As your organisation evolves to use Customer Journey Analytics (CJA), this is existing news and opens up use cases such as: call centre deflection and cross-channel attribution. Whilst your users will be happy that their Adobe Analytics (AA) Workspace skills are transferable to CJA, there are several changes to common implementation tasks and BAU admin activities.
This post is aimed at an administrator audience.
No more props and eVars
As a native Adobe Experience Platform (AEP) application, CJA uses XDM and its schema structure. What does it mean?
- Unlimited dimensions – no need to consider prop vs eVar differences, no limits in managing the variable allocations as strictly.
- Structure – the data types supported are modernised e.g. nested objects and attributes.
- Deep dive provided here: https://www.datanalyst.info/adobe-experience-platform/data-architecture-for-cja-admin/
Three Free listvars
listVars were probably the most prized item in AA but since CJA uses XDM, it supports unlimited string arrays which can be used similarly to listVars.
More Control
There is more control on what is the primary key whereas in AA it was always the ECID or AAID before that. Now as an admin, you can create different Connections to bring together multiple data sources. e.g.
- Digital traffic reporting – using ECID as the Person ID.
- Customer journeys – using a hashed customer ID as the Person ID.
- Stitching is also available to have a mixed view comparable to Cross-Device Analytics.
If you have inherited a legacy implementation of multiple AA report suites at the device type level (web vs mobile app) or a single brand instead of a global report suite. Piping the data using the Analytics Data Connector to AEP (CJA) in the short term could unlock cross-device and cross-brand reporting without data collection changes.
Flexible (Data) Views
Data Views on the surface look similar to Virtual Report Suites in AA. Whilst it can accomplish that utility, it unlocks even more flexibility without altering data collection. If your organisation has limited release cycles specifically in secure environments, Data Views are a game changer:
- Create metrics from strings – we may not have control over the ingested data format, but rules can be created to match strings and count them as events.
- Create dimension values from integers – bucket values to abstract complexity for Workspace users.
- Customising the “No Value” to contextual message to increase confidence in the data.
Marketing Channels
If you remember the last time that you had to edit the Marketing Channel Processing Rules, these rules were normally set during the implementation. This concept is different in CJA, it can be implemented with Derived Fields.
Find more info and other uses for Derived Fields here. The comparative feature for AA Processing rules is also Derived Fields.
Customer Attributes Enhancement
Breaking down data with fields such as CRM, demographic or NPS scores is native as part of the XDM model. Profile Datasets can be ingested from AEP with CJA connections.
Classifications to Lookups
Classifications are implemented via Lookup Datasets, these structures can also be used with AEP Audiences. In doing so, at the data modelling stage, the data is ingested in the same manner for both insights and direct activation with Real-Time CDP or Adobe Journey Optimizer. Look up Datasets can be used for both event or profile Datasets.
Classification Rule Builder
The comporable feature is Substring which is configured at the Data View level.
Low Traffic, Less Likely
Although uncommon in the first place, there are fewer chances to encounter low traffic bucketing. This is useful when troubleshooting error codes or granular person-level data. More info here: https://experienceleague.adobe.com/docs/analytics-platform/using/cja-components/dimensions/high-cardinality.html?lang=en
Data Warehouse
The comparable feature is full table exports or alternatively you can consume the a custom view using the Query Service:
Data Validation
In AA, the validation would stop at client-side data collection checks. With the variety of data ingested for analysis in CJA, the added functionality of using the flexibility of SQL queries via Query Service to access the raw data is better as a self-serve option.
Conclusion
On the journey to switch from long-standing processes and workflows, there are more and more tools and resources that facilitate the transition to higher maturity use cases. Make sure to bookmark the full AA to CJA feature comparison page to assess the new features and ways of doing the tasks.
Continue your learning by joining the Experience League and I recommend the blogs by Brian Au.