Marketing Automation MCP Server

# Marketing Automation MCP - DoorDash Demo ## 🚀 Quick Demo Setup This demo showcases how Marketing Automation MCP can transform DoorDash's marketing operations with AI-powered optimization. ### Prerequisites - Docker and Docker Compose installed - 5 minutes to run the demo - Web browser for viewing results ### 1-Minute Quick Start ```bash # Clone the repository git clone https://github.com/your-org/marketing-automation-mcp.git cd marketing-automation-mcp # Run the demo ./deploy.sh demo start ``` That's it! The demo will: 1. ✅ Connect to simulated marketing accounts 2. 📊 Analyze campaign performance 3. 🤖 Generate AI optimizations 4. 💰 Show projected ROI improvements 5. 📈 Display savings dashboard ## 📑 Demo Components ### 1. Live Demo Script (`demo.py`) Runs a complete marketing automation workflow: - Connects to Google Ads & Facebook Ads (simulated) - Identifies underperforming campaigns - Generates optimization recommendations - Projects ROI improvements - Calculates time/cost savings ### 2. Interactive Presentation (`doordash_demo_deck.html`) Professional slide deck showing: - Current campaign performance - AI-powered recommendations - Projected business impact - Operational efficiency gains - ROI summary ### 3. Real-Time Dashboard (`http://localhost:8080`) Live metrics dashboard displaying: - Time saved through automation - Cost savings - ROI improvements - Campaign performance - Automation timeline ## 🎯 Key Demo Highlights ### For DoorDash's Marketing Team **Current Challenges:** - Managing 100+ campaigns across platforms - Manual budget optimization taking 40+ hours/week - Delayed response to performance changes - Inconsistent optimization decisions **Our Solution:** - **99.5% time reduction** in routine tasks - **28% average ROI improvement** - **Real-time optimization** across all campaigns - **$438,750 annual benefit** (projected) ### Technical Excellence - **MCP Protocol**: Industry-standard AI integration - **Multi-Platform**: Google Ads, Facebook Ads, Analytics - **AI-Powered**: OpenAI GPT-4 for intelligent decisions - **Scalable**: Docker-based microservices architecture - **Secure**: OAuth2, encrypted credentials, audit logs ## 📊 Demo Results ### Campaign Optimization - **Search Campaign**: +25% conversions with dayparting - **Video Campaign**: Save $21,000/month by reallocation - **Overall ROI**: 46.6% → 75.2% improvement ### Operational Efficiency - **Weekly Time Saved**: 39.6 hours - **Annual Cost Saved**: $154,000 in labor - **FTE Equivalent**: 1.0 full-time employee - **Automation ROI**: 857% first-year return ## 🛠️ Technical Architecture ``` ┌─────────────────────────┐ │ MCP Client (Claude) │ └───────────┬─────────────┘ │ MCP Protocol ┌───────────┴─────────────┐ │ Marketing Automation │ │ MCP Server │ ├─────────────────────────┤ │ • Report Generation │ │ • Budget Optimization │ │ • Copy Creation │ │ • Audience Analysis │ └───────────┬─────────────┘ │ ┌───────┴───────┐ │ Integrations │ ├───────────────┤ │ • Google Ads │ │ • Facebook │ │ • Analytics │ └───────────────┘ ``` ## 🚦 Running Different Demo Modes ### Quick Demo (5 minutes) ```bash ./deploy.sh demo start ``` ### Full Development Environment ```bash ./deploy.sh dev start # Includes Jupyter notebook at http://localhost:8888 ``` ### Production Deployment ```bash ./deploy.sh prod start ``` ## 📈 Customizing for Your Interview ### Modify Campaign Data Edit `demo.py` line 24-75 to use your specific examples: ```python self.sample_campaigns = [ { "campaign_id": "your_campaign", "name": "Your Campaign Name", "roi": 150.0, # Your metrics ... } ] ``` ### Adjust Projections Edit `demo.py` line 224-240 to match your targets: ```python projected_revenue = current_total_revenue * 1.28 # Your improvement ``` ### Brand Customization The presentation deck uses DoorDash colors. To change: 1. Edit `demo.py` line 520 for primary color 2. Update logo/branding in presentation HTML ## 🎬 Demo Script for Interview ### Opening (1 minute) "I'd like to show you how we can transform DoorDash's marketing operations with AI-powered automation. This system integrates with your existing platforms and delivers immediate ROI." ### Problem Statement (1 minute) "Currently, your team spends 40+ hours weekly on routine optimization tasks. Campaigns underperform due to delayed reactions, and manual processes limit scale." ### Live Demo (3 minutes) 1. Run `./deploy.sh demo start` 2. Show real-time optimization process 3. Highlight AI recommendations 4. Display projected improvements ### Business Impact (2 minutes) "This delivers $438,750 in annual benefits through: - 28% ROI improvement - 39.6 hours saved weekly - Consistent optimization at scale" ### Technical Discussion (3 minutes) - MCP protocol for AI integration - Multi-platform architecture - Security and compliance - Deployment options ### Next Steps (1 minute) "We can start with a pilot on your search campaigns, showing results within 2 weeks. Full deployment takes 30 days with immediate ROI." ## 🤝 Support For demo issues or customization: - Check logs: `docker-compose logs` - Reset demo: `./deploy.sh demo stop && ./deploy.sh demo start` - Full documentation: See `/docs` directory ## 🎯 Interview Tips 1. **Practice the demo flow** - Run it 2-3 times before the interview 2. **Prepare for questions** about scale, security, and integration 3. **Have backup slides** in case of technical issues 4. **Emphasize ROI** - DoorDash cares about measurable impact 5. **Show confidence** in the technical implementation Good luck with your interview! 🚀