AI-Powered Marketing Funnel Automation Workflow
An end-to-end AI workflow that automates marketing campaigns, predicts user intent, and optimizes conversions.
🎯 Project Overview
Built a fully automated AI marketing workflow that predicts customer intent, personalizes content with LLMs, and optimizes campaigns using reinforcement learning. Integrated with HubSpot and Meta Ads for real-time execution, delivering 28-60% conversion boosts and 35% reduction in ad spend waste.
💼 Business Impact
- 28-60% boost in conversions
- 35% reduction in ad spend waste
- 2× higher engagement from personalized campaigns
- Automated workflows save 20+ hours/week
- Predictive targeting identifies high-intent customers
🛠️ Technical Architecture
Workflow Components
1. User Segmentation
Clustering customers based on behavior patterns: purchase history, website interactions, email engagement, social media activity. K-means and hierarchical clustering identify distinct customer segments.
2. Predictive Intent Modeling
ML models predict which users are about to buy, churn, or engage. Classification models (XGBoost, Random Forest) trained on historical behavior patterns. Real-time scoring updates as users interact.
3. Content Personalization
LLM (GPT-4) generates personalized emails, ad copy, and landing page text based on user segment and intent score. A/B testing framework validates generated content effectiveness.
4. Campaign Optimization
Multi-armed bandit algorithm selects best variant (ad copy, landing page, offer) in real-time. Explores new variants while exploiting winning ones. Continuously adapts to changing user preferences.
5. Performance Tracking
Real-time metrics: click-through rates, conversion rates, churn scores, customer lifetime value predictions. Google Analytics 4 integration for comprehensive tracking. Custom dashboards for campaign performance.
6. Automation
Automated email campaigns triggered by user behavior. Retargeting ads on Meta/Facebook. Push notifications via mobile apps. All orchestrated through HubSpot workflows and Meta Ads API.
Core Technologies
- Python - ML pipeline and automation logic
- Scikit-Learn - Clustering and classification models
- LangChain - LLM orchestration for content generation
- HubSpot API - CRM integration and workflow automation
- Meta Ads API - Facebook/Instagram ad management
- TensorFlow - Deep learning for intent prediction
- GA4 - Analytics and conversion tracking
🔧 Technical Challenges Solved
Challenge 1: Real-Time Personalization at Scale
Problem: Generating personalized content for thousands of users in real-time is computationally expensive.
Solution: Pre-generate content templates for each segment, use LLM for final personalization only when needed. Cache generated content and reuse for similar user profiles.
Challenge 2: Multi-Armed Bandit Cold Start
Problem: New campaign variants have no data, making it hard to choose which to test first.
Solution: Use Thompson Sampling with prior distributions based on historical similar campaigns. Start with small budgets for exploration, then scale winners.
Challenge 3: Attribution and ROI Tracking
Problem: Users interact with multiple touchpoints before converting—hard to attribute credit.
Solution: Multi-touch attribution model using Markov chains. Tracks user journey across email, ads, website, and assigns fractional credit to each touchpoint.
📊 Performance Metrics
💡 Key Features
- Intent prediction: Identify customers ready to buy before they know it
- LLM content generation: Personalized emails, ads, landing pages
- Multi-armed bandit: Automatically finds best campaign variants
- HubSpot integration: Seamless CRM workflow automation
- Meta Ads optimization: Real-time ad budget allocation
🚀 Results
- ✅ 28-60% increase in conversion rates across campaigns
- ✅ 35% reduction in wasted ad spend
- ✅ 2× higher engagement from personalized content
- ✅ 20+ hours/week saved through automation
- ✅ ROI improvement of 40%+ on marketing spend