sf-agent-framework/sf-core/utils/adoption-tracker.md

AI Agent Orchestration Framework for Salesforce Development - Two-phase architecture with 70% context reduction

# Adoption Tracker Utility - Agent Instructions

## Purpose

This utility provides instructions for AI agents to generate comprehensive
adoption tracking and analytics solutions for Salesforce organizations, helping
measure and improve platform utilization.

## Agent Instructions

### When to Generate Adoption Tracking

Generate adoption tracking components when:

- Organizations need to measure Salesforce user engagement
- Executive dashboards require adoption metrics
- Gamification or incentive programs are being implemented
- User training effectiveness needs to be measured
- License optimization analysis is required
- Change management initiatives need tracking

### Core Components to Generate

#### 1. Adoption Metrics Engine

Generate an Apex class that:

- Tracks login frequency and patterns
- Measures feature utilization rates
- Calculates data quality contributions
- Monitors process compliance
- Computes weighted adoption scores
- Analyzes usage trends over time

#### 2. User Behavior Tracking

Create Lightning Web Components that:

- Capture user interactions and clicks
- Track time spent on different features
- Monitor navigation patterns
- Record feature discovery and usage
- Collect performance metrics
- Respect user privacy settings

#### 3. Data Collection Framework

Implement tracking mechanisms for:

- Login history analysis
- Object and field usage statistics
- Report and dashboard utilization
- Mobile app engagement
- API usage patterns
- Custom feature adoption

### Configuration Requirements

#### Custom Metadata Types

Generate custom metadata for:

```yaml
Adoption_Settings__mdt:
  - Tracking_Enabled__c (Checkbox)
  - Retention_Days__c (Number)
  - Batch_Size__c (Number)
  - Feature_List__c (Long Text Area)
  - Scoring_Algorithm__c (Picklist)

Adoption_Weights__mdt:
  - Metric_Type__c (Picklist)
  - Weight__c (Number)
  - Active__c (Checkbox)
  - Description__c (Text)
```

#### Custom Objects

Create objects for:

```yaml
Adoption_Event__c:
  - User__c (Lookup to User)
  - Feature__c (Text)
  - Action__c (Text)
  - Timestamp__c (DateTime)
  - Session_Id__c (Text)
  - Metadata__c (Long Text Area)

Adoption_Score__c:
  - User__c (Lookup to User)
  - Period__c (Text)
  - Login_Score__c (Number)
  - Feature_Score__c (Number)
  - Data_Score__c (Number)
  - Overall_Score__c (Number)
  - Calculation_Date__c (Date)
```

### Implementation Patterns

#### Batch Processing Pattern

For large-scale adoption calculations:

1. Implement Database.Batchable interface
2. Process users in chunks of 200
3. Use stateful batch for aggregation
4. Schedule daily/weekly calculations
5. Handle governor limits appropriately

#### Real-time Tracking Pattern

For immediate adoption insights:

1. Use Platform Events for tracking
2. Implement event subscribers
3. Update metrics asynchronously
4. Cache frequently accessed data
5. Minimize performance impact

#### Privacy-First Pattern

Ensure compliance by:

1. Implementing opt-out mechanisms
2. Anonymizing sensitive data
3. Respecting data retention policies
4. Providing data export capabilities
5. Following GDPR/CCPA requirements

### Dashboard Components to Generate

#### Executive Dashboard

Create components showing:

- Organization-wide adoption score
- Department/team comparisons
- Feature adoption trends
- User segmentation analysis
- License utilization rates
- ROI metrics

#### Manager Dashboard

Include views for:

- Team member adoption scores
- Individual progress tracking
- Training needs identification
- Performance correlations
- Coaching opportunities

#### User Dashboard

Display personal metrics:

- Individual adoption score
- Feature usage history
- Achievement badges
- Peer comparisons (anonymized)
- Improvement suggestions

### Integration Points

#### Chatter Integration

- Post adoption milestones
- Share team achievements
- Announce champions
- Provide tips and tricks

#### Email Integration

- Send periodic adoption reports
- Alert on score changes
- Notify achievements
- Share best practices

#### Analytics Integration

- Export to Tableau/PowerBI
- Create Einstein Analytics datasets
- Generate predictive models
- Identify adoption patterns

### Best Practices to Implement

1. **Performance Optimization**
   - Use selective queries with indexes
   - Implement efficient caching strategies
   - Batch process large datasets
   - Minimize synchronous operations

2. **Data Quality**
   - Validate tracking data integrity
   - Handle edge cases gracefully
   - Implement error recovery
   - Log anomalies for review

3. **User Experience**
   - Make tracking transparent
   - Provide value to users
   - Minimize intrusion
   - Offer clear benefits

4. **Scalability**
   - Design for growth
   - Handle millions of events
   - Optimize storage usage
   - Plan for archival

### Error Handling Instructions

Implement comprehensive error handling:

1. Catch and log all exceptions
2. Provide fallback mechanisms
3. Alert administrators of issues
4. Ensure data consistency
5. Implement retry logic

### Testing Requirements

Generate test classes that:

1. Cover all tracking scenarios
2. Test batch processing
3. Validate calculations
4. Verify privacy controls
5. Ensure performance standards

### Security Considerations

Implement security measures:

1. Enforce CRUD/FLS in all queries
2. Validate user permissions
3. Encrypt sensitive data
4. Implement sharing rules
5. Audit data access