In the ever-evolving landscape of customer data management, the transition from Adobe Audience Manager (AAM) to Real-time CDP (RTCDP) represents not merely an upgrade but a profound evolution. As business use cases mature and become more sophisticated, the need for a unified, real-time customer data platform has become increasingly imperative. This transition marks a significant shift in the way businesses approach customer data, empowering them to gain deeper insights, deliver personalized omnichannel experiences, and drive business growth.
In this blog, let’s candidly outline how common tasks will change for an AAM Admin now in charge of RTCDP.
Data Sources
Data Sources in AAM serve multiple purposes:
- Identity Namespace – when a data source is created with the ID Type:
”Cross device ID” and Inbound Customer ID – This is similar to defining an RTCDP Namespace in the Identity section. - Data Sources also act as labels for Traits and Segments for Role-based Access Controls (RBAC). In RTCDP, the comparable attribute level feature is Attribute Based Access Controls (ABAC) which allows admins to control right down to the field or RTCDP Audience.
Not much conceptually, the management of Data Sources was always low-touch unless it involved ingesting new data or perhaps more granular (RBAC) permissions. The added support for custom labels can add further support to more granular Destination enforcement “types”.
| Purpose | AAM | RTCDP |
|---|---|---|
| Data Governance | – Set Data Export Controls on Data Sources | – Manage Labels and Policies |
| Granular Permissions | – Assign Traits to Data Sources – Assign Segments to Data Sources | – Assign Labels to Schema Fields – Assign Labels to Audiences |
| Create New Identity | – Create a new Data Source with Cross device – Customer ID | – Create a new custom Identity |
Signals
Signals are the raw key and value pairs that are received by AAM servers. As a reminder, the data is not stored as a Signal. Signals are less stuctured which has means that it is less strict to ingest data however without strict validation it can also result in data loss.
RTCDP uses the XDM Schema stucture. Standardised out of the box field groups are included to cater for common data attributes.
| Purpose | AAM | RTCDP |
|---|---|---|
| Monitoring | – Monitor using the Signals Dashboard | – Setup alerts for any ingestion errors |
| Data Modelling | – Create requirements and standards for key value pairs from different sources such as online or offline systems | – Create & maintain a XDM data model from multiple systems, data connectors – Work with IT to ingest the data in the correct format or manage the mapping |
Traits
Traits are the key data building blocks in AAM. Each unique combination of (1 or more) key-value pairs would have to be created and maintained in the event that we needed to activate based on that pair in a Segment.
In RTCDP, there is a concept of a unified profile. We do not have to worry about creating trait-level data structures. We control what goes into this real-time customer profile based on the Dataset setting.
| Purpose | AAM | RTCDP |
|---|---|---|
| Data “storage” | – Create and maintain a large collection of Traits | – Not required. Keep an eye on licensing guardrails. |
| Data Retention | – Trait expiry – Data Source retention | – Profile TTL – Dataset TTL |
Segments
Segments are how we define data that is to be activated. Segments can be made up of one or more Traits.
In RTCDP, we do not have Traits, instead we can build rule-based Audiences based on:
- Attributes: latest value of a field
- Events: value of fields over time
- Other Audiences
| Purpose | AAM | RTCDP |
|---|---|---|
| Rules based Segments | Create Segments based on other existing Traits | Create Audiences by using the Segment Builder to combine: any combination of attributes and/or events or other existing Audiences |
| Modelled Segments | Create look-alike models to generate algorithmic Traits to be used in SegmentsCreate a predictive audience model to generate classification Segments | Create new Audiences based on the look-alike model generated for every AudienceCreate a Customer AI model and create the Audience based on a propensity score |
Destinations
The concept of Destinations remains similar for both AAM and RTCDP. The major change is that some RTCDP Destinations enable you to export attributes as well as the audience membership.
| Type | AAM | RTCDP |
|---|---|---|
| Out of the box Destinations | Request server to server Destinations to be created by Adobe client care | Create the Destination using the UI flow |
| Emerging Destinations | Request a custom Destination to S3 bucket to be created by Adobe client care | Configure cloud Destinations in the UI for S3, Azure, Kinesis, HTTP APIRequest that the partner or create your own with the Adobe Destination SDK |
Reporting
Reporting in AAM is either at the Trait or Segment level. Reports include Traits or Segment counts, trend report, overlap reports etc.
With RTCDP, there is more flexibility as you will have accept to a Query Service to write SQL-like queries to get quicker and more custom validation and reporting.
| Type | AAM | RTCDP |
|---|---|---|
| General Reports | – Review Segment Detail Page – Review General Report | Review Audience Dasboard |
| Overlap, trend, etc | Review Interactive and Overlap Reports | Review various widgets such as Audience overlap |
| Custom reports | More difficult, would need to export Segment membership | Write SQL-like queries |
Conclusion
The transition from AAM to RTCDP will feel reassuringly familiar for power users. While bringing more advanced functionality, RTCDP retains core concepts that allow experienced AAM administrators to leverage their expertise. The unified customer profile, flexible segmentation, and expanded integration options build logically upon well-known components.
