Step 1: Data Generation
Generate 1000-2000 synthetic e-commerce orders across 50 unique products with realistic attributes (price, category, shipping dates, customer emails).
Step 2: Review Generation Nova Lite Nova Pro
• Nova Lite generates 8-40 AI reviews per product in batches
• Reviews include ratings (1-5), sentiment (positive/neutral/negative), and return reasons
• Nova Pro validates 3 random samples per product for quality (target: 70+ score)
• Total: ~1200+ reviews across 50 products
Step 3: Risk Scoring Nova Lite Nova Pro
• Nova Lite analyzes each order in batches of 20
• Considers: price, category, payment method, product history, customer reviews
• Outputs: risk_score (0-100), risk_factors, confidence level
• Nova Pro validates random samples for accuracy
• Result: ~1400 high-risk orders (score > 50) identified
Step 4: Friction Clustering Titan Embeddings Nova Lite
• Titan Embeddings vectorizes negative reviews (1536-dim)
• KMeans clustering groups similar complaints into 3 clusters
• Nova Lite generates human-readable cluster names
• Nova Pro validates cluster quality and distinctness
• Output: Top 3 friction categories per product with percentages
Step 5: Intervention Generation Nova Pro
• Nova Pro analyzes risk score + friction clusters for each high-risk order
• Decides optimal strategy: send_email, offer_discount, priority_support, send_replacement
• Generates personalized email subject + body
• Estimates intervention cost and prevention probability
Step 6: Outcome Simulation
• Simulates customer engagement (email opened/clicked)
• Determines if intervention prevented return (probabilistic)
• Calculates comprehensive savings:
→ Prevented: +$15.93 (logistics) + order price (revenue kept)
→ Returned: -$15.93 (logistics) - order price (refund) - $2.50 (restocking)