Customer Journey Analytics, migrated and adopted.

What CJA replaces, and what it adds.

Customer Journey Analytics is the destination Adobe Analytics customers move to. The end-of-life of Adobe Analytics is making this migration urgent for most enterprises. The lift is non-trivial.

CJA does not just replace report suites. It widens the analytic surface. CJA can stitch web events, mobile events, call center events, point-of-sale events, and offline events into a single cross-channel view of the customer. The Analysis Workspace looks similar. The capability underneath is fundamentally different.

The migration.

A typical CJA migration covers three things: bringing the historical Adobe Analytics data into CJA, mapping every existing report and dashboard to its CJA equivalent, and training the team to operate the new tool.

The mistake most migrations make is treating CJA as a like-for-like replacement. It is not. Trying to recreate every Adobe Analytics report verbatim wastes the upside of the new platform. We migrate by use case, not by report. Every dashboard rebuilt is a chance to ask whether it is the right dashboard.

Top three stalls

1. XDM schema design (44% of projects in our benchmark stall here).
2. Stitching identity across web, mobile, and offline (38%).
3. User adoption beyond the analytics team (61%).

Cross-channel stitching.

Stitching is what makes CJA different from Adobe Analytics. Done well, you get a unified journey across every channel a customer touches. Done poorly, you get inflated visitor counts and broken conversion attributions.

We design the stitching strategy alongside the identity strategy in AEP. Both layers depend on the same identity namespace decisions. We get them right once. The downstream analytics work compounds.

Data views and derived fields.

Data views are the business-facing schemas in CJA. They are where you reshape the underlying event data into the metrics and dimensions that the marketing team uses every day.

Derived fields are calculated at query time. They let you fix data quality issues, normalize categorical values, and create new dimensions without going back to the data layer. Used well, derived fields shorten the time from question to answer by an order of magnitude. Used poorly, they multiply across a hundred dashboards and become a maintenance burden.

We build the data view strategy with the analytics team. We define which fields belong as physical fields, which belong as derived, and which belong as calculated metrics in the Workspace. The discipline matters more than the tool.

Analysis Workspace.

Workspace is the analyst surface in CJA. It is also the place where 80% of the value gets unlocked or buried. We build templated workspaces for the recurring analyses: traffic and engagement, journey performance, segment comparison, conversion attribution, and ad-hoc exploration. Templates compress time-to-insight for everyone who is not a power user.

User enablement across four stages.

Adoption is the gap that kills most CJA migrations. The analytics team learns the tool. The marketing team never logs in. The product team uses a different system. Six months later, the value is still locked in the workspace of three power users.

We run enablement in four stages.

Awareness

The audience knows CJA exists, knows what kinds of questions it can answer, and knows where to find help.

Understanding

The audience can read a workspace someone else built and answer the question it was built to answer.

Proficiency

The audience can build their own workspaces for recurring analyses.

Mastery

The audience can build derived fields, design new data views, and teach others.

Average time to Mastery is 6 months when supported. 14 months when self-led.

How we engage on CJA.

A typical CJA migration engagement runs 12 to 20 weeks for the technical migration, with a 6-month enablement program running alongside. We have shipped CJA migrations for financial services, telco, retail, and B2B software.