sf-agent-framework

# Metrics Calculator Utility - Agent Instructions ## Purpose This utility provides instructions for AI agents to generate comprehensive metrics calculation solutions for Salesforce implementations, enabling organizations to measure KPIs, performance metrics, and business outcomes. ## Agent Instructions ### When to Generate Metrics Calculation Generate metrics calculation components when: - KPIs need automated calculation - Business metrics require tracking - Performance indicators need measurement - ROI calculations are required - Adoption metrics need monitoring - Quality scores need computation - Compliance metrics need tracking ### Core Components to Generate #### 1. Metrics Calculation Engine Generate calculation components that: - Compute complex business metrics - Aggregate data across objects - Apply weighted scoring algorithms - Calculate time-based metrics - Process statistical measures - Generate composite scores Key calculations to support: - Sales metrics (pipeline, conversion, velocity) - Service metrics (resolution time, satisfaction) - Adoption metrics (usage, engagement) - Quality metrics (accuracy, completeness) - Performance metrics (speed, efficiency) - Financial metrics (ROI, cost savings) #### 2. Aggregation Framework Create aggregation components for: - Period-based rollups - Hierarchical calculations - Cross-object summaries - Real-time aggregations - Historical comparisons - Predictive calculations #### 3. Metrics Storage Manager Implement storage for: - Calculated metric values - Historical snapshots - Trend data - Benchmark values - Target tracking - Variance analysis ### Configuration Requirements #### Custom Objects ```yaml Metric_Definition__c: - Name (Text) - Category__c (Picklist) - Calculation_Formula__c (Long Text Area) - Target_Value__c (Number) - Frequency__c (Picklist) - Active__c (Checkbox) - Owner__c (Lookup to User) Metric_Result__c: - Metric_Definition__c (Master-Detail) - Period_Start__c (Date) - Period_End__c (Date) - Calculated_Value__c (Number) - Target_Value__c (Number) - Variance__c (Number) - Status__c (Picklist) Metric_Component__c: - Metric_Definition__c (Master-Detail) - Object_Name__c (Text) - Field_Name__c (Text) - Aggregation_Type__c (Picklist) - Filter_Criteria__c (Text Area) - Weight__c (Number) ``` ### Calculation Formulas to Implement #### Sales Velocity ``` Sales Velocity = (Number of Opportunities × Average Deal Size × Win Rate) / Sales Cycle Length ``` #### Customer Lifetime Value ``` CLV = (Average Purchase Value × Purchase Frequency × Customer Lifespan) - Customer Acquisition Cost ``` #### Service Level Achievement ``` SLA = (Cases Resolved Within SLA / Total Cases) × 100 ``` #### Adoption Score ``` Adoption = (Active Users × Feature Usage × Data Quality) / Total Users × 100 ``` ### Implementation Patterns #### Real-time Calculation Pattern 1. Capture triggering event 2. Identify affected metrics 3. Recalculate values 4. Update storage 5. Trigger notifications 6. Refresh dashboards #### Batch Calculation Pattern 1. Schedule calculation job 2. Query source data 3. Apply calculations 4. Store results 5. Generate snapshots 6. Send reports #### Incremental Update Pattern 1. Track data changes 2. Calculate deltas 3. Update aggregates 4. Maintain accuracy 5. Optimize performance ### Metric Categories to Support #### Business Metrics - Revenue metrics - Growth indicators - Market share - Customer metrics - Profitability - Efficiency ratios #### Operational Metrics - Process efficiency - Resource utilization - Cycle times - Quality measures - Productivity rates - Cost metrics #### Technical Metrics - System performance - API usage - Storage utilization - Error rates - Response times - Availability ### Dashboard Components to Generate #### Executive Metrics Dashboard Display: - Key metric cards - Trend visualizations - Target vs actual - YoY comparisons - Predictive insights - Action items #### Operational Dashboard Show: - Real-time metrics - Process indicators - Team performance - Quality scores - Bottleneck analysis - Improvement areas #### Analytical Dashboard Include: - Historical trends - Correlation analysis - Variance reports - Drill-down capabilities - Custom calculations - Export options ### Calculation Algorithms #### Weighted Average ``` 1. Define component weights 2. Calculate component values 3. Apply weights 4. Sum weighted values 5. Normalize if needed ``` #### Moving Average ``` 1. Define period window 2. Collect historical data 3. Calculate period average 4. Slide window forward 5. Smooth variations ``` #### Percentile Ranking ``` 1. Collect all values 2. Sort ascending 3. Calculate position 4. Determine percentile 5. Apply benchmarks ``` ### Integration Requirements #### Analytics Platform Integration - Einstein Analytics - Tableau CRM - Power BI - Custom analytics - Real-time streaming #### External System Integration - ERP metrics import - Financial systems - Marketing platforms - HR systems - Custom databases #### Notification Integration - Threshold alerts - Target achievements - Anomaly detection - Trend notifications - Report distribution ### Best Practices to Implement 1. **Calculation Accuracy** - Validate formulas - Test edge cases - Handle nulls - Document logic - Version control 2. **Performance Optimization** - Efficient queries - Indexed fields - Batch processing - Caching strategies - Async calculations 3. **Data Quality** - Input validation - Error handling - Missing data strategies - Outlier detection - Consistency checks 4. **Governance** - Metric ownership - Change control - Access management - Audit trails - Documentation ### Advanced Features to Consider 1. **Predictive Analytics** - Trend forecasting - Anomaly prediction - Goal achievement probability - Risk indicators - What-if scenarios 2. **Machine Learning Integration** - Pattern recognition - Automated insights - Recommendation engine - Optimization suggestions - Adaptive thresholds 3. **Real-time Processing** - Streaming calculations - Event-driven updates - Live dashboards - Instant alerts - Continuous monitoring ### Error Handling Instructions Handle these scenarios: 1. Division by zero 2. Null values 3. Data type mismatches 4. Formula errors 5. Performance timeouts Recovery strategies: - Default values - Error logging - Calculation retry - Manual override - Notification alerts ### Testing Requirements Generate test classes for: 1. Formula accuracy 2. Edge cases 3. Performance limits 4. Integration points 5. Error handling ### Success Metrics Track and measure: - Calculation accuracy - Processing speed - Dashboard usage - Alert effectiveness - User satisfaction - Business impact