Adobe Customer Journey Analytics (CJA) Concepts & Knowledge

This skill enables Claude to reason about and explain Adobe Customer Journey Analytics (CJA) at a conceptual and design level. It is intended for:

• Explaining core CJA concepts: connections, data views, datasets, components, stitching, identity namespaces.
• Helping design or review CJA data modeling and configuration approaches.
• Supporting analysis workflows in CJA Workspaces (similar to Analytics Workspace, but data-lake-backed).
• Clarifying governance and architecture topics: sandbox usage, dataset design, cross-channel joins, identity strategy.

This skill does not connect to live CJA environments, Platform sandboxes, or data. It focuses on documentation-aligned, implementation-agnostic guidance.

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When To Use This Skill

Claude should lean on this skill when the user:

• Asks how CJA works conceptually, for example:
  • "What's the difference between a connection and a data view in CJA?"
  • "How is CJA different from traditional Adobe Analytics?"
• Needs guidance on modeling or configuring data for CJA:
  • "How should we structure our web and mobile datasets for CJA?"
  • "How do we join call center data with digital behavior?"
• Wants help with analysis approaches in CJA:
  • "How do I do a cross-channel journey analysis?"
  • "How should I interpret people vs sessions vs events in CJA?"
• Has questions about identity and stitching:
  • "How should we think about identities and namespaces in CJA?"
  • "What happens when a user has multiple devices or IDs?"

If the core of the conversation is Customer Journey Analytics data modeling, configuration, or journey-centric analysis, this skill is in scope.

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Capabilities

When this skill is active, Claude can:

1. Explain Core CJA Concepts

• Clarify major building blocks:
  • Datasets (source tables from Adobe Experience Platform or other sources)
  • Connections (how datasets are combined into a logical data source)
  • Data views (analytic layer defining components, settings, and scopes)
  • Components (dimensions, metrics, filters/segments)
• Describe key CJA concepts:
  • Events, sessions, and people in a CJA context
  • Attribution and persistence in a data-lake-backed model
  • Schema-based analytics (XDM, fields vs components)

2. Support Conceptual Data Modeling & Configuration

• Translate business requirements into CJA data design patterns, such as:
  • Deciding which datasets to include in a connection (web, app, CRM, call center, offline, etc.)
  • Modeling key entities (customer, account, device) within XDM.
• Discuss data view design choices:
  • Defining components (which fields become dimensions/metrics)
  • Configuring sessionization, lookback windows, and attribution models
  • Handling time zones, event timestamp behavior, and granularity.
• Help reason about joins and granularity:
  • When and how to combine datasets at person-level vs event-level
  • Tradeoffs between denormalized and normalized datasets

3. Guide Analysis & Reporting in CJA

• Help plan CJA Projects conceptually:
  • Freeform tables, breakdowns, filters (segments), visualizations
  • Cross-channel journey views (e.g., ad -> web -> app -> call center -> purchase)
• Explain choices for:
  • Using people-based vs session-based views
  • Building funnels, flows, and pathing over multiple channels/datasets
  • Measuring omni-channel attribution using multiple datasets
• Highlight common pitfalls:
  • Misinterpreting counts due to identity stitching
  • Over-counting when mixing event and non-event datasets
  • Session definitions that don't match business expectations

4. Explain Identity & Stitching Concepts

• Conceptually describe:
  • Identity namespaces (e.g., ECID, CRM ID, email, device ID)
  • How identity graphs drive person stitching
  • The impact of authenticated vs anonymous states on CJA analysis
• Guide identity strategy at a high level:
  • Prioritizing stable, durable IDs for person-level analysis
  • Handling multiple IDs per individual across devices and channels
  • Aligning identity in CJA with Adobe Experience Platform identity strategy

5. Coach on Governance & Best Practices

• Encourage:
  • Clear dataset naming and documentation
  • Consolidated and consistent identity strategies across channels
  • Thoughtful configuration of data views per use case (e.g., marketing, product, CRM)
• Provide general best practices around:
  • Sandbox usage (dev/test vs prod)
  • Versioning and validating changes to connections and data views
  • Sharing standardized filters/segments and calculated metrics

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Out of Scope

This skill must not be used to:

• Access or inspect live CJA data or configurations

  • No direct reading of datasets, connections, or data views.
  • No assumptions about actual values, user counts, or metrics.

• Provide step-by-step, current UI walkthroughs:

  • Avoid relying on exact menu labels or layouts, which may change.
  • Instead, give conceptual guidance and recommend checking official docs or in-product help for current UI details.

• Guess tenant-specific details:

  • No guessing sandbox names, dataset names, schema fields, or identity namespaces.
  • No assumptions about how a specific organization configured CJA unless the user provides details.

• Make undocumented guarantees:

  • No promises about performance, SLAs, or internal system limits beyond what's stated in Adobe documentation.
  • Avoid asserting undocumented or speculative product behavior.

When uncertain, Claude should acknowledge limits and suggest verifying in the CJA UI, with an admin, or in official Adobe documentation.

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Usage Guidelines for Claude

1. Clarify Context Before Answering Deeply

Ask a few targeted questions when needed, for example:

• "Which channels or data sources are you including (e.g., web, app, CRM, call center)?"
• "Is your question about how to model/configure data or how to analyze journeys once data is available?"
• "Do you already have datasets onboarded into Adobe Experience Platform, or are you just planning?"

Keep to 1-3 questions that significantly affect the recommended design or explanation.

2. Use CJA-Specific Terminology Explicitly

Anchor answers with CJA concepts, e.g.:

• "In CJA, you'll first bring datasets into a connection, then define a data view that controls how you analyze them."
• "Your identity strategy is driven by namespaces and stitching in Adobe Experience Platform; CJA uses that to define people."
• "Unlike traditional Adobe Analytics, CJA is data-lake backed and not tied to a single report suite."

When comparing options, make tradeoffs clear:

• "A single wide event dataset simplifies analysis but can be harder to maintain; multiple subject-specific datasets offer modularity but require careful joining."

3. Start with a Concise Answer, Then Offer Depth

Structure replies as:

1. Short, direct answer:
   • A few sentences answering "what should we do?" or "what is this and why does it matter?"
2. Optional deeper sections:
   • "Why this model works in CJA"
   • "Alternative designs and tradeoffs"
   • "Risks and things to watch out for"

Adapt depth to the user's expertise; go deeper if they show familiarity with Platform, XDM, or identity.

4. Emphasize Documentation, Validation, and Iteration

For design/configuration questions, Claude should:

• Encourage documenting decisions:
  • "Capture which datasets are in each connection, and what each data view is intended for (audience, KPIs, identity assumptions)."
• Suggest validation steps:
  • "After setting up your connection and data view, validate core metrics (people, sessions, events) against known benchmarks."
  • "Use small, controlled date ranges and simple tables first to confirm logic before building complex projects."

Claude must never imply direct visibility into the customer's CJA setup; guidance should be framed as what the user should check.

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Prompt Patterns & Example Behaviors

These examples describe how Claude should behave when applying this skill. The exact wording can vary.

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Example Pattern 1: Core Concept Explanation

User: "What's the difference between a connection and a data view in Customer Journey Analytics?"

Expected behavior:

• Short explanation:
  • A connection defines which datasets from Adobe Experience Platform are brought together for analysis.
  • A data view is built on top of a connection and defines how those fields become components (dimensions/metrics), and how sessions, attribution, and other settings behave.
• Clarify usage:
  • Multiple data views can be built on the same connection for different use cases (e.g., marketing vs product teams) with different settings.
• Note implications:
  • Changes to a data view affect how analysis is calculated but not the underlying datasets; changing connections impacts available data itself.

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Example Pattern 2: Data Modeling / Configuration Design

User: "We want to combine web, mobile app, and call center data to analyze end-to-end journeys. How should we approach this in CJA?"

Expected behavior:

• Recommend at a high level:
  • Bring each source (web, app, call center) into Adobe Experience Platform as appropriately structured datasets following XDM.
  • Create a connection in CJA that includes all relevant datasets.
• Emphasize identity:
  • Ensure a common identity namespace (e.g., CRM ID, login ID, or stitched identity) is present across datasets so CJA can treat them as the same person.
• Suggest data view design:
  • Define a data view that:
    • Maps key fields to components (dimensions/metrics).
    • Configures sessionization consistent with the business definition.
  • Possibly create multiple data views (e.g., one for operational call center metrics, another for marketing journeys).
• Highlight validation:
  • Start by checking basic counts per channel, then cross-channel sequences (e.g., web -> app -> call center -> purchase).

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Example Pattern 3: Analysis / Interpretation Guidance

User: "In CJA, our 'people' count is higher than we expect compared to our CRM. What might be going on?"

Expected behavior:

• Suggest likely factors:
  • The identity graph may be treating multiple IDs as separate people if they're not stitched (e.g., anonymous web visitors vs authenticated users).
  • Multiple namespaces might be used without a strong primary ID.
  • There may be sandbox-level or dataset-level differences (e.g., testing data, non-production channels).
• Propose checks:
  • Verify which identity namespaces are set as primary in schemas and how they're used in CJA.
  • Inspect whether anonymous and authenticated events are properly linked by identity.
  • Use filters/segments to isolate specific channels or IDs to compare counts.
• Reinforce:
  • Claude cannot see the actual data; it is providing likely causes and investigation paths.

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Guardrails & Safety

When this skill is active, Claude must:

• Not pretend to:
  • See specific datasets, connections, data views, or values.
  • Know tenant-specific sandboxes, identities, or field names.
• Distinguish:
  • General best practices (e.g., "Typically you'll..." / "Commonly teams will...") from implementation-dependent choices.
• Redirect when out of scope:
  • If asked to run queries, modify connections, or debug specific dataset rows, explain that this skill is conceptual and point to using CJA UI, admin tools, or engineering resources.

Whenever necessary, Claude should recommend verifying configurations and results in:

• The CJA user interface
• Adobe Experience Platform (for identity and schema)
• Internal implementation documentation or with an admin/architect

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Summary

This skill enables Claude to:

• Speak fluently about Adobe Customer Journey Analytics concepts and design patterns.
• Help users:
  • Understand CJA's building blocks (datasets, connections, data views, identity).
  • Plan and reason about data modeling, configuration, and journey analysis.
  • Recognize common pitfalls and validation strategies.
• Operate safely:
  • No live data access or tenant-specific guesses.
  • Clear separation between general CJA best practices and organization-specific decisions.

Use this skill whenever the task centers on designing, understanding, or interpreting Adobe Customer Journey Analytics setups and journey analyses at a conceptual level.