Beyond Pageviews: Seeing the Whole Customer Journey with CJA
Sylexa Team · 11/25/2025
Most analytics tools tell you what happened on your website. But your customers don’t live only on your website. They see your ad, browse your app, chat with your call center, walk into your store, abandon, come back later, and finally convert after an email reminder.
Customer Journey Analytics (CJA) exists to make that story visible.
What is Customer Journey Analytics?
Customer Journey Analytics is Adobe’s next‑generation analytics solution, built on Adobe Experience Platform (AEP). You can think of it as Analysis Workspace sitting on top of Adobe Experience Platform.
Instead of being limited to web or app data, CJA lets you bring in any event or record data from AEP—stitched at the person/profile level—and analyze it in the familiar Analysis Workspace interface.
- Bring in web, app, email events
- Add call center logs and POS/in‑store transactions
- Join CRM and offline datasets
- Analyze together with people‑level identity stitching
Why CJA vs “classic” Adobe Analytics?
Traditional Adobe Analytics excels at digital analytics, but has structural limits. CJA removes many of them with flexible schemas, cross‑channel joins, and report‑time transformations.
| Dimension | Adobe Analytics | Customer Journey Analytics |
|---|---|---|
| Data model | eVars, props, events | Flexible XDM schemas (dimensions + metrics) |
| Limits | Slots & unique value limits | Unlimited variables & unique values |
| Cross‑channel | Mostly digital, per report suite | Fully cross‑channel, cross‑dataset |
| Historical fixes | Hard to retro‑fix data | Non‑destructive, report‑time transformations |
| Identity | ECID + some stitching | Full identity graph across IDs |
How CJA works (in simple steps)
Data lands in Adobe Experience Platform, is modeled in XDM, and then selected via a Connection in CJA. Data Views define how fields become dimensions/metrics—and session logic—using report‑time transformations. Analysts then build Workspace projects with freeform tables, flows, fallouts, cohorts, and journey visualizations.
- Ingest data into AEP via tags, ETL, connectors, or batch; model with XDM.
- Create a Connection in CJA to select datasets (e.g., web + call center + orders).
- Define Data Views: field mappings, sessionization, filters, derived metrics.
- Analyze in Workspace: freeform tables, Flows/Fallouts, Cohorts, journey visuals.
Key use cases: what teams actually do
With cross‑channel data at the person level, teams move from siloed channel views to customer‑centric journeys.
- True omnichannel journeys: web → app → call center → store → email
- Fallout across multi‑step processes to find drop‑offs
- Cohorts to track retention, upgrades, and returns
- Customer‑centric KPIs (revenue per person, time‑to‑first‑purchase, multi‑touch attribution)
A narrative use case: from insight to action
Scenario: A subscription service sees high abandonment after trial. Data in Platform includes web usage, in‑app events, billing events, and support tickets. Build a CJA Connection to join datasets at the person level and define Data Views for sessionization.
In Workspace, identify pre‑churn patterns (e.g., feature X never used) and channels where users seek help before cancel. Create an audience of likely‑to‑churn users, publish to Real‑Time CDP, and orchestrate in AJO with in‑app guides, email sequences, and offers. Close the loop by measuring journey impact on retention and LTV in CJA.
Why CJA matters
If Adobe Target is how you test & personalize and AEM is how you deliver experiences, CJA is how you truly understand what happens across the entire journey.
Without it, you see fragments. With it, you can finally ask: what journeys lead to the outcomes we care about—and how do we design more of them?