| Ability to combine datasets (such as Adobe Analytics report suites) | Customer Journey Analytics allows you to combine data from multiple report suites as if they were a single report suite in Adobe Analytics. |
| Accommodation for any kind of data | Customer Journey Analytics is combined with Experience Platform’s ability to hold all kinds of data schemas and types. Using Experience Data Model (XDM), data can be uniformly represented and organized, ready for combination and exploration. Adobe Analytics is predominantly focused on web and mobile analytics data with some capabilities to import data. |
| BI Extension | The BI Extension lets you connect CJA directly to popular BI visualization tools such as PowerBI or Tableau. By using this extension, you can have your BI reports precisely match what you see in Analysis Workspace and other CJA reporting interfaces. This is a much easier way to get BI reporting for CJA without the need to recreate reports/metrics from raw data. |
| Content Analytics | Content Analytics helps marketers to understand how content impacts the key performance indicators that a business has defined. On top of the behavioral data, Content Analytics collects data on how content is consumed and how content drives impact. |
| Cross-Device Analytics | Customer Journey Analytics supports the seamless combination of device-specific datasets from unauthenticated and authenticated sessions. Customer Journey Analytics offers to backfill historical data to known devices. In Adobe Analytics, this capability is limited to a single report suite and the use of a device graph. |
| Dimension enhancements |
Customer Journey Analytics provides greater flexibility when using dimensions:
In Adobe Analytics, only a handful of built-in numeric dimensions were available, and sorting by string-based dimensions was not possible.
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| Derived Fields | Derived fields allow for report-time transformations to your data. Data can be combined, corrected, or created on the fly and these transformations apply retroactively to all reporting. |
| Enhanced security and privacy options - HIPAA readiness | Customer Journey Analytics is HIPAA ready and offers additional security options for regulation compliance. Adobe Analytics is not HIPAA ready. |
| Experimentation analysis | Customer Journey Analytics can evaluate the lift and confidence of any experiment from any data source defined as part of a connection. This evaluation allows you to understand cause-and-effect relationships between customer interactions spanning any channel. Analytics is limited to experimentation analysis through A4T. |
| Forecasting | Forecasting is an AI/ML capability that includes a statistical prediction for time-series-related data based on the historical data that already exists in Customer Journey Analytics. Forecasts can appear in freeform tables and line graph visualizations. |
| Guided analysis | Guided analysis enables users to self-serve high quality data and insights about the customer journey through guided workflows, built on the cross-channel data of Customer Journey Analytics. |
| Intelligent Captions | Intelligent captions use advanced Machine Learning and Generative AI to provide valuable natural-language insights for Workspace visualizations. Intelligent captions are supported for the following visualizations: Line, Multi-line, Bar, Horizontal bar, Donut, Area, Flow, and Fallout. |
| Journey Canvas | Journey canvas is a visualization in Analysis workspace that allows you to analyze how people proceed through or fall out of a defined journey. |
| Product Usage | Product usage shows you how your organization uses Customer Journey Analytics. |
| Report-time transformations | Data views in Customer Journey Analytics allow you to further interpret data from a connection. You can alter or remove data without changing your implementation, use substrings to manipulate dimensions, create metrics from any value, or filter subevents. All of these transformations are done non-destructively. Adobe Analytics provides limited capabilities through virtual report suites and custom session length. |
| Shared metrics and dimensions across data views | Allows you to apply dimension and metric settings across multiple data views. Changes made to a shared dimension or metric apply to all instances of that dimension or metric across all applicable data views. |
| SQL access | Using the Data Distiller option, Customer Journey Analytics can remove the limitations of data collected on Adobe’s backend processing. You can modify your data with SQL, create values and datasets unique to your business and continue to explore. Analytics does not support any kind of SQL access to its data. |
| Stitching | Stitching is a powerful feature that elevates an event dataset’s suitability for cross-channel analysis. Cross-channel analysis is a main use case that Customer Journey Analytics can handle, allowing you to seamlessly combine and run reports on multiple datasets from different channels, based on a common identifier (person ID). |
| Templates in Adobe Journey Optimizer | Customize the new reporting interface in Adobe Journey Optimizer by creating or editing a template in Customer Journey Analytics, then saving the template to be used on the Reports page in Journey Optimizer. |
| Unlimited customer dimensions and metrics | Customer Journey Analytics dimensions are unlimited; values can be numeric, text, objects, lists, or mixtures of all. Dimensions can be nested or hierarchical.
By contrast, Adobe Analytics supports up to a maximum of 75 props and 250 eVars. |
| Unlimited unique values |
Customer Journey Analytics supports unlimited unique values or dimension items that can be reported on within a single dimension.
There are no cardinality limits on a dimension, allowing for any unique value to appear and be counted.
This approach removes reporting and analysis limitations that can exist with large-scale Adobe Analytics implementations, resulting in Low Traffic labels.
In Customer Journey Analytics, it is possible to see a Uniques Exceeded label, but these occur far less frequently and can be mitigated by applying a segment to the data.
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