Skip to content

Journey Map Template

Document Information

Version: 1.0 Last Updated: December 2025 Status: Active Owner: Product Team

How to Use This Template

A journey map visualizes a user's experience as they interact with the platform to accomplish a specific goal. Journey maps help teams:

  • Understand the end-to-end user experience
  • Identify pain points and moments of delight
  • Discover opportunities for improvement
  • Align teams around user needs
  • Prioritize improvements based on impact

Creating Your Journey Map:

  1. Choose a specific persona and scenario
  2. Map the actual journey (based on research), not the ideal one
  3. Include both actions and emotions
  4. Identify specific pain points and opportunities
  5. Use visuals to make it engaging and memorable
  6. Validate with real users

Journey Map Template Structure

1. Persona and Scenario

Persona: [Name and brief description - reference to your persona document]

Scenario: [Specific goal or task the user is trying to accomplish]

Context: [Relevant background - when, why, and under what conditions this journey occurs]

Frequency: [How often users undertake this journey - daily, weekly, monthly, rarely]

Business Value: [Why this journey matters to the business]

2. Journey Stages

Break the journey into logical phases. Common stages:

  • Awareness/Discovery: User becomes aware of need or capability
  • Learning: User researches and learns how to accomplish goal
  • Setup/Configuration: User prepares tools and environment
  • Execution: User performs the main task
  • Monitoring/Validation: User confirms success
  • Reflection: User evaluates experience and outcome

3. Actions Per Stage

For each stage, document:

  • What the user does (specific steps)
  • What tools/systems they use
  • Who they interact with
  • What information they need

4. Thoughts and Emotions

For each stage, capture:

  • Thoughts: What's going through the user's mind?
  • Emotions: How do they feel? (frustrated, confident, confused, delighted)
  • Emotional Intensity: Rate from 1-5 or show as a line graph
  • Quotes: Direct quotes from user research

5. Pain Points

For each stage, identify:

  • Specific problems or friction
  • Root causes
  • Severity (critical, major, minor)
  • Frequency (how often this occurs)

6. Opportunities for Improvement

For each pain point:

  • Proposed solution or improvement
  • Expected impact (effort vs. value)
  • Priority (high, medium, low)
  • Owner/team responsible

7. Touchpoints with Platform

For each stage, note:

  • Platform features used
  • Documentation consulted
  • Support channels utilized
  • External tools integrated

Journey Map Template (Table Format)

Stage Actions Thoughts Emotions Pain Points Opportunities Touchpoints
[Stage 1] [User actions] [User thoughts] [Emotions: 😊 😐 😟] [Problems encountered] [Improvement ideas] [Platform features used]
[Stage 2] [User actions] [User thoughts] [Emotions: 😊 😐 😟] [Problems encountered] [Improvement ideas] [Platform features used]
[Stage 3] [User actions] [User thoughts] [Emotions: 😊 😐 😟] [Problems encountered] [Improvement ideas] [Platform features used]

Journey Map Template (Mermaid Diagram Format)

Text-Based Journey Flow

graph TD
    Start([User Goal: Goal Description]) --> Stage1[Stage 1: Name]
    Stage1 --> Stage2[Stage 2: Name]
    Stage2 --> Stage3[Stage 3: Name]
    Stage3 --> Stage4[Stage 4: Name]
    Stage4 --> End([Goal Achieved])

    Stage1 -.->|Pain Point| Pain1[Pain: Description]
    Stage2 -.->|Pain Point| Pain2[Pain: Description]
    Stage3 -.->|Pain Point| Pain3[Pain: Description]

    Pain1 -.->|Opportunity| Opp1[Improve: Solution]
    Pain2 -.->|Opportunity| Opp2[Improve: Solution]
    Pain3 -.->|Opportunity| Opp3[Improve: Solution]

    style Start fill:#e1f5e1
    style End fill:#e1f5e1
    style Pain1 fill:#ffe1e1
    style Pain2 fill:#ffe1e1
    style Pain3 fill:#ffe1e1
    style Opp1 fill:#e1e5ff
    style Opp2 fill:#e1e5ff
    style Opp3 fill:#e1e5ff

Emotional Journey Line

%%{init: {'theme':'base'}}%%
graph LR
    subgraph "Emotional Journey"
        A[Stage 1: High 😊] --> B[Stage 2: Medium 😐]
        B --> C[Stage 3: Low 😟]
        C --> D[Stage 4: High 😊]
        D --> E[Stage 5: Medium 😐]
    end

    style A fill:#90EE90
    style B fill:#FFD700
    style C fill:#FF6B6B
    style D fill:#90EE90
    style E fill:#FFD700

Detailed Journey Map with Swim Lanes

graph TB
    subgraph "Discovery"
        D1[Identify Need]
        D2[Search Documentation]
        D3{Find Answer?}
    end

    subgraph "Learning"
        L1[Read Tutorial]
        L2[Watch Demo Video]
        L3[Ask Colleague]
    end

    subgraph "Setup"
        S1[Configure Environment]
        S2[Install Dependencies]
        S3{Setup Success?}
    end

    subgraph "Execution"
        E1[Perform Task]
        E2[Troubleshoot Issues]
        E3[Complete Task]
    end

    subgraph "Validation"
        V1[Check Results]
        V2[Monitor for Issues]
        V3[Document Learnings]
    end

    D1 --> D2
    D2 --> D3
    D3 -->|Yes| S1
    D3 -->|No| L1

    L1 --> L2
    L2 --> L3
    L3 --> S1

    S1 --> S2
    S2 --> S3
    S3 -->|Yes| E1
    S3 -->|No| L3

    E1 --> E2
    E2 --> E3
    E3 --> V1

    V1 --> V2
    V2 --> V3

    style D3 fill:#FFD700
    style S3 fill:#FFD700
    style E2 fill:#FF6B6B
    style V3 fill:#90EE90

Example Journey Map: Deploying a New Microservice

1. Persona and Scenario

Persona: Maria Rodriguez - Application Developer

Scenario: Deploy a new payment validation microservice to production for the first time

Context: Maria has developed a new microservice that validates payment information before processing. It's been tested locally and in the dev environment. She needs to deploy it to production before the sprint ends tomorrow. This is her first time deploying a completely new service (not just an update to existing).

Frequency: Deploying a new service happens 2-3 times per year for a typical developer

Business Value: Successful deployments demonstrate platform efficiency (DORA metric: deployment frequency). Poor deployment experience causes developer frustration and delays feature delivery.

2. Detailed Journey Stages

Stage 1: Pre-Deployment Preparation (30 minutes)

Actions:

  1. Reviews deployment checklist from Confluence
  2. Ensures all tests are passing in CI pipeline
  3. Gets code review approval from tech lead
  4. Checks service dependencies are healthy
  5. Prepares rollback plan
  6. Notifies team in Mattermost about upcoming deployment

Thoughts:

  • "Did I configure everything correctly?"
  • "What if I forget something important?"
  • "Is there a complete checklist somewhere?"

Emotions: Anxious 😟 (Intensity: 3/5)

Pain Points:

  • Deployment checklist is outdated (last updated 6 months ago)
  • Unclear which environment variables are required
  • No automated validation of configuration before deployment
  • Rollback procedure is manual and poorly documented

Opportunities:

  • Automated pre-flight checks that validate configuration
  • Interactive deployment wizard that guides through requirements
  • Up-to-date checklist generated from actual deployment requirements
  • One-click rollback capability

Touchpoints:

  • Confluence (documentation)
  • Jenkins (CI pipeline status)
  • GitHub (code review)
  • Mattermost (team communication)

Stage 2: Service Configuration (45 minutes)

Actions:

  1. Creates Kubernetes deployment manifest
  2. Configures service discovery and load balancing
  3. Sets up environment-specific configuration
  4. Configures secrets and database connections
  5. Defines resource limits and autoscaling rules
  6. Asks platform team for clarification on ingress configuration

Thoughts:

  • "There must be an easier way to do this"
  • "Am I doing this the same way as the rest of the team?"
  • "Why do I need to know so much about Kubernetes?"

Emotions: Frustrated 😟 (Intensity: 4/5)

Pain Points:

  • Copying and modifying YAML from another service (brittle, error-prone)
  • Kubernetes concepts are complex for application developers
  • No template or wizard for common service types
  • Ingress configuration requires platform team expertise
  • Trial and error to get resource limits right

Opportunities:

  • Golden path template for common service types (REST API, worker, etc.)
  • Service scaffolding tool that generates configuration
  • Built-in validation that catches common configuration errors
  • Self-service ingress configuration with guardrails
  • Resource recommendations based on similar services

Touchpoints:

  • kubectl (Kubernetes CLI)
  • Text editor (writing YAML)
  • Kubernetes documentation
  • Platform team support channel
  • Another service's deployment as reference

Stage 3: Initial Deployment to Staging (1 hour)

Actions:

  1. Applies Kubernetes manifests to staging environment
  2. Waits for pods to start (unclear if they're actually starting)
  3. Deployment fails - pods in CrashLoopBackOff
  4. Searches logs in Kibana (takes 10 minutes to find the right logs)
  5. Discovers misconfigured database connection string
  6. Fixes configuration and redeploys
  7. Tests service endpoints manually with curl
  8. Verifies service in Grafana dashboard

Thoughts:

  • "Why is this taking so long?"
  • "What does CrashLoopBackOff even mean?"
  • "Where are the logs?"
  • "Is this going to work in production?"

Emotions: Stressed 😟 (Intensity: 5/5)

Pain Points:

  • No visibility into deployment progress or why pods aren't starting
  • Error messages from Kubernetes are cryptic
  • Finding relevant logs requires knowing multiple systems
  • No easy way to test service before declaring deployment successful
  • Manual verification steps are time-consuming and error-prone

Opportunities:

  • Real-time deployment status with clear progress indicators
  • Plain-English explanations of common deployment errors
  • Integrated log viewer that automatically shows relevant logs
  • Automated health checks and smoke tests post-deployment
  • Deployment success/failure notifications in Mattermost

Touchpoints:

  • kubectl (status checking)
  • Kibana (log searching)
  • Grafana (service metrics)
  • curl/Postman (manual testing)
  • Documentation (error troubleshooting)

Stage 4: Production Deployment (2 hours, including waiting)

Actions:

  1. Updates production manifests (careful copy-paste from staging)
  2. Posts in team channel: "Deploying to production, standby"
  3. Triggers production deployment manually via Jenkins job
  4. Watches Jenkins logs anxiously
  5. Deployment appears successful but has no traffic yet
  6. Updates DNS/ingress rules to route traffic to new service
  7. Watches Grafana for error rates (refreshing every 30 seconds)
  8. Everything looks good after 15 minutes
  9. Posts success message to team
  10. Documents deployment in Confluence runbook

Thoughts:

  • "Please don't let this break anything"
  • "Am I doing this at the right time?"
  • "How will I know if something goes wrong?"
  • "When can I stop watching the dashboard?"

Emotions: Anxious → Relieved 😟 → 😊 (Intensity: 4/5 → 2/5)

Pain Points:

  • Manual Jenkins job triggering (why not automatic from merge to main?)
  • No gradual rollout or traffic shifting (all-or-nothing deployment)
  • Unclear when it's safe to stop monitoring
  • No automatic rollback if errors spike
  • Have to stay online to monitor even though it's end of day

Opportunities:

  • GitOps automatic deployment on merge to main branch
  • Canary deployments with automatic traffic shifting
  • Automated rollback if error rates or latency exceed thresholds
  • Clear "deployment complete and healthy" notification
  • Scheduled deployments during business hours only

Touchpoints:

  • Jenkins (deployment trigger)
  • Kubernetes (deployment status)
  • Grafana (monitoring metrics)
  • Mattermost (team communication)
  • Confluence (documentation updates)

Stage 5: Post-Deployment Monitoring (Next day, 30 minutes)

Actions:

  1. Checks service metrics first thing in the morning
  2. Reviews overnight logs for any errors
  3. Validates business metrics (payment validations are processing)
  4. Updates team in standup
  5. Closes Jira ticket

Thoughts:

  • "Did everything stay healthy overnight?"
  • "I hope I didn't miss any errors"
  • "What should I be looking for?"

Emotions: Cautiously optimistic 😐 (Intensity: 2/5)

Pain Points:

  • Uncertain what "healthy" looks like for a new service
  • No proactive alerts, has to manually check
  • Business metrics are in a separate system from technical metrics
  • Unclear how long to keep watching

Opportunities:

  • Automated deployment validation and notification
  • Baseline metrics automatically generated for new services
  • Unified dashboard showing both technical and business metrics
  • Smart alerts that trigger only on actual anomalies
  • "Deployment health report" summarizing first 24 hours

Touchpoints:

  • Grafana (metrics)
  • Kibana (logs)
  • Business intelligence dashboard
  • Jira (ticket tracking)

3. Journey Map Visualization (Mermaid)

graph TB
    Start([Goal: Deploy New Microservice to Production]) --> Stage1[Stage 1: Pre-Deployment Preparation]

    Stage1 --> Stage2[Stage 2: Service Configuration]
    Stage2 --> Stage3[Stage 3: Deploy to Staging]
    Stage3 --> Decision1{Staging Deployment Successful?}

    Decision1 -->|No| Debug1[Debug CrashLoopBackOff]
    Debug1 --> Stage3
    Decision1 -->|Yes| Stage4[Stage 4: Production Deployment]

    Stage4 --> Monitor[Monitor for Errors]
    Monitor --> Decision2{Healthy After 15min?}

    Decision2 -->|No| Rollback[Manual Rollback]
    Rollback --> Debug2[Investigate and Fix]
    Debug2 --> Stage4

    Decision2 -->|Yes| Stage5[Stage 5: Post-Deployment Monitoring]
    Stage5 --> End([Deployment Complete])

    Stage1 -.->|Pain| Pain1["Outdated checklist<br/>No validation"]
    Stage2 -.->|Pain| Pain2["Complex YAML<br/>No templates"]
    Stage3 -.->|Pain| Pain3["Cryptic errors<br/>Hard to find logs"]
    Stage4 -.->|Pain| Pain4["Manual process<br/>All-or-nothing deployment"]
    Stage5 -.->|Pain| Pain5["Unclear success criteria<br/>Manual monitoring"]

    Pain1 -.->|Fix| Opp1["Automated pre-flight checks<br/>Deployment wizard"]
    Pain2 -.->|Fix| Opp2["Golden path templates<br/>Service scaffolding"]
    Pain3 -.->|Fix| Opp3["Clear error messages<br/>Integrated log viewer"]
    Pain4 -.->|Fix| Opp4["GitOps automation<br/>Canary deployments"]
    Pain5 -.->|Fix| Opp5["Health report<br/>Smart alerts"]

    style Start fill:#e1f5e1
    style End fill:#e1f5e1
    style Pain1 fill:#ffe1e1
    style Pain2 fill:#ffe1e1
    style Pain3 fill:#ffe1e1
    style Pain4 fill:#ffe1e1
    style Pain5 fill:#ffe1e1
    style Opp1 fill:#e1e5ff
    style Opp2 fill:#e1e5ff
    style Opp3 fill:#e1e5ff
    style Opp4 fill:#e1e5ff
    style Opp5 fill:#e1e5ff
    style Debug1 fill:#fff4e1
    style Rollback fill:#fff4e1
    style Debug2 fill:#fff4e1

4. Emotional Journey Graph

%%{init: {'theme':'base', 'themeVariables': {'fontSize':'16px'}}}%%
graph LR
    subgraph "Emotional Intensity Over Time"
        S1["Stage 1<br/>Preparation<br/>😟 3/5"] --> S2["Stage 2<br/>Configuration<br/>😟 4/5"]
        S2 --> S3["Stage 3<br/>Staging Deploy<br/>😟 5/5"]
        S3 --> S4["Stage 4<br/>Prod Deploy<br/>😟→😊 4/5→2/5"]
        S4 --> S5["Stage 5<br/>Monitoring<br/>😐 2/5"]
    end

    style S1 fill:#FFD700
    style S2 fill:#FFA500
    style S3 fill:#FF6B6B
    style S4 fill:#98D8C8
    style S5 fill:#90EE90

5. Summary: Key Insights

Biggest Pain Points (Prioritized):

  1. Service configuration complexity - Requires deep Kubernetes knowledge, no templates
  2. Difficult troubleshooting - Cryptic errors, hard to find relevant logs
  3. Manual deployment process - Time-consuming, stressful, error-prone
  4. Unclear success criteria - Don't know when it's safe to stop monitoring
  5. No safety net - All-or-nothing deployment, manual rollback

Quick Wins (High Impact, Low Effort):

  1. Create golden path service templates for common patterns
  2. Add automated pre-flight configuration validation
  3. Improve error messages with troubleshooting links
  4. Implement deployment notifications in Mattermost
  5. Create deployment health dashboard

Strategic Improvements (High Impact, Higher Effort):

  1. Implement GitOps with ArgoCD for automatic deployments
  2. Add canary deployment capability with automatic rollback
  3. Build service scaffolding tool ("create-service" wizard)
  4. Integrate logs, metrics, and traces in single view
  5. Develop automated smoke tests for deployments

Success Metrics:

  • Time to deploy new service: Target < 30 minutes (currently 4+ hours)
  • Developer confidence rating: Target > 8/10 (currently ~4/10)
  • Failed deployments: Target < 5% (currently ~20%)
  • Time to recover from failed deployment: Target < 5 minutes (currently ~30 minutes)

Additional Journey Map Examples

Example: Troubleshooting a Production Incident

graph TB
    Alert[Alert Received: High Error Rate] --> Assess[Assess Severity and Impact]
    Assess --> FindLogs[Search for Relevant Logs]
    FindLogs --> Correlate[Correlate Logs Across Services]
    Correlate --> Decision1{Root Cause Found?}

    Decision1 -->|No| CheckMetrics[Review Metrics and Traces]
    CheckMetrics --> CheckChanges[Review Recent Deployments]
    CheckChanges --> Decision1

    Decision1 -->|Yes| Fix[Implement Fix or Rollback]
    Fix --> Deploy[Deploy Fix to Production]
    Deploy --> Monitor[Monitor for Resolution]
    Monitor --> Decision2{Issue Resolved?}

    Decision2 -->|No| Assess
    Decision2 -->|Yes| Document[Document Incident and RCA]
    Document --> End([Incident Closed])

    FindLogs -.->|Pain| P1["Logs scattered across multiple systems"]
    Correlate -.->|Pain| P2["Manual correlation is time-consuming"]
    CheckMetrics -.->|Pain| P3["No single view of system health"]
    Deploy -.->|Pain| P4["Stressful emergency deployment"]

    style Alert fill:#FF6B6B
    style End fill:#90EE90
    style P1 fill:#ffe1e1
    style P2 fill:#ffe1e1
    style P3 fill:#ffe1e1
    style P4 fill:#ffe1e1

Example: Onboarding to the Platform

graph LR
    Start([First Day: Need to Deploy Code]) --> Learn[Read Platform Documentation]
    Learn --> Confused{Documentation Clear?}

    Confused -->|No| AskColleague[Ask Colleague for Help]
    AskColleague --> Shadow[Shadow Colleague's Workflow]

    Confused -->|Yes| Attempt[Attempt First Deployment]
    Shadow --> Attempt

    Attempt --> Stuck{Got Stuck?}
    Stuck -->|Yes| Search[Search Confluence & Mattermost]
    Search --> AskPlatform[Ask Platform Team]
    AskPlatform --> Attempt

    Stuck -->|No| Success[First Successful Deployment]
    Success --> Practice[Practice More Deployments]
    Practice --> Confident([Confident Platform User])

    Learn -.->|Pain| O1["Docs assume too much knowledge"]
    AskColleague -.->|Pain| O2["Interrupts colleague's work"]
    Search -.->|Pain| O3["Information scattered and outdated"]

    style Start fill:#e1f5e1
    style Confident fill:#90EE90
    style O1 fill:#ffe1e1
    style O2 fill:#ffe1e1
    style O3 fill:#ffe1e1

Tips for Creating Effective Journey Maps

Research-Based

  • Base on actual user research, not assumptions
  • Include direct quotes from users
  • Validate with multiple users
  • Update as you learn more

Specific and Concrete

  • Focus on one persona and one scenario
  • Use real examples and actual steps
  • Include specific pain points, not generalizations
  • Note actual time durations

Visual and Engaging

  • Use color to indicate emotion (green = positive, red = negative)
  • Include emojis for quick emotional scanning
  • Make it scannable (people should grasp it in 30 seconds)
  • Display prominently where team can see it

Actionable

  • Clearly identify opportunities for improvement
  • Prioritize based on impact and effort
  • Assign owners to improvements
  • Track which improvements have been addressed

Collaborative

  • Create journey maps as a team activity
  • Include people from different functions (product, engineering, design)
  • Workshop format: sticky notes on wall, then digitize
  • Share widely and gather feedback

Journey Map Workshop Format

Preparation (30 minutes before):

  • Select persona and scenario
  • Gather research materials (interview notes, analytics)
  • Prepare materials (sticky notes, markers, large paper/whiteboard)

Workshop (2 hours):

  1. Introduce persona and scenario (10 min)
  2. Brainstorm journey stages (15 min)
  3. Detail actions for each stage (20 min)
  4. Add thoughts and emotions (20 min)
  5. Identify pain points (20 min)
  6. Generate opportunities (20 min)
  7. Prioritize improvements (15 min)

Follow-up (After workshop):

  • Digitize journey map (Mermaid diagram)
  • Add to documentation
  • Share with stakeholders
  • Create backlog items for top opportunities
  • Schedule review in 3-6 months

Additional Resources

  • Interview Guide Template
  • Persona Template
  • Nielsen Norman Group Journey Mapping 101: https://www.nngroup.com/articles/customer-journey-mapping/
  • Adaptive Path's Guide to Experience Mapping
  • Mermaid Documentation: https://mermaid.js.org/