Adobe Experience Platform (AEP)
Adobe Experience Platform is powerful — but only when its data models, identities, and governance are designed intentionally.
We help enterprise teams implement AEP as a scalable data foundation for analytics, personalization, and activation.
Where AEP implementations struggle
AEP is often introduced as part of a broader analytics or personalization initiative.
In practice, AEP programs struggle when:
- XDM schemas are created without long-term intent
- Identity namespaces and stitching rules are unclear
- Datasets proliferate without governance
- Upstream data quality issues are carried into the platform
- Activation use cases outpace data readiness
These challenges increase complexity without delivering clarity or scale.
Our approach to Adobe Experience Platform
We approach AEP as an enterprise data platform — not a collection of Adobe services.
Our work typically includes:
- Designing XDM schemas aligned to real use cases
- Defining identity strategy and namespace governance
- Structuring datasets for analytics and activation reuse
- Aligning Web SDK and Edge data with platform design
- Validating data flows end-to-end across consumers
This ensures AEP remains scalable, understandable, and trusted by downstream teams.
AEP in a connected ecosystem
AEP delivers the most value when it sits at the center of analytics, experimentation, and activation workflows.
We help teams integrate AEP with Adobe Analytics, Customer Journey Analytics, experimentation platforms, and marketing tools — so data can flow cleanly and consistently.
The goal is not more data, but better-aligned data across the organization.
When to engage us
Organizations typically engage us when:
- They are planning or actively implementing AEP
- XDM schemas and datasets feel hard to reason about
- Identity resolution outcomes are unclear or inconsistent
- AEP data is not trusted by analytics or activation teams
Not confident in your AEP foundation?
Request an analytics audit to review your AEP schemas, identity strategy, datasets, and downstream activation readiness — and identify where structure and clarity are needed.
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