Skip to content

πŸ” Data & AnalyticsΒΆ

🎯 Overview¢

The Data & Analytics component provides comprehensive data management, analytics, and reporting capabilities for the Email Marketing system. It handles contact management, campaign analytics, database operations, and report generation with real-time insights and performance tracking.

πŸ—οΈ ArchitectureΒΆ

Core ComponentsΒΆ

1. Contact ManagerΒΆ

Responsibility: Contact sourcing, validation, and management - LinkedIn Integration: Professional contact scraping and enrichment - Zero Bounce Integration: Email validation and verification - Contact Enrichment: Data enhancement and quality improvement - List Management: Contact segmentation and organization

2. Analytics EngineΒΆ

Responsibility: Campaign performance analysis and insights - Real-time Analytics: Live campaign performance monitoring - Performance Metrics: Comprehensive KPI tracking and analysis - Trend Analysis: Historical data analysis and trend identification - Predictive Analytics: Machine learning-based performance prediction

3. Database ManagerΒΆ

Responsibility: Data persistence and retrieval operations - PostgreSQL Management: Complex database operations and optimization - Data Modeling: Schema design and relationship management - Query Optimization: Performance tuning and query optimization - Backup & Recovery: Data protection and disaster recovery

4. Reporting SystemΒΆ

Responsibility: Report generation and distribution - Automated Reports: Scheduled report generation and delivery - Custom Dashboards: Interactive dashboard creation and management - Export Capabilities: Multiple format export (PDF, Excel, CSV) - Alert System: Proactive notification and alerting

πŸ”„ System WorkflowsΒΆ

Contact Management WorkflowΒΆ

sequenceDiagram
    participant CM as Contact Manager
    participant LI as LinkedIn API
    participant ZB as Zero Bounce
    participant DM as Database Manager
    participant AE as Analytics Engine

    CM->>LI: Scrape Contacts
    LI-->>CM: Raw Contact Data
    CM->>ZB: Validate Emails
    ZB-->>CM: Validation Results
    CM->>DM: Store Validated Contacts
    DM-->>CM: Storage Confirmed
    CM->>AE: Update Analytics
    AE-->>CM: Analytics Updated

Analytics Processing WorkflowΒΆ

graph TD
    A[Campaign Data] --> B[Data Collection]
    B --> C[Data Validation]
    C --> D[Data Processing]
    D --> E[Metric Calculation]
    E --> F[Trend Analysis]
    F --> G[Report Generation]
    G --> H[Distribution]

πŸ› οΈ Technical ImplementationΒΆ

Contact ManagerΒΆ

class ContactManager {
  constructor(linkedInAPI, zeroBounceAPI, databaseManager) {
    this.linkedInAPI = linkedInAPI;
    this.zeroBounceAPI = zeroBounceAPI;
    this.databaseManager = databaseManager;
  }

  async scrapeLinkedInContacts(searchCriteria) {
    const {
      keywords,
      location,
      industry,
      companySize,
      limit = 1000
    } = searchCriteria;

    try {
      // 1. Search LinkedIn profiles
      const profiles = await this.linkedInAPI.searchProfiles({
        keywords,
        location,
        industry,
        companySize,
        limit
      });

      // 2. Extract contact information
      const contacts = await this.extractContactInfo(profiles);

      // 3. Validate emails
      const validatedContacts = await this.validateEmails(contacts);

      // 4. Store in database
      await this.databaseManager.storeContacts(validatedContacts);

      return {
        total: contacts.length,
        valid: validatedContacts.length,
        invalid: contacts.length - validatedContacts.length
      };

    } catch (error) {
      console.error('LinkedIn scraping failed:', error);
      throw error;
    }
  }

  async validateEmails(contacts) {
    const emails = contacts.map(contact => contact.email);
    const validationResults = await this.zeroBounceAPI.validateBatch(emails);

    return contacts.filter(contact => {
      const result = validationResults.find(r => r.email === contact.email);
      return result && result.status === 'valid';
    });
  }

  async enrichContactData(contact) {
    const enrichmentData = await this.linkedInAPI.getProfileDetails(contact.linkedinUrl);

    return {
      ...contact,
      company: enrichmentData.company,
      position: enrichmentData.position,
      experience: enrichmentData.experience,
      education: enrichmentData.education,
      skills: enrichmentData.skills,
      connections: enrichmentData.connections
    };
  }
}

Analytics EngineΒΆ

class AnalyticsEngine {
  constructor(databaseManager, reportingSystem) {
    this.db = databaseManager;
    this.reporting = reportingSystem;
  }

  async calculateCampaignMetrics(campaignId) {
    const campaign = await this.db.getCampaign(campaignId);
    const activities = await this.db.getCampaignActivities(campaignId);

    const metrics = {
      delivery: this.calculateDeliveryMetrics(activities),
      engagement: this.calculateEngagementMetrics(activities),
      performance: this.calculatePerformanceMetrics(activities),
      trends: await this.calculateTrends(campaignId)
    };

    return metrics;
  }

  calculateDeliveryMetrics(activities) {
    const total = activities.length;
    const delivered = activities.filter(a => a.status === 'delivered').length;
    const bounced = activities.filter(a => a.status === 'bounced').length;
    const failed = activities.filter(a => a.status === 'failed').length;

    return {
      total,
      delivered,
      bounced,
      failed,
      deliveryRate: (delivered / total) * 100,
      bounceRate: (bounced / total) * 100,
      failureRate: (failed / total) * 100
    };
  }

  calculateEngagementMetrics(activities) {
    const delivered = activities.filter(a => a.status === 'delivered');
    const opened = delivered.filter(a => a.opened).length;
    const clicked = delivered.filter(a => a.clicked).length;
    const unsubscribed = delivered.filter(a => a.unsubscribed).length;

    return {
      openRate: (opened / delivered.length) * 100,
      clickRate: (clicked / delivered.length) * 100,
      unsubscribeRate: (unsubscribed / delivered.length) * 100,
      clickToOpenRate: (clicked / opened) * 100
    };
  }

  async calculateTrends(campaignId) {
    const historicalData = await this.db.getHistoricalCampaignData(campaignId);

    return {
      deliveryTrend: this.calculateTrend(historicalData, 'deliveryRate'),
      openTrend: this.calculateTrend(historicalData, 'openRate'),
      clickTrend: this.calculateTrend(historicalData, 'clickRate'),
      bounceTrend: this.calculateTrend(historicalData, 'bounceRate')
    };
  }

  calculateTrend(data, metric) {
    if (data.length < 2) return 'insufficient_data';

    const recent = data.slice(-7); // Last 7 days
    const previous = data.slice(-14, -7); // Previous 7 days

    const recentAvg = recent.reduce((sum, d) => sum + d[metric], 0) / recent.length;
    const previousAvg = previous.reduce((sum, d) => sum + d[metric], 0) / previous.length;

    const change = ((recentAvg - previousAvg) / previousAvg) * 100;

    if (change > 5) return 'increasing';
    if (change < -5) return 'decreasing';
    return 'stable';
  }
}

Database ManagerΒΆ

class DatabaseManager {
  constructor(pool, redis) {
    this.pool = pool;
    this.redis = redis;
  }

  async storeContacts(contacts) {
    const client = await this.pool.connect();

    try {
      await client.query('BEGIN');

      for (const contact of contacts) {
        await client.query(`
          INSERT INTO contacts (
            email, first_name, last_name, company, position,
            linkedin_url, phone, location, industry, source,
            created_at, updated_at
          ) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, NOW(), NOW())
          ON CONFLICT (email) DO UPDATE SET
            first_name = EXCLUDED.first_name,
            last_name = EXCLUDED.last_name,
            company = EXCLUDED.company,
            position = EXCLUDED.position,
            updated_at = NOW()
        `, [
          contact.email,
          contact.firstName,
          contact.lastName,
          contact.company,
          contact.position,
          contact.linkedinUrl,
          contact.phone,
          contact.location,
          contact.industry,
          'linkedin'
        ]);
      }

      await client.query('COMMIT');

      // Update cache
      await this.redis.del('contacts:count');

    } catch (error) {
      await client.query('ROLLBACK');
      throw error;
    } finally {
      client.release();
    }
  }

  async getCampaignMetrics(campaignId, dateRange) {
    const cacheKey = `campaign:${campaignId}:metrics:${dateRange.start}:${dateRange.end}`;

    // Check cache first
    const cached = await this.redis.get(cacheKey);
    if (cached) {
      return JSON.parse(cached);
    }

    const query = `
      SELECT 
        COUNT(*) as total_sent,
        COUNT(CASE WHEN status = 'delivered' THEN 1 END) as delivered,
        COUNT(CASE WHEN status = 'bounced' THEN 1 END) as bounced,
        COUNT(CASE WHEN status = 'failed' THEN 1 END) as failed,
        COUNT(CASE WHEN opened = true THEN 1 END) as opened,
        COUNT(CASE WHEN clicked = true THEN 1 END) as clicked,
        COUNT(CASE WHEN unsubscribed = true THEN 1 END) as unsubscribed,
        AVG(response_time) as avg_response_time
      FROM campaign_activities 
      WHERE campaign_id = $1 
        AND created_at BETWEEN $2 AND $3
    `;

    const result = await this.pool.query(query, [
      campaignId,
      dateRange.start,
      dateRange.end
    ]);

    const metrics = result.rows[0];

    // Cache for 5 minutes
    await this.redis.setex(cacheKey, 300, JSON.stringify(metrics));

    return metrics;
  }

  async getAgentPerformance(agentId, dateRange) {
    const query = `
      SELECT 
        COUNT(*) as total_sessions,
        AVG(session_duration) as avg_session_duration,
        AVG(actions_performed) as avg_actions,
        AVG(typing_speed_wpm) as avg_typing_speed,
        AVG(error_rate) as avg_error_rate,
        COUNT(CASE WHEN session_completed = true THEN 1 END) as completed_sessions
      FROM activity_sessions 
      WHERE agent_id = $1 
        AND session_start BETWEEN $2 AND $3
    `;

    const result = await this.pool.query(query, [
      agentId,
      dateRange.start,
      dateRange.end
    ]);

    return result.rows[0];
  }
}

Reporting SystemΒΆ

class ReportingSystem {
  constructor(databaseManager, analyticsEngine) {
    this.db = databaseManager;
    this.analytics = analyticsEngine;
  }

  async generateCampaignReport(campaignId, options = {}) {
    const {
      format = 'pdf',
      includeCharts = true,
      dateRange = null
    } = options;

    // 1. Get campaign data
    const campaign = await this.db.getCampaign(campaignId);
    const metrics = await this.analytics.calculateCampaignMetrics(campaignId);

    // 2. Generate report data
    const reportData = {
      campaign: campaign,
      metrics: metrics,
      dateRange: dateRange || {
        start: campaign.created_at,
        end: new Date()
      },
      generatedAt: new Date()
    };

    // 3. Generate report based on format
    switch (format) {
      case 'pdf':
        return await this.generatePDFReport(reportData, includeCharts);
      case 'excel':
        return await this.generateExcelReport(reportData);
      case 'csv':
        return await this.generateCSVReport(reportData);
      default:
        throw new Error(`Unsupported format: ${format}`);
    }
  }

  async generatePDFReport(data, includeCharts) {
    const pdf = new PDFDocument();
    const stream = new PassThrough();

    pdf.pipe(stream);

    // Header
    pdf.fontSize(20).text('Campaign Report', 50, 50);
    pdf.fontSize(12).text(`Campaign: ${data.campaign.name}`, 50, 80);
    pdf.text(`Generated: ${data.generatedAt.toLocaleDateString()}`, 50, 100);

    // Metrics section
    pdf.fontSize(16).text('Performance Metrics', 50, 130);

    const metrics = data.metrics;
    let y = 160;

    pdf.fontSize(12);
    pdf.text(`Total Sent: ${metrics.delivery.total}`, 50, y);
    y += 20;
    pdf.text(`Delivery Rate: ${metrics.delivery.deliveryRate.toFixed(2)}%`, 50, y);
    y += 20;
    pdf.text(`Open Rate: ${metrics.engagement.openRate.toFixed(2)}%`, 50, y);
    y += 20;
    pdf.text(`Click Rate: ${metrics.engagement.clickRate.toFixed(2)}%`, 50, y);
    y += 20;
    pdf.text(`Bounce Rate: ${metrics.delivery.bounceRate.toFixed(2)}%`, 50, y);

    // Charts (if requested)
    if (includeCharts) {
      y += 40;
      pdf.text('Performance Charts', 50, y);
      // Add chart generation logic here
    }

    pdf.end();

    return stream;
  }

  async generateExcelReport(data) {
    const workbook = new ExcelJS.Workbook();
    const worksheet = workbook.addWorksheet('Campaign Report');

    // Add headers
    worksheet.addRow(['Metric', 'Value']);

    // Add metrics
    const metrics = data.metrics;
    worksheet.addRow(['Total Sent', metrics.delivery.total]);
    worksheet.addRow(['Delivery Rate', `${metrics.delivery.deliveryRate.toFixed(2)}%`]);
    worksheet.addRow(['Open Rate', `${metrics.engagement.openRate.toFixed(2)}%`]);
    worksheet.addRow(['Click Rate', `${metrics.engagement.clickRate.toFixed(2)}%`]);
    worksheet.addRow(['Bounce Rate', `${metrics.delivery.bounceRate.toFixed(2)}%`]);

    // Add charts
    const chart = worksheet.addChart({
      type: 'column',
      name: 'Campaign Metrics',
      categories: ['Delivery Rate', 'Open Rate', 'Click Rate'],
      values: [
        metrics.delivery.deliveryRate,
        metrics.engagement.openRate,
        metrics.engagement.clickRate
      ]
    });

    chart.setPosition('E2', 'H15');

    return workbook;
  }

  async scheduleReport(reportConfig) {
    const {
      campaignId,
      frequency,
      format,
      recipients,
      time
    } = reportConfig;

    const schedule = {
      id: generateId(),
      campaignId,
      frequency,
      format,
      recipients,
      time,
      nextRun: this.calculateNextRun(frequency, time),
      createdAt: new Date()
    };

    await this.db.storeScheduledReport(schedule);

    // Schedule in job queue
    await this.scheduleJob(schedule);

    return schedule;
  }
}

πŸ“Š Database SchemaΒΆ

Core TablesΒΆ

-- Contacts table
CREATE TABLE contacts (
    id BIGSERIAL PRIMARY KEY,
    email VARCHAR(255) UNIQUE NOT NULL,
    first_name VARCHAR(100),
    last_name VARCHAR(100),
    company VARCHAR(200),
    position VARCHAR(200),
    linkedin_url VARCHAR(500),
    phone VARCHAR(50),
    location VARCHAR(200),
    industry VARCHAR(100),
    source VARCHAR(50),
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- Campaigns table
CREATE TABLE campaigns (
    id BIGSERIAL PRIMARY KEY,
    name VARCHAR(200) NOT NULL,
    description TEXT,
    status VARCHAR(50) DEFAULT 'draft',
    target_audience JSONB,
    content JSONB,
    scheduling JSONB,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- Campaign activities table
CREATE TABLE campaign_activities (
    id BIGSERIAL PRIMARY KEY,
    campaign_id BIGINT REFERENCES campaigns(id),
    contact_id BIGINT REFERENCES contacts(id),
    agent_id BIGINT,
    status VARCHAR(50),
    sent_at TIMESTAMP,
    opened_at TIMESTAMP,
    clicked_at TIMESTAMP,
    bounced_at TIMESTAMP,
    unsubscribed_at TIMESTAMP,
    response_time INTEGER,
    error_message TEXT,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- Analytics cache table
CREATE TABLE analytics_cache (
    id BIGSERIAL PRIMARY KEY,
    cache_key VARCHAR(255) UNIQUE NOT NULL,
    data JSONB NOT NULL,
    expires_at TIMESTAMP NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

IndexesΒΆ

-- Performance indexes
CREATE INDEX idx_campaign_activities_campaign_id ON campaign_activities(campaign_id);
CREATE INDEX idx_campaign_activities_contact_id ON campaign_activities(contact_id);
CREATE INDEX idx_campaign_activities_sent_at ON campaign_activities(sent_at);
CREATE INDEX idx_campaign_activities_status ON campaign_activities(status);
CREATE INDEX idx_contacts_email ON contacts(email);
CREATE INDEX idx_contacts_company ON contacts(company);
CREATE INDEX idx_analytics_cache_key ON analytics_cache(cache_key);
CREATE INDEX idx_analytics_cache_expires ON analytics_cache(expires_at);

πŸ“ˆ Analytics & MetricsΒΆ

Campaign MetricsΒΆ

  • Delivery Metrics: Total sent, delivered, bounced, failed
  • Engagement Metrics: Open rate, click rate, unsubscribe rate
  • Performance Metrics: Response time, error rate, success rate
  • Trend Analysis: Historical performance trends
  • Comparative Analysis: Campaign performance comparison

Agent MetricsΒΆ

  • Session Metrics: Total sessions, completion rate, duration
  • Performance Metrics: Typing speed, error rate, accuracy
  • Behavioral Metrics: Activity patterns, engagement levels
  • Evolution Metrics: Learning and adaptation over time

System MetricsΒΆ

  • Database Performance: Query performance, connection usage
  • API Performance: Response times, error rates
  • Resource Usage: CPU, memory, storage utilization
  • Scalability Metrics: Performance under load

πŸ”§ ConfigurationΒΆ

Database ConfigurationΒΆ

{
  "database": {
    "host": "localhost",
    "port": 5432,
    "database": "email_marketing",
    "username": "postgres",
    "password": "password",
    "pool": {
      "min": 5,
      "max": 20,
      "idleTimeoutMillis": 30000
    }
  }
}

Analytics ConfigurationΒΆ

{
  "analytics": {
    "cache": {
      "enabled": true,
      "ttl": 300,
      "maxSize": 1000
    },
    "reports": {
      "defaultFormat": "pdf",
      "includeCharts": true,
      "scheduledReports": true
    },
    "metrics": {
      "realTime": true,
      "historical": true,
      "trends": true
    }
  }
}

πŸ”’ Security & ComplianceΒΆ

Data ProtectionΒΆ

  • Encryption: Sensitive data encryption at rest and in transit
  • Access Control: Role-based database access
  • Audit Logging: Comprehensive data access logging
  • Data Retention: Configurable retention policies

Privacy ComplianceΒΆ

  • GDPR Compliance: Data protection and privacy controls
  • Data Minimization: Only necessary data collection
  • Right to Deletion: Contact data deletion capabilities
  • Consent Management: Data usage consent tracking

πŸš€ DeploymentΒΆ

Infrastructure RequirementsΒΆ

  • Database: PostgreSQL cluster with read replicas
  • Cache: Redis cluster for analytics caching
  • Storage: Large storage for historical data
  • Compute: High-performance servers for analytics

Scaling StrategyΒΆ

  • Database Scaling: Read replicas and connection pooling
  • Cache Scaling: Redis cluster with sharding
  • Analytics Scaling: Distributed analytics processing
  • Storage Scaling: Data partitioning and archiving