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Lead Scoring System Specifications

Overview​

The Lead Scoring system provides intelligent lead qualification and scoring capabilities to help identify high-value prospects and optimize marketing efforts.

Features​

1. Lead Scoring Performance​

Route: / (default) and /lead-scoring Navigation: Lead Scoring > Lead Scoring Performance

Core Functionality​

  • Real-time Scoring: Live lead scoring as leads come in
  • Performance Dashboard: Visual representation of scoring performance
  • Score Distribution: Analyze score distribution across leads
  • Conversion Tracking: Track conversion rates by score ranges
  • ROI Analysis: Measure return on investment by score
  • Trend Analysis: Historical performance trends

Key Metrics​

  • Lead Volume: Total number of leads processed
  • Average Score: Mean lead score across all leads
  • High-Quality Leads: Percentage of leads above threshold
  • Conversion Rate: Percentage of leads that convert
  • Score Accuracy: Model accuracy and performance
  • Revenue Attribution: Revenue generated by scored leads

Dashboard Components​

  • Score Distribution Chart: Histogram of lead scores
  • Conversion Funnel: Lead progression through stages
  • Performance Metrics: Key performance indicators
  • Trend Charts: Historical performance data
  • Top Performing Sources: Best lead sources by score
  • Geographic Analysis: Lead quality by location

2. Lead Scoring Settings​

Route: /lead-scoring-settings Navigation: Lead Scoring > Lead Scoring Settings

Core Functionality​

  • Model Configuration: Configure scoring models and parameters
  • Feature Management: Manage scoring features and weights
  • Threshold Settings: Set score thresholds for lead qualification
  • Model Training: Train and retrain scoring models
  • A/B Testing: Test different model configurations
  • Performance Monitoring: Monitor model performance metrics

Configuration Options​

  • Scoring Criteria: Define what factors influence scoring
  • Weight Assignment: Assign weights to different features
  • Threshold Configuration: Set qualification thresholds
  • Model Selection: Choose between different ML models
  • Feature Engineering: Create and modify scoring features
  • Validation Rules: Set up data validation rules

Model Management​

  • Model Training: Train new models with historical data
  • Model Validation: Validate model performance
  • Model Deployment: Deploy trained models to production
  • Model Monitoring: Monitor model performance over time
  • Model Rollback: Rollback to previous model versions
  • Model Comparison: Compare different model versions

3. Lead Scoring Audit​

Route: /lead-scoring-audit Navigation: Lead Scoring > Lead Scoring Audit

Core Functionality​

  • Audit Trail: Track all scoring decisions and changes
  • Data Quality: Monitor data quality and completeness
  • Model Drift: Detect model performance degradation
  • Bias Detection: Identify potential bias in scoring
  • Compliance Reporting: Generate compliance reports
  • Performance Analysis: Detailed performance analysis

Audit Features​

  • Decision Logging: Log all scoring decisions
  • Change Tracking: Track configuration changes
  • User Activity: Monitor user actions and changes
  • Data Lineage: Track data flow and transformations
  • Error Logging: Log scoring errors and exceptions
  • Performance Metrics: Track system performance

Data Models​

Lead Model​

interface Lead {
  id: string;
  client_id: string;
  email: string;
  name: string;
  phone?: string;
  company?: string;
  source: string;
  score: number;
  score_confidence: number;
  qualified: boolean;
  conversion_status: 'new' | 'contacted' | 'qualified' | 'converted' | 'lost';
  conversion_date?: Date;
  revenue?: number;
  created_at: Date;
  updated_at: Date;
  scored_at: Date;
}

Scoring Model​

interface ScoringModel {
  id: string;
  name: string;
  version: string;
  model_type: 'logistic_regression' | 'random_forest' | 'neural_network';
  features: ScoringFeature[];
  weights: Record<string, number>;
  thresholds: {
    qualified: number;
    high_priority: number;
    low_priority: number;
  };
  accuracy: number;
  precision: number;
  recall: number;
  f1_score: number;
  is_active: boolean;
  created_at: Date;
  updated_at: Date;
}

Scoring Feature​

interface ScoringFeature {
  name: string;
  type: 'numerical' | 'categorical' | 'boolean';
  weight: number;
  transformation?: string;
  validation_rules: ValidationRule[];
  is_active: boolean;
}

API Endpoints​

Lead Scoring​

  • GET /api/v1/leadscoring/leads - List scored leads
  • POST /api/v1/leadscoring/score - Score a lead
  • GET /api/v1/leadscoring/performance - Get performance metrics
  • GET /api/v1/leadscoring/analytics - Get analytics data
  • POST /api/v1/leadscoring/batch-score - Score multiple leads

Model Management​

  • GET /api/v1/leadscoring/models - List scoring models
  • POST /api/v1/leadscoring/models - Create new model
  • PUT /api/v1/leadscoring/models/{id} - Update model
  • POST /api/v1/leadscoring/models/{id}/train - Train model
  • POST /api/v1/leadscoring/models/{id}/deploy - Deploy model
  • GET /api/v1/leadscoring/models/{id}/performance - Get model performance

Configuration​

  • GET /api/v1/leadscoring/config - Get scoring configuration
  • PUT /api/v1/leadscoring/config - Update configuration
  • GET /api/v1/leadscoring/features - List scoring features
  • POST /api/v1/leadscoring/features - Create feature
  • PUT /api/v1/leadscoring/features/{id} - Update feature

Scoring Features​

Behavioral Features​

  • Email Engagement: Open rates, click rates, response rates
  • Website Activity: Page views, time on site, bounce rate
  • Content Interaction: Downloads, form submissions, video views
  • Social Media: Social media engagement and activity
  • Campaign Interaction: Email campaign engagement

Demographic Features​

  • Company Size: Number of employees
  • Industry: Company industry classification
  • Location: Geographic location
  • Job Title: Professional role and seniority
  • Company Revenue: Annual revenue range

Firmographic Features​

  • Technology Stack: Technologies used
  • Company Stage: Startup, growth, enterprise
  • Funding Status: Venture capital funding
  • Growth Rate: Company growth metrics
  • Market Position: Market share and position

Engagement Features​

  • Response Time: Time to respond to communications
  • Meeting Attendance: Meeting attendance rates
  • Content Consumption: Content engagement patterns
  • Support Interactions: Customer support interactions
  • Purchase History: Previous purchase behavior

Machine Learning Models​

Logistic Regression​

  • Use Case: Linear relationship modeling
  • Pros: Interpretable, fast, stable
  • Cons: Limited to linear relationships
  • Best For: Simple scoring with clear linear patterns

Random Forest​

  • Use Case: Non-linear relationship modeling
  • Pros: Handles non-linear relationships, feature importance
  • Cons: Less interpretable, can overfit
  • Best For: Complex scoring with multiple interactions

Neural Networks​

  • Use Case: Complex pattern recognition
  • Pros: Handles complex patterns, high accuracy
  • Cons: Requires large datasets, less interpretable
  • Best For: Advanced scoring with rich data

Performance Metrics​

Model Performance​

  • Accuracy: Overall prediction accuracy
  • Precision: True positive rate
  • Recall: Sensitivity to positive cases
  • F1 Score: Harmonic mean of precision and recall
  • AUC-ROC: Area under the ROC curve
  • Confusion Matrix: Detailed performance breakdown

Business Metrics​

  • Conversion Rate: Percentage of leads that convert
  • Revenue per Lead: Average revenue generated per lead
  • Cost per Conversion: Cost to acquire a conversion
  • ROI: Return on investment
  • Lead Quality Score: Overall lead quality assessment
  • Sales Velocity: Speed of lead progression

Data Quality & Validation​

Data Validation​

  • Completeness: Check for missing required fields
  • Accuracy: Validate data accuracy and consistency
  • Format: Ensure data format compliance
  • Range: Validate data within expected ranges
  • Uniqueness: Check for duplicate records
  • Timeliness: Ensure data freshness

Data Cleaning​

  • Duplicate Removal: Remove duplicate leads
  • Outlier Detection: Identify and handle outliers
  • Missing Value Handling: Handle missing data
  • Data Normalization: Normalize data for scoring
  • Feature Engineering: Create derived features
  • Data Transformation: Transform data for model input

Integration Points​

CRM Integration​

  • Lead Sync: Sync leads with CRM systems
  • Score Updates: Update CRM with latest scores
  • Conversion Tracking: Track conversions back to scores
  • Pipeline Management: Manage lead pipeline stages

Marketing Automation​

  • Triggered Campaigns: Trigger campaigns based on scores
  • Segmentation: Segment leads by score ranges
  • Personalization: Personalize content based on scores
  • Lead Nurturing: Nurture leads based on scores

Analytics Integration​

  • Performance Tracking: Track scoring performance
  • Attribution: Attribute conversions to scoring
  • ROI Analysis: Analyze return on investment
  • Trend Analysis: Analyze scoring trends

Security & Compliance​

Data Privacy​

  • PII Protection: Protect personally identifiable information
  • Data Encryption: Encrypt sensitive data
  • Access Control: Control access to scoring data
  • Audit Logging: Log all data access and changes
  • Data Retention: Manage data retention policies

Compliance​

  • GDPR Compliance: European data protection compliance
  • CCPA Compliance: California privacy compliance
  • SOC 2: Security and availability compliance
  • Data Governance: Data governance policies
  • Risk Management: Risk assessment and mitigation

Testing Strategy​

Unit Tests​

  • Scoring Logic: Test scoring algorithms
  • Feature Engineering: Test feature creation
  • Model Validation: Test model validation
  • Data Processing: Test data processing logic
  • Error Handling: Test error scenarios

Integration Tests​

  • API Endpoints: Test all API endpoints
  • Database Operations: Test data persistence
  • External Integrations: Test third-party integrations
  • Model Training: Test model training process
  • Performance Testing: Test system performance

End-to-End Tests​

  • Lead Scoring Flow: Test complete scoring process
  • Model Deployment: Test model deployment
  • Performance Monitoring: Test monitoring systems
  • User Workflows: Test user interactions
  • Error Scenarios: Test error handling

Future Enhancements​

Planned Features​

  • Real-time Scoring: Real-time lead scoring
  • Advanced ML Models: More sophisticated models
  • Feature Store: Centralized feature management
  • Model Explainability: Explain model decisions
  • A/B Testing: Test different scoring approaches
  • Multi-channel Scoring: Score across multiple channels
  • Predictive Analytics: Predict future lead behavior
  • Automated Retraining: Automatic model retraining