Martech Integration

This cookbook shows how a martech company can integrate MDB into their existing pixel infrastructure with multi-tenancy support and scalable, production-ready serverless architecture.

This guide shows examples using both the legacy pixel format and the new v1 pixel format. For new integrations, use the v1 Pixel format: https://p.mdb.tools/v1/PIXEL_ID

Martech Multi-Tenant Integration

This example shows how a martech company can integrate MDB into their existing pixel infrastructure with multi-tenancy support and scalable serverless processing.

Scenario

You’re a martech product with:

Existing pixels deployed on multiple customer sites
Multi-tenant architecture with multiple clients
Need to attribute website visitors to your own system
Requirement for scalable, serverless processing

Architecture Overview

Step 1: Modify Your Existing Pixel

Update your existing pixel to load the MDB pixel with tenant identification:

Self-Executing Function Example

// Your existing pixel code
(function() {
    // Your existing pixel logic here
    var tenantId = 'client_abc123';
    var pageId = 'homepage_v2';
    var orgId = 'your_org_id_from_mdb_dashboard';
    
    // Load MDB pixel with tenant attribution
    var mdbOptions = {
        tenant_id: tenantId,
        page_id: pageId,
        source: 'your_pixel_v1',
        timestamp: Date.now(),
        // Add any other attribution data you need
        campaign: getCampaignData(), // Your existing function
        user_segment: getUserSegment() // Your existing function
    };
    
    // Create MDB pixel script
    var script = document.createElement('script');
    
    // For v1 pixel (recommended):
    // script.src = 'https://p.mdb.tools/v1/' + pixelId + '?options=' + 
    //              encodeURIComponent(JSON.stringify(mdbOptions));
    
    // For legacy pixel:
    script.src = 'https://p.mdb.tools/' + orgId + '?options=' + 
                 encodeURIComponent(JSON.stringify(mdbOptions));
    script.async = true;
    
    // Load MDB pixel
    document.head.appendChild(script);
    
    // Continue with your existing pixel logic
    // ... rest of your pixel code
})();

Multi-Tenant Configuration

// Advanced multi-tenant setup
function loadMDBPixel(config) {
    var mdbOptions = {
        // Core tenant identification
        tenant_id: config.tenantId,
        client_name: config.clientName,
        
        // Page and context attribution  
        page_id: config.pageId,
        page_type: config.pageType,
        
        // Campaign attribution from your system
        campaign_id: config.campaignId,
        campaign_name: config.campaignName,
        
        // Your pixel metadata
        pixel_version: '2.1.0',
        integration_type: 'martech_wrapper',
        
        // Custom attribution
        custom_attributes: config.customAttributes || {}
    };
    
    var script = document.createElement('script');
    
    // For v1 pixel (recommended):
    // script.src = 'https://p.mdb.tools/v1/' + config.mdbPixelId + 
    //              '?options=' + encodeURIComponent(JSON.stringify(mdbOptions));
    
    // For legacy pixel:
    script.src = 'https://p.mdb.tools/' + config.mdbOrgId + 
                 '?options=' + encodeURIComponent(JSON.stringify(mdbOptions));
    script.async = true;
    document.head.appendChild(script);
}

// Usage in your pixel
loadMDBPixel({
    tenantId: 'client_abc123',
    clientName: 'AcmeCorp',
    pageId: 'product_page_electronics',
    pageType: 'product',
    campaignId: 'summer2024_electronics',
    campaignName: 'Summer Electronics Sale',
    mdbOrgId: 'your_mdb_org_id',
    customAttributes: {
        product_category: 'electronics',
        price_range: 'premium'
    }
});

Step 2: Scalable Serverless Webhook Handler

Create a serverless function that can handle high-volume webhook traffic:

AWS Lambda Example

// AWS Lambda handler
const AWS = require('aws-sdk');
const sqs = new AWS.SQS();

exports.handler = async (event) => {
    try {
        // Parse webhook payload
        const webhook = JSON.parse(event.body);
        const { version, options, data } = webhook;
        
        // Validate webhook
        if (version !== 'v1') {
            return {
                statusCode: 400,
                body: JSON.stringify({ error: 'Unsupported version' })
            };
        }
        
        // Extract tenant information
        const tenantId = options.tenant_id;
        const pageId = options.page_id;
        
        if (!tenantId) {
            return {
                statusCode: 400,
                body: JSON.stringify({ error: 'Missing tenant_id' })
            };
        }
        
        // Enrich with metadata
        const enrichedEvent = {
            id: generateEventId(),
            timestamp: Date.now(),
            tenant_id: tenantId,
            page_id: pageId,
            mdb_data: data,
            pixel_options: options,
            processing_metadata: {
                webhook_received_at: new Date().toISOString(),
                lambda_request_id: event.requestContext.requestId
            }
        };
        
        // Send to SQS for processing
        await sqs.sendMessage({
            QueueUrl: process.env.PROCESSING_QUEUE_URL,
            MessageBody: JSON.stringify(enrichedEvent),
            MessageAttributes: {
                'tenant_id': {
                    DataType: 'String',
                    StringValue: tenantId
                },
                'event_type': {
                    DataType: 'String', 
                    StringValue: 'visitor_identified'
                }
            }
        }).promise();
        
        return {
            statusCode: 200,
            body: JSON.stringify({ success: true, event_id: enrichedEvent.id })
        };
        
    } catch (error) {
        console.error('Webhook processing error:', error);
        
        return {
            statusCode: 500,
            body: JSON.stringify({ error: 'Processing failed' })
        };
    }
};

function generateEventId() {
    return 'evt_' + Date.now() + '_' + Math.random().toString(36).substr(2, 9);
}

Firebase Functions Example

// Firebase Cloud Functions
const functions = require('firebase-functions');
const { PubSub } = require('@google-cloud/pubsub');

const pubsub = new PubSub();

exports.mdbWebhook = functions.https.onRequest(async (req, res) => {
    try {
        if (req.method !== 'POST') {
            return res.status(405).json({ error: 'Method not allowed' });
        }
        
        const { version, options, data } = req.body;
        
        // Validate webhook
        if (version !== 'v1') {
            return res.status(400).json({ error: 'Unsupported version' });
        }
        
        const tenantId = options.tenant_id;
        if (!tenantId) {
            return res.status(400).json({ error: 'Missing tenant_id' });
        }
        
        // Create processing event
        const processingEvent = {
            id: generateEventId(),
            timestamp: Date.now(),
            tenant_id: tenantId,
            page_id: options.page_id,
            mdb_data: data,
            pixel_options: options,
            processing_metadata: {
                webhook_received_at: new Date().toISOString(),
                function_execution_id: req.get('Function-Execution-Id')
            }
        };
        
        // Publish to Pub/Sub
        await pubsub
            .topic('visitor-identification-events')
            .publishMessage({
                data: Buffer.from(JSON.stringify(processingEvent)),
                attributes: {
                    tenant_id: tenantId,
                    event_type: 'visitor_identified'
                }
            });
        
        res.status(200).json({ success: true, event_id: processingEvent.id });
        
    } catch (error) {
        console.error('Webhook processing error:', error);
        res.status(500).json({ error: 'Processing failed' });
    }
});

Step 3: Queue-Based Event Processing

Process events from your queue system for scalability:

AWS SQS + Lambda Processor

// SQS event processor Lambda
exports.processVisitorEvents = async (event) => {
    const promises = event.Records.map(async (record) => {
        try {
            const eventData = JSON.parse(record.body);
            await processVisitorIdentification(eventData);
        } catch (error) {
            console.error('Event processing failed:', error);
            throw error; // This will send message to DLQ
        }
    });
    
    await Promise.all(promises);
};

async function processVisitorIdentification(eventData) {
    const { tenant_id, mdb_data, pixel_options } = eventData;
    
    // Look up tenant configuration
    const tenantConfig = await getTenantConfig(tenant_id);
    
    // Process the identified visitor data
    const visitorProfile = {
        tenant_id: tenant_id,
        visitor_data: mdb_data,
        attribution: {
            page_id: pixel_options.page_id,
            campaign_id: pixel_options.campaign_id,
            source: pixel_options.source
        },
        processed_at: new Date().toISOString()
    };
    
    // Store in your database
    await storeVisitorProfile(visitorProfile, tenantConfig);
    
    // Trigger any downstream processes
    await triggerTenantWebhooks(tenant_id, visitorProfile);
    await updateAnalytics(tenant_id, visitorProfile);
}

Pub/Sub Processing Example

// Pub/Sub consumer for event processing
const { PubSub } = require('@google-cloud/pubsub');

const pubsub = new PubSub({
    projectId: process.env.GCP_PROJECT_ID
});

const subscription = pubsub.subscription('visitor-identification-subscription');

async function startProcessing() {
    subscription.on('message', async (message) => {
        try {
            const eventData = JSON.parse(message.data.toString());
            await processVisitorEvent(eventData);
            message.ack(); // Acknowledge the message
        } catch (error) {
            console.error('Processing failed:', error);
            message.nack(); // Negative acknowledgment - retry
        }
    });
}

async function processVisitorEvent(eventData) {
    const { tenant_id, mdb_data, pixel_options } = eventData;
    
    // Multi-tenant processing logic
    const processor = await getTenantProcessor(tenant_id);
    await processor.handleVisitorIdentification(mdb_data, pixel_options);
}

Step 4: Database Schema for Multi-Tenancy

Design your database schema to handle multi-tenant visitor data:

PostgreSQL Schema Example

-- Tenants table
CREATE TABLE tenants (
    id VARCHAR(255) PRIMARY KEY,
    name VARCHAR(255) NOT NULL,
    mdb_org_id VARCHAR(255) NOT NULL,
    webhook_config JSONB,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- Visitor identifications table
CREATE TABLE visitor_identifications (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    tenant_id VARCHAR(255) REFERENCES tenants(id),
    visitor_data JSONB NOT NULL,
    pixel_options JSONB NOT NULL,
    page_id VARCHAR(255),
    campaign_id VARCHAR(255),
    identified_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    processed_at TIMESTAMP
);

-- Indexes for performance
CREATE INDEX idx_visitor_identifications_tenant_id ON visitor_identifications(tenant_id);
CREATE INDEX idx_visitor_identifications_identified_at ON visitor_identifications(identified_at);
CREATE INDEX idx_visitor_identifications_page_id ON visitor_identifications(page_id);

Step 5: Monitoring and Observability

Implement comprehensive monitoring for your integration:

CloudWatch/Application Insights Metrics

// Custom metrics tracking
const AWS = require('aws-sdk');
const cloudwatch = new AWS.CloudWatch();

async function trackMetrics(tenantId, eventType, success) {
    await cloudwatch.putMetricData({
        Namespace: 'MDB/Integration',
        MetricData: [
            {
                MetricName: 'WebhookProcessed',
                Dimensions: [
                    { Name: 'TenantId', Value: tenantId },
                    { Name: 'EventType', Value: eventType },
                    { Name: 'Success', Value: success.toString() }
                ],
                Value: 1,
                Unit: 'Count'
            }
        ]
    }).promise();
}

Best Practices Summary

Scalability

Use serverless functions for webhook handling
Implement queue-based processing for high volume
Design for horizontal scaling with proper partitioning
Use appropriate database indexes for query performance

Multi-Tenancy

Include tenant_id in all pixel options
Isolate tenant data at the database level
Implement tenant-specific configuration
Monitor per-tenant usage and performance

Reliability

Implement proper error handling and retries
Use dead letter queues for failed processing
Design idempotent processing logic
Monitor webhook delivery success rates

Security

Validate all webhook payloads
Use HTTPS for all endpoints
Implement proper authentication
Sanitize and validate tenant data

Next Steps

Start with a single tenant - Implement and test with one client first
Monitor performance - Track latency, throughput, and error rates
Scale gradually - Add tenants and monitor system performance
Optimize based on usage - Adjust queue sizes, function timeouts, and database queries based on real usage patterns

Quickstart (v1)

Quickstart (Legacy)

Additional Resources

Integration Examples

Martech Integration

Martech Integration

Martech Multi-Tenant Integration

Scenario

Architecture Overview

Step 1: Modify Your Existing Pixel

Self-Executing Function Example

Multi-Tenant Configuration

Step 2: Scalable Serverless Webhook Handler

AWS Lambda Example

Firebase Functions Example

Step 3: Queue-Based Event Processing

AWS SQS + Lambda Processor

Pub/Sub Processing Example

Step 4: Database Schema for Multi-Tenancy

PostgreSQL Schema Example

Step 5: Monitoring and Observability

CloudWatch/Application Insights Metrics

Best Practices Summary

Next Steps

Quickstart (v1)

Quickstart (Legacy)

Additional Resources

Integration Examples

​Martech Integration

​Martech Multi-Tenant Integration

​Scenario

​Architecture Overview

​Step 1: Modify Your Existing Pixel

​Self-Executing Function Example

​Multi-Tenant Configuration

​Step 2: Scalable Serverless Webhook Handler

​AWS Lambda Example

​Firebase Functions Example

​Step 3: Queue-Based Event Processing

​AWS SQS + Lambda Processor

​Pub/Sub Processing Example

​Step 4: Database Schema for Multi-Tenancy

​PostgreSQL Schema Example

​Step 5: Monitoring and Observability

​CloudWatch/Application Insights Metrics

​Best Practices Summary

​Next Steps

Martech Integration

Martech Multi-Tenant Integration

Scenario

Architecture Overview

Step 1: Modify Your Existing Pixel

Self-Executing Function Example

Multi-Tenant Configuration

Step 2: Scalable Serverless Webhook Handler

AWS Lambda Example

Firebase Functions Example

Step 3: Queue-Based Event Processing

AWS SQS + Lambda Processor

Pub/Sub Processing Example

Step 4: Database Schema for Multi-Tenancy

PostgreSQL Schema Example

Step 5: Monitoring and Observability

CloudWatch/Application Insights Metrics

Best Practices Summary

Next Steps