This submission presents enterprise-grade solutions for intelligent campaign orchestration and multi-touch attribution, leveraging modern streaming architecture with Apache Kafka, Flink, and managed cloud services. The approach emphasizes rapid POC validation followed by systematic scaling to handle enterprise volumes while maintaining cost efficiency.
NOTE: People Cost will depend upon the geography and where the team is located
Event-Driven Real-Time Architecture:
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Data Sources │───▶│ Kafka Streams │───▶│ Flink Processing│
│ (Web, Email, │ │ (Event Backbone) │ │ (ML Inference) │
│ SMS, Social) │ └──────────────────┘ └─────────────────┘
└─────────────────┘ │ │
│ ▼
┌─────────────────┐ │ ┌─────────────────┐
│ Campaign Exec │◀─────────────┼─────────────│ Redis Feature │
│ (SendGrid, FB, │ │ │ Store │
│ Google Ads) │ │ └─────────────────┘
└─────────────────┘ │ │
▼ ▼
┌──────────────────────────────────────────┐
│ Airflow Orchestration │
│ (Model Training & Batch Jobs) │
└──────────────────────────────────────────┘
Technology Stack:
- Event Streaming: Confluent Cloud (managed Kafka) for 10M+ events/day
- Real-time Processing: Apache Flink on Kubernetes for <50ms ML inference
- Feature Store: Redis Enterprise with Kafka Connect for real-time features
- ML Platform: MLflow + Kubernetes for model lifecycle management
- Orchestration: Apache Airflow for batch processing and retraining
- API Gateway: Kong with rate limiting for external integrations
Integration Strategy:
- Webhook Collectors: Capture events from SendGrid, Facebook, Google Ads
- JavaScript SDK: Client-side behavioral tracking with privacy compliance
- API Connectors: Bidirectional sync with existing MarTech stack
- Campaign Triggers: Real-time decision API for channel selection and timing
- Customer Engagement Rate: Increase CTR from 2% to 7% within 6 months
- Churn Reduction: Decrease promotional email churn from 40% to 15%
- Revenue Per Campaign: Achieve 35% increase in campaign-attributed revenue through optimized timing and channel selection
Core Team (5 people):
- Lead Data Engineer (Kafka/Flink expertise): $120K annually
- ML Engineer (Real-time inference, A/B testing): $110K annually
- Backend Engineer (API development, integrations): $100K annually
- DevOps Engineer (Kubernetes, monitoring): $105K annually
- Marketing Technologist (Campaign strategy, tool integration): $95K annually
Supporting Roles:
- Data Scientist (Model development): 50% allocation
- Product Manager (Requirements, roadmap): 25% allocation
Scope: Email-only optimization with basic ML model
- Kafka setup for email events: 2 weeks
- Simple Flink job for timing prediction: 2 weeks
- Basic integration with SendGrid: 2 weeks
- Target: 20% CTR improvement on test segment (1K customers)
Scope: Multi-channel orchestration with advanced ML
- SMS and social media integration: 4 weeks
- Real-time ML inference pipeline: 4 weeks
- Campaign conflict resolution: 2 weeks
- Target: Handle 100K events/day, 50 concurrent campaigns
Scope: Production-grade system with full automation
- Advanced ML models (deep learning): 6 weeks
- Multi-region deployment: 4 weeks
- Full MarTech stack integration: 6 weeks
- Target: 10M+ events/day, 99.9% uptime, auto-scaling
Total Investment: $365K over 32 weeks Annual Operating Cost: $180K (infrastructure + licenses)
Real-Time Attribution Processing:
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Multi-Touch │───▶│ Kafka Topics │───▶│ Flink Journey │
│ Data Collection │ │ (Web, Email, │ │ Stitching │
│ │ │ Ad, Social) │ │ │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│ │
│ ▼
┌─────────────────┐ │ ┌─────────────────┐
│ Druid OLAP │◀────────────┼─────────────│ Attribution │
│ (Real-time │ │ │ Engine (Flink) │
│ Queries) │ │ │ │
└─────────────────┘ │ └─────────────────┘
│ │
▼ ▼
┌──────────────────────────────────────────┐
│ Spark Batch Processing │
│ (Complex Shapley Value Calculations) │
└──────────────────────────────────────────┘
Technology Stack:
- Data Collection: Custom JavaScript SDK + server-side APIs
- Event Streaming: Shared Kafka infrastructure with Case 1
- Identity Resolution: Flink CEP for real-time customer journey stitching
- Attribution Processing: Apache Flink for streaming attribution + Spark for batch
- Analytics Database: Apache Druid for sub-second query performance
- Visualization: Apache Superset with custom attribution dashboards
Attribution Model Design:
- Algorithmic Attribution: Shapley value-based with incremental updates
- Channel Interaction Effects: Cross-channel influence modeling
- Time Decay Functions: Configurable attribution windows (1-90 days)
- Custom Business Rules: Industry-specific attribution logic
- Query Performance: <200ms response time for attribution dashboards at 95th percentile
- Model Accuracy: 95%+ correlation with holdout conversion tests
- Business Impact: 25% improvement in marketing ROI through optimized budget allocation
Core Team (5 people):
- Senior Data Engineer (Flink/Druid optimization): $125K annually
- Data Scientist (Attribution modeling, statistics): $115K annually
- Analytics Engineer (Dashboard development, query optimization): $105K annually
- Full-Stack Developer (API development, UI/UX): $100K annually
- Data Governance Lead (Privacy, compliance, quality): $110K annually
Scope: Basic last-click vs. multi-touch comparison
- Data collection setup: 3 weeks
- Simple attribution algorithm: 3 weeks
- Basic dashboard: 2 weeks
- Target: Attribution accuracy >85% vs. ground truth
Scope: Real-time attribution with basic Shapley values
- Flink streaming attribution: 6 weeks
- Druid setup and optimization: 4 weeks
- Advanced dashboards: 2 weeks
- Target: Handle 1M touchpoints/day, <500ms query response
Scope: Advanced attribution with optimization recommendations
- Complex interaction modeling: 8 weeks
- Budget optimization algorithms: 6 weeks
- ML-driven attribution insights: 4 weeks
- Target: 50M+ touchpoints/day, real-time budget recommendations
Total Investment: $410K over 38 weeks Annual Operating Cost: $165K (infrastructure + licenses)
Shared Components (30% cost reduction):
- Common Kafka infrastructure
- Unified Kubernetes cluster
- Shared monitoring and alerting (Prometheus/Grafana)
- Common CI/CD pipelines and security frameworks
- Technical Risks: POC validation before major investments
- Integration Risks: Parallel development with existing system compatibility
- Scalability Risks: Cloud-native architecture with auto-scaling
- Business Risks: Phased rollout with clear success metrics
Case 1 Success: 250% CTR improvement + 62% churn reduction Case 2 Success: <200ms query performance + 25% ROI improvement Combined ROI: 300%+ within 18 months based on revenue impact
- Combined Development: $630K (vs. $775K separate)
- Annual Operating: $280K for both systems
- Expected Annual Value: $2M+ in improved marketing efficiency