Available in production · $129 one-time

Customer Journey Analytics
Master Pack

The Cross-Channel Analysis Operator Academy.

Turn governed Experience Platform datasets into explainable decisions. Learn the contracts behind connections, person identity, stitching, data views, components, sessions, attribution, derived fields, Workspace, experimentation, exports, and production diagnosis.

Review product details Review the curriculum

Curriculum15 operator domains

Practice10 labs · 15 templates · 200 questions

Price$129 USD · one time

Customer Journey Analytics system map from Experience Platform datasets through connections and data views into analysis and governed decisions — Data does not become trustworthy analysis until every semantic handoff is explicit.

Start here in 15 minutes

Review the contracts in dependency order.

01Confirm the business question and grain.
02Trace identity, connection, and data-view semantics.
03Demand reconciliation evidence before trusting the chart.

Operator outcomes

Make every reported number explainable.

Design

Build the analytical contract.

Define question, grain, population, identity, measures, dimensions, time, and expected evidence before opening the UI.

Configure

Separate each semantic layer.

Keep dataset readiness, connection ingestion, stitching, data-view behavior, and project logic independently reviewable.

Diagnose

Find the first divergence.

Reconcile representative records through every handoff instead of changing several settings until a chart looks plausible.

Three ways to learn

See how trustworthy analysis is assembled.

Move from the operator mental model to a release board, then trace one question through data, semantics, Workspace, and decision evidence.

Operator field notes CJA · production sequence

Start with the question. Freeze the semantics before reading the chart.

1 · Write the decision

Name the question, grain, population, time window, and action the answer will support.

2 · Inspect source contracts

Check event, profile, lookup, and summary datasets for timestamps, keys, types, consent, latency, and representative records.

3 · Choose identity deliberately

Document the person ID, cross-dataset keys, stitching method, lookback, case handling, and expected unresolved population.

4 · Operate the connection

Track selected datasets, backfill, retention, ingestion latency, skipped records, volume, and accountable owners.

5 · Version the data view

Review components, session rules, persistence, attribution, derived fields, access, and change impact as one semantic release.

6 · Reconcile before sharing

Trace specimen journeys from source records to Workspace totals and preserve annotations, exceptions, and approval evidence.

A plausible chart is not proof. Trust begins when a reviewer can reproduce the population and explain every semantic handoff.

Use these notes when a metric changes, a data view is revised, or two teams produce different answers to the same question.

Animated mental model

Questions become decisions through governed semantics.

CJA can reinterpret compatible Platform data at reporting time, but flexibility increases the need for explicit definitions, version evidence, and impact review.

Animated CJA analysis loop from question through semantics and validation to a decision

Complete curriculum

Fifteen domains, ordered by dependency.

The buyer academy includes the complete curriculum, question banks, hands-on labs, reusable templates, and the Data View Design Review Workbench.

Architecture · All roles

CJA architecture and analytical contracts

Connect Platform data, semantic configuration, reporting surfaces, and decision evidence without treating the interface as a black box.

Foundations · Analyst

Questions, measures, entities, and grain

Translate a business decision into explicit events, entities, measures, dimensions, and acceptance specimens before configuring CJA.

Data · Architect

Schema and dataset readiness

Review event, profile, lookup, and summary datasets for timestamps, keys, field types, data quality, privacy, and analytical suitability.

Administration · Admin

Connections and ingestion operations

Design sandbox, dataset, retention, backfill, latency, volume, skipped-record, and ownership contracts.

Identity · Architect

Person identity and dataset linking

Choose person IDs and cross-dataset keys without merging devices, people, accounts, and households accidentally.

Identity · Advanced

Field-based and graph-based stitching

Assess prerequisites, consent, case sensitivity, replay, lookback, package boundaries, and validation evidence.

Semantics · Admin

Data-view architecture

Curate governed components, names, descriptions, compatibility, access, and release controls above a connection.

Semantics · Analyst

Metric and dimension behavior

Define value inclusion, formatting, deduplication, no-value handling, and reusable component behavior explicitly.

Interpretation · Analyst

Sessions, persistence, and attribution

Separate session boundaries, component persistence, attribution models, lookback windows, and reporting containers.

Modeling · Admin

Derived fields and reusable semantics

Use report-time transformation deliberately, with dependency, complexity, package-limit, validation, and change-impact controls.

Analysis · Analyst

Analysis Workspace foundations

Build projects with panels, tables, visualizations, segments, calculated metrics, date ranges, annotations, and QA evidence.

Analysis · All roles

Journey, funnel, cohort, retention, and guided analysis

Select the right analytical method, understand its population and sequence logic, and avoid overstating the result.

Experimentation · Advanced

Experimentation and causal boundaries

Prepare experiment data and interpret lift and confidence without treating a visualization as automatic causal approval.

Activation · Admin

Sharing, audiences, exports, APIs, and BI

Choose the appropriate governed path for scheduled projects, audience publishing, project exports, reporting APIs, and BI access.

Operations · Architect

Governance, performance, migration, and incidents

Operate access, changing guardrails, cost and volume, Adobe Analytics migration, release monitoring, and first-divergence diagnosis.

End-to-end scenario

Explain why conversion changed after a data-view release.

A retailer combines web, mobile, order, and support events. Conversion rises after a data-view edit. The operator freezes versions, checks source grain and person IDs, confirms connection ingestion, compares session and attribution settings, isolates a derived-field change, and reconciles representative journeys before publishing a conclusion.

QuestionDatasetsConnectionData viewAnalysisEvidence

Inside the release