Overview
Automated email marketing system with behavior-based follow-ups, A/B testing, and personalization at scale. Integrates with CRM systems and provides detailed analytics on campaign performance.
System Architecture
Component Overview
┌─────────────────────────────────────────────┐
│ Contact Management Layer │
│ CRM Integration • List Segmentation │
└─────────────┬───────────────────────────────┘
│
┌─────────────▼───────────────────────────────┐
│ Campaign Orchestration Engine │
│ Workflow Builder • Trigger Management │
└─────────────┬───────────────────────────────┘
│
┌─────────────▼───────────────────────────────┐
│ Personalization & Content Engine │
│ Dynamic Content • Template Management │
└─────────────┬───────────────────────────────┘
│
┌─────────────▼───────────────────────────────┐
│ Analytics & Optimization │
│ Tracking • A/B Testing • Reporting │
└─────────────────────────────────────────────┘
Core Features
1. Behavior-Based Automation
class BehaviorTriggerEngine:
def __init__(self):
self.triggers = {}
self.event_queue = Queue()
def register_trigger(self, event_type, conditions, action):
"""
Register behavior-based email triggers
"""
self.triggers[event_type] = {
'conditions': conditions,
'action': action,
'cooldown': 24 # hours
}
def process_event(self, event):
"""
Process user behavior events
"""
if event.type in self.triggers:
trigger = self.triggers[event.type]
if self.evaluate_conditions(event, trigger['conditions']):
self.execute_action(trigger['action'], event.user)
def evaluate_conditions(self, event, conditions):
"""
Complex condition evaluation
"""
# Time-based conditions
# Engagement score thresholds
# Segment membership
# Previous action history
return all(self.check_condition(event, c) for c in conditions)
2. Dynamic Personalization
class PersonalizationEngine:
def __init__(self):
self.user_profiles = {}
self.content_variants = {}
def personalize_email(self, template, user_data):
"""
Dynamic content personalization
"""
personalized = template
# Merge tags replacement
for field, value in user_data.items():
personalized = personalized.replace(f'{{{field}}}', str(value))
# Dynamic content blocks
personalized = self.insert_dynamic_content(personalized, user_data)
# Personalized recommendations
if '{{recommendations}}' in personalized:
recs = self.generate_recommendations(user_data)
personalized = personalized.replace('{{recommendations}}', recs)
return personalized
def generate_recommendations(self, user_data):
"""
ML-based content recommendations
"""
# Collaborative filtering
# Content-based filtering
# Hybrid approach
return recommended_items
3. A/B Testing Framework
class ABTestingEngine:
def __init__(self):
self.active_tests = {}
self.results = {}
def create_test(self, test_config):
"""
Set up A/B test
"""
test = {
'id': generate_test_id(),
'variants': test_config['variants'],
'sample_size': test_config['sample_size'],
'success_metric': test_config['metric'],
'statistical_significance': 0.95
}
self.active_tests[test['id']] = test
return test['id']
def assign_variant(self, user_id, test_id):
"""
Random variant assignment with consistent hashing
"""
hash_value = hashlib.md5(f"{user_id}{test_id}".encode()).hexdigest()
variant_index = int(hash_value, 16) % len(self.active_tests[test_id]['variants'])
return self.active_tests[test_id]['variants'][variant_index]
def calculate_winner(self, test_id):
"""
Statistical significance testing
"""
from scipy.stats import chi2_contingency
data = self.results[test_id]
chi2, p_value, dof, expected = chi2_contingency(data)
if p_value < 0.05:
return self.identify_winner(data)
return None # No significant winner yet
Integration Layer
CRM Synchronization
class CRMIntegration:
def __init__(self, crm_type):
self.connector = self.setup_connector(crm_type)
self.sync_interval = 300 # seconds
def sync_contacts(self):
"""
Bidirectional contact sync
"""
# Pull new contacts from CRM
new_contacts = self.connector.get_new_contacts()
# Push email engagement data to CRM
engagement_data = self.get_engagement_metrics()
self.connector.update_contacts(engagement_data)
# Handle conflicts
self.resolve_conflicts()
def setup_webhooks(self):
"""
Real-time event synchronization
"""
webhooks = [
'contact.created',
'contact.updated',
'deal.closed',
'task.completed'
]
for event in webhooks:
self.connector.register_webhook(event, self.handle_webhook)
Email Service Provider Integration
- SendGrid API integration
- Mailchimp automation
- BombBomb video email
- Custom SMTP support
Analytics Pipeline
Data Collection
class AnalyticsCollector:
def __init__(self):
self.events = []
self.redis_client = redis.Redis()
def track_event(self, event_type, data):
"""
Real-time event tracking
"""
event = {
'type': event_type,
'timestamp': datetime.now(),
'data': data,
'session_id': data.get('session_id'),
'user_id': data.get('user_id')
}
# Real-time processing
self.redis_client.xadd('email_events', event)
# Batch storage
self.events.append(event)
if len(self.events) >= 1000:
self.flush_to_database()
- Open rates by segment
- Click-through rate optimization
- Conversion tracking
- Revenue attribution
- Engagement scoring
Technology Stack
Backend:
- Python (Django/FastAPI)
- Celery (task queue)
- PostgreSQL (transactional data)
- ClickHouse (analytics)
Message Queue:
- RabbitMQ/Kafka (event streaming)
- Redis (caching, rate limiting)
Email Infrastructure:
- MJML (responsive templates)
- Litmus (email testing)
- SpamAssassin (deliverability)
Monitoring:
- Prometheus (metrics)
- Grafana (dashboards)
- Sentry (error tracking)
Architecture Decisions
# Distributed task processing
class EmailDispatcher:
def __init__(self):
self.celery_app = Celery('email_dispatcher')
self.rate_limiter = RateLimiter()
@celery_app.task(rate_limit='1000/m')
def send_email_batch(self, batch):
"""
Rate-limited batch sending
"""
for email in batch:
if self.rate_limiter.allow(email['provider']):
send_email.delay(email)
else:
# Queue for retry
schedule_retry(email)
- Batch processing for large campaigns
- Connection pooling for SMTP
- Template caching
- CDN for email assets
Results & Impact
- Throughput: 100,000+ emails/hour
- Deliverability: 98% inbox rate
- Latency: <500ms trigger to send
- Uptime: 99.9% availability
Business Metrics
- 40% improvement in open rates
- 25% increase in click-through rates
- 3x faster campaign deployment
- 50% reduction in manual work
Key Features
Advanced Segmentation:
- Behavioral cohorts
- Predictive segments
- Dynamic list building
- RFM analysis
Workflow Automation:
- Visual workflow builder
- Conditional branching
- Time delays and scheduling
- Multi-channel orchestration
Compliance & Deliverability:
- GDPR compliance tools
- CAN-SPAM management
- Bounce handling
- Reputation monitoring
Specific client details and proprietary algorithms have been omitted.