey Analytics are applied at report runtime. In Adobe Analytics, settings like filters or variables are static and apply consistently to historical data. In CJA, many settings live in the data view and can change how historical reports are interpreted—meaning a simple update can alter the meaning of past analysis. This creates confusion when comparing performance before and after changes.

Without awareness of this, analysts may draw incorrect conclusions about user behavior or campaign effectiveness. For example, a shift in how a person ID is defined or how data is filtered could make a trend appear or disappear. This is especially risky when moving from Adobe Analytics, where data consistency is more predictable. Understanding the dynamic nature of CJA settings ensures accurate, reliable insights across time.

The key ideas

Customer Journey Analytics and Adobe Analytics differ in how data is stored and used. Adobe Analytics gathers web and app data into report suites, where it’s organized and accessible through predefined structures. In contrast, Customer Journey Analytics works with datasets stored in Experience Platform, combining web, app, CRM, call-centre, and offline data into a single, unified view. This means data in Customer Journey Analytics is more diverse and can reflect real-world customer interactions across channels.

A key practical difference is how settings are applied. In Customer Journey Analytics, many settings—like filters or segmentation rules—are defined in the data view and are applied when a report runs. This means changing a setting can alter how historical data is interpreted, which is not the case in Adobe Analytics. In Adobe Analytics, custom variables like eVars and props are used to track events, while in Customer Journey Analytics, dimensions and metrics come from schema fields with clear, descriptive names, making them easier to understand without needing to memorize variable numbers.

When moving from Adobe Analytics to Customer Journey Analytics, analysts typically begin by selecting a Person ID and deciding which datasets belong to a connection. This foundational step ensures accurate tracking and analysis across the customer journey.

How to apply it

When moving from Adobe Analytics to Customer Journey Analytics, analysts should first define the Person ID and select which datasets—such as web, CRM, or call-centre data—will be included in the connection. This foundational step ensures data alignment across channels. Once the data source is set, the analysis settings are applied directly in the data view, meaning any changes to filters, time ranges, or segmentation affect how historical data is interpreted. This is different from Adobe Analytics, where settings are typically fixed in report suites and historical data remains unchanged unless manually reprocessed.

In Customer Journey Analytics, dimensions and metrics are named descriptively (e.g., “Customer Source” or “Session Duration”), unlike Adobe Analytics’ numbered custom variables (e.g., eVar1 or prop2). This makes it easier to understand and communicate findings without needing to memorize variable numbers. Analysts should leverage the Analysis Workspace in both tools, but now with a focus on adjusting data view configurations to reflect real-time or evolving business questions. This shift requires careful planning to avoid misinterpreting past trends due to updated filtering or segmentation.

Mistakes to avoid

• Avoid changing settings in a data view after running reports, as this can alter how historical data is interpreted. Since many analysis settings live in the data view and apply at report runtime, modifying them retroactively may lead to inconsistent or misleading insights over time.

• Don’t assume that dimensions and metrics in Customer Journey Analytics are equivalent to Adobe Analytics’ eVars or props. The descriptive schema fields in CJA require a different understanding of data structure. Always verify data mapping and alignment with business logic before building reports. Start with clear decisions on Person ID and dataset inclusion to ensure consistent, reliable analysis from day one.

Quick checklist

• Confirm the Person ID strategy and ensure it’s consistently applied across all datasets in the connection
• Identify which datasets (web, app, CRM, call-centre, offline) will be included and verify their data quality
• Review the data view settings in Customer Journey Analytics, as changes can alter how historical data is interpreted
• Map Adobe Analytics custom variables (eVars, props) to equivalent schema fields in Customer Journey Analytics using descriptive dimension names
• Understand that analysis settings in Customer Journey Analytics are applied at report runtime, so historical analysis may need revalidation
• Verify that the Analysis Workspace is used for both reporting, as it is the shared interface in both Adobe Analytics and Customer Journey Analytics
• Ensure all teams agree on the data sources and definitions before running any reports to maintain consistency
• Test a sample report using the new data view and schema to validate data interpretation and accuracy