Build a cross-channel analysis workspace

A cross-channel analysis workspace unifies behavioral signals from every customer touchpoint — web sessions, voice interactions, call-center transcripts — into a single person-level view that makes previously invisible journey segments visible to analysts without requiring SQL skills. The workspace is built in two layers: a data connection that stitches raw channel datasets on a shared identity key, and a data view that maps raw field paths to analyst-friendly dimension and metric names.

The stitching decision is the most consequential configuration choice. The Person ID field must exist in every dataset being joined; using email address is common but introduces latency if a visitor is not yet authenticated. Practitioners must also decide on backfill depth, session timeout thresholds, and how to handle datasets that lack a direct Person ID (using a derived field or a lookup dataset). Once the connection is live, the data view exposes the joined schema to the workspace layer where funnels, flows, and attribution analyses are assembled as reusable components.

Parallel viability: Medium parallelism. Snowflake or Databricks with a BI front-end (Looker, Tableau, Metabase) can replicate the cross-channel join and funnel visualization patterns. The primary CJA advantages are real-time data freshness from the AEP streaming pipeline, native session-level stitching that uses AEP's Identity Service graph, and the no-code workspace experience aimed at marketing analysts. Teams with existing data-warehouse infrastructure and analyst SQL fluency should evaluate whether the composable path meets their latency and identity-resolution requirements before adopting CJA.