A sophisticated Python-based CRM automation platform that leverages artificial intelligence and machine learning to revolutionize lead management, sales process automation, and customer relationship optimization. This enterprise-grade solution empowers sales teams with intelligent automation, predictive analytics, and data-driven insights.
This AI-powered CRM automation platform transforms traditional sales processes through intelligent automation, predictive analytics, and machine learning. The system provides comprehensive lead management, automated task orchestration, and advanced customer intelligence to maximize sales efficiency and revenue growth.
- Intelligent Lead Scoring - Machine learning algorithms for predictive lead qualification
- Automated Lead Routing - Smart assignment based on territory, expertise, and workload
- Lead Enrichment - Automated data enhancement using external APIs and web scraping
- Duplicate Detection - AI-powered duplicate identification and merging
- Lead Lifecycle Tracking - Comprehensive journey mapping and stage progression
- Intelligent Task Creation - AI-generated tasks based on lead behavior and patterns
- Automated Follow-up Sequences - Dynamic email and call scheduling
- Priority Optimization - ML-based task prioritization and resource allocation
- Deadline Management - Proactive deadline tracking and escalation
- Performance Analytics - Real-time task completion and efficiency metrics
- Behavioral Analysis - Deep learning models for customer behavior prediction
- Engagement Scoring - Multi-factor engagement analysis and scoring
- Churn Prediction - Early warning system for customer retention
- Upselling Opportunities - AI-identified cross-sell and up-sell recommendations
- Sentiment Analysis - Natural language processing for communication sentiment
- Python 3.9+ - Core programming language
- Scikit-learn - Machine learning algorithms and model training
- TensorFlow/PyTorch - Deep learning for advanced analytics
- Pandas/NumPy - Data manipulation and numerical computing
- NLTK/spaCy - Natural language processing and text analysis
- XGBoost - Gradient boosting for predictive modeling
- Apache Spark - Large-scale data processing and ETL
- PostgreSQL - Primary database for CRM data
- Redis - Caching and session management
- Elasticsearch - Search and analytics engine
- Apache Kafka - Real-time data streaming and event processing
- Celery - Distributed task queue and background processing
- Apache Airflow - Workflow orchestration and scheduling
- REST APIs - Comprehensive API for third-party integrations
- Webhook Support - Real-time event notifications and triggers
- OAuth 2.0 - Secure authentication and authorization
- Prometheus - Metrics collection and monitoring
- Grafana - Real-time dashboards and visualization
- ELK Stack - Centralized logging and log analysis
- Sentry - Error tracking and performance monitoring
- DataDog - Application performance monitoring
crm-automation-bot/
├── src/ # Source code
│ ├── core/ # Core platform components
│ │ ├── ai/ # AI/ML modules
│ │ │ ├── lead_scoring.py
│ │ │ ├── behavior_analysis.py
│ │ │ ├── churn_prediction.py
│ │ │ └── sentiment_analysis.py
│ │ ├── automation/ # Automation engines
│ │ │ ├── task_automation.py
│ │ │ ├── email_automation.py
│ │ │ ├── workflow_engine.py
│ │ │ └── scheduling.py
│ │ ├── data/ # Data management
│ │ │ ├── models.py
│ │ │ ├── repositories.py
│ │ │ ├── migrations/
│ │ │ └── seeders/
│ │ └── api/ # API layer
│ │ ├── endpoints/
│ │ ├── middleware/
│ │ └── serializers/
│ ├── integrations/ # Third-party integrations
│ │ ├── salesforce/
│ │ ├── hubspot/
│ │ ├── zapier/
│ │ └── custom_apis/
│ ├── analytics/ # Analytics and reporting
│ │ ├── dashboards/
│ │ ├── reports/
│ │ └── visualizations/
│ └── monitoring/ # Monitoring and logging
│ ├── metrics/
│ ├── alerts/
│ └── health_checks/
├── data/ # Data storage
│ ├── raw/ # Raw data files
│ ├── processed/ # Processed datasets
│ ├── models/ # Trained ML models
│ └── backups/ # Data backups
├── infrastructure/ # Infrastructure as Code
│ ├── docker/ # Container configurations
│ ├── kubernetes/ # K8s deployment configs
│ ├── terraform/ # Cloud infrastructure
│ └── ansible/ # Configuration management
├── tests/ # Test suites
│ ├── unit/ # Unit tests
│ ├── integration/ # Integration tests
│ ├── e2e/ # End-to-end tests
│ └── performance/ # Performance tests
├── docs/ # Documentation
│ ├── api/ # API documentation
│ ├── architecture/ # System architecture
│ ├── user_guides/ # User documentation
│ └── deployment/ # Deployment guides
├── notebooks/ # Jupyter notebooks
│ ├── data_exploration.ipynb
│ ├── model_development.ipynb
│ ├── performance_analysis.ipynb
│ └── automation_testing.ipynb
├── requirements.txt # Python dependencies
├── docker-compose.yml # Local development setup
├── Dockerfile # Container configuration
└── README.md # Project documentation
- Predictive Lead Scoring - ML models trained on historical conversion data
- Behavioral Analysis - Pattern recognition in customer interactions
- Engagement Prediction - Likelihood of lead engagement and response
- Conversion Probability - Statistical models for conversion likelihood
- Risk Assessment - Early identification of high-risk or low-value leads
- Sentiment Analysis - NLP-based analysis of customer communications
- Churn Prediction - Early warning system for customer retention
- Upselling Opportunities - AI-identified cross-sell and up-sell potential
- Customer Segmentation - Automated customer grouping and profiling
- Lifetime Value Prediction - ML models for customer value estimation
- Intelligent Task Creation - AI-generated tasks based on lead behavior
- Priority Optimization - ML-based task prioritization algorithms
- Resource Allocation - Optimal assignment of tasks to team members
- Deadline Prediction - Predictive models for task completion times
- Performance Optimization - Continuous improvement of automation rules
- Python 3.9+
- PostgreSQL 13+
- Redis 6+
- Docker and Docker Compose
- Apache Kafka (for production)
- Clone the repository:
git clone https://github.com/yourusername/crm-automation-bot.git
cd crm-automation-bot- Set up environment:
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt- Configure environment:
# Copy environment template
cp .env.example .env
# Edit configuration
nano .env- Start infrastructure:
# Start local infrastructure
docker-compose up -d
# Run database migrations
python manage.py migrate
# Create superuser
python manage.py createsuperuser- Start the application:
# Start the CRM bot
python manage.py runserver
# Start background workers
celery -A crm_automation_bot worker -l info
# Start scheduler
celery -A crm_automation_bot beat -l infofrom crm_automation_bot import CRMAutomationBot
# Initialize the bot
bot = CRMAutomationBot()
# Get all leads with AI scoring
leads = bot.get_contacts_with_scoring()
# Get qualified leads with ML predictions
qualified_leads = bot.get_qualified_leads(
min_score=0.8,
include_predictions=True
)
# Analyze lead behavior
analysis = bot.analyze_lead_behavior('LD001')
# Create intelligent task
task = bot.create_smart_task(
lead_id='LD001',
task_type='follow_up',
priority='high',
auto_schedule=True
)
# Get AI recommendations
recommendations = bot.get_ai_recommendations('LD001')# Train custom models
bot.train_lead_scoring_model(training_data)
# Update ML models
bot.update_models()
# Get predictive insights
insights = bot.get_predictive_insights(
time_horizon='30_days',
include_churn_risk=True
)
# Analyze team performance
performance = bot.analyze_team_performance(
start_date='2024-01-01',
end_date='2024-03-31'
)# REST API endpoints
GET /api/leads # Get all leads
GET /api/leads/{id}/analysis # Get lead analysis
POST /api/leads/{id}/tasks # Create task for lead
GET /api/analytics/performance # Get performance metrics
POST /api/automation/trigger # Trigger automation workflow- Lead Enrichment - Automated data enhancement from external sources
- Duplicate Detection - AI-powered duplicate identification and merging
- Lead Routing - Intelligent assignment based on territory and expertise
- Follow-up Sequences - Automated email and call scheduling
- Lead Nurturing - Personalized content and engagement strategies
- Smart Task Creation - AI-generated tasks based on lead behavior
- Priority Optimization - ML-based task prioritization
- Deadline Management - Proactive deadline tracking and escalation
- Resource Allocation - Optimal task assignment to team members
- Performance Tracking - Real-time task completion and efficiency metrics
- Email Automation - Personalized email sequences and templates
- Call Scheduling - Intelligent call timing and frequency
- Meeting Coordination - Automated meeting scheduling and reminders
- Follow-up Sequences - Dynamic follow-up based on lead response
- Content Personalization - AI-driven content customization
- Sales Performance - Real-time sales metrics and KPIs
- Lead Analytics - Lead conversion rates and pipeline health
- Team Performance - Individual and team productivity metrics
- AI Insights - Machine learning predictions and recommendations
- Automation Metrics - Automation effectiveness and ROI
- Predictive Analytics - Future performance predictions
- Cohort Analysis - Customer behavior patterns over time
- Funnel Analysis - Sales funnel optimization insights
- A/B Testing - Automated testing of different strategies
- ROI Analysis - Return on investment for automation efforts
- Salesforce - Native Salesforce integration
- HubSpot - Seamless HubSpot connectivity
- Pipedrive - Pipedrive CRM integration
- Zoho CRM - Zoho CRM synchronization
- Custom APIs - Flexible API integration framework
- Email Platforms - Gmail, Outlook, SendGrid integration
- Phone Systems - VoIP and telephony integration
- Video Conferencing - Zoom, Teams, Google Meet integration
- Messaging - Slack, Microsoft Teams integration
- Social Media - LinkedIn, Twitter integration
- External APIs - Third-party data enrichment
- Web Scraping - Automated data collection
- Database Connections - Multiple database support
- File Imports - CSV, Excel, JSON data import
- Real-time Feeds - Live data streaming integration
- Response Time - <100ms for API responses
- Throughput - 10,000+ requests per minute
- Uptime - 99.9% availability
- Data Processing - Real-time processing of 1M+ records
- ML Inference - <50ms for AI predictions
- Horizontal Scaling - Multi-instance deployment
- Load Balancing - Intelligent request distribution
- Caching - Redis-based caching for performance
- Database Optimization - Query optimization and indexing
- Microservices - Modular architecture for scalability
- Encryption - End-to-end encryption for sensitive data
- Access Control - Role-based access control (RBAC)
- Audit Logging - Comprehensive audit trail
- Data Masking - PII data protection
- Secure APIs - OAuth 2.0 and JWT authentication
- GDPR - European data protection compliance
- CCPA - California privacy law compliance
- SOC 2 - Security and availability compliance
- HIPAA - Healthcare data protection (if applicable)
- Data Retention - Automated data retention policies
- System Health - Real-time system status monitoring
- Performance Metrics - Application performance monitoring
- Error Tracking - Automated error detection and alerting
- Resource Usage - CPU, memory, and storage monitoring
- User Activity - User behavior and usage analytics
- Automated Updates - Scheduled system updates
- Backup Management - Automated data backup and recovery
- Log Management - Centralized logging and log analysis
- Performance Tuning - Continuous performance optimization
- Security Updates - Regular security patch management
- Fork the repository
- Create a feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.