Retail & E-commerce
Agentic AI Implementation Guide
Document ID: B7-RETAIL-ECOMMERCE
Version: 1.0
Category: Industry Vertical
Sector Overview
| Characteristic | Description |
|---|---|
| Decision Velocity | Very Fast (real-time pricing, inventory) |
| Transaction Volume | Very High (millions daily) |
| Personalization Need | Critical differentiator |
| Seasonality | Extreme (holidays, sales events) |
| Margin Pressure | High (competitive pricing) |
| Customer Expectations | Instant, omnichannel |
Primary Use Cases
1. Intelligent Product Discovery (GS + LSR)
Application: Conversational shopping assistant
Paradigm: GS (product retrieval) + LSR (recommendations)
Flow:
1. Customer describes need (natural language)
2. GS retrieves matching products from catalog
3. LSR synthesizes personalized recommendations
4. Follow-up questions refine selection
5. Add to cart with confidence
Knowledge Base:
- Product catalog with attributes
- Customer purchase history
- Reviews and ratings
- Inventory status
- Promotion rules
Example Interaction:
Customer: "I need a laptop for my teenager who does
gaming and homework"
Agent: Based on your needs, I recommend:
1. [Product A] - Best balance of gaming/productivity
2. [Product B] - Budget-friendly option
3. [Product C] - Premium performance
Your teen's friend purchased [Product A] last month
and rated it 5 stars. Currently 15% off.
ROI Metrics:
- 25% increase in conversion rate
- 35% increase in average order value
- 50% reduction in product return rate
2. Dynamic Pricing Optimization (EP + VE)
Application: Real-time price optimization
Paradigm: EP (strategy) + VE (execution)
EP Phase - Strategy Development:
- Analyze competitor pricing
- Evaluate inventory levels
- Consider demand signals
- Factor in margin targets
- Test pricing hypotheses
VE Phase - Price Execution:
- Validate against pricing rules
- Check MAP compliance
- Verify margin floors
- Apply approved changes
- Log all decisions
Constraints:
- Minimum margin requirements
- MAP/MSRP compliance
- Geographic restrictions
- Channel consistency
3. Customer Service Automation (GS + EP)
Application: Full-service customer support
Tier 1: GS Agent (80% of inquiries)
- Order status inquiries
- Return policy questions
- Product information
- Basic troubleshooting
Tier 2: EP Agent (15% of inquiries)
- Complex returns/exchanges
- Multi-order issues
- Complaint resolution
- Exception handling
Tier 3: Human Agent (5% of inquiries)
- Escalated complaints
- High-value customers
- Sensitive situations
Automation Targets:
| Inquiry Type | Automation Rate |
|---|---|
| Where's my order? | 95% |
| Return initiation | 85% |
| Product questions | 80% |
| Complaints | 40% |
| Billing disputes | 30% |
4. Inventory Management (EP)
Application: Demand forecasting and replenishment
Paradigm: EP (adaptive planning)
Inputs:
- Historical sales data
- Seasonality patterns
- Promotional calendar
- External signals (weather, events)
- Supplier lead times
Outputs:
- Demand forecasts by SKU/location
- Reorder recommendations
- Allocation optimization
- Stockout risk alerts
Learning Loop:
- Compare forecast to actual
- Identify error patterns
- Adjust models
- Improve over time
5. Personalized Marketing (LSR + GS)
Application: Individualized marketing content
Phase 1 (GS): Customer Understanding
- Purchase history analysis
- Browse behavior patterns
- Segment identification
- Preference inference
Phase 2 (LSR): Content Generation
- Personalized email copy
- Product recommendations
- Promotional messaging
- Subject line variants
Compliance:
- CAN-SPAM requirements
- Unsubscribe handling
- Data privacy (CCPA/GDPR)
Architecture Pattern
Real-Time Commerce Stack
┌─────────────────────────────────────────────┐
│ Customer Touchpoints │
│ (Web, Mobile, In-Store, Social, Chat) │
└─────────────────────┬───────────────────────┘
│
┌─────────────────────▼───────────────────────┐
│ Experience Orchestrator │
│ (Route to appropriate agent/service) │
└─────────────────────┬───────────────────────┘
│
┌─────────────────┼─────────────────┐
▼ ▼ ▼
┌─────────┐ ┌─────────┐ ┌─────────┐
│ Product │ │ Customer│ │ Pricing │
│Discovery│ │ Service │ │ Agent │
│ Agent │ │ Agent │ │ │
└────┬────┘ └────┬────┘ └────┬────┘
│ │ │
┌────▼───────────────▼───────────────▼────┐
│ Commerce Data Layer │
│ (Products, Customers, Orders, Pricing) │
└──────────────────────────────────────────┘
Peak Load Handling
class RetailAgentScaler:
"""Auto-scale agents for peak events"""
def __init__(self):
self.baseline_capacity = 1000 # requests/minute
self.peak_multiplier = 10
def scale_for_event(self, event_type):
multipliers = {
'black_friday': 10,
'cyber_monday': 8,
'prime_day': 6,
'flash_sale': 5,
'normal': 1
}
capacity = self.baseline_capacity * multipliers.get(event_type, 1)
# Use faster/cheaper models during peak
if event_type in ['black_friday', 'cyber_monday']:
self.model_tier = 'haiku' # Speed over quality
else:
self.model_tier = 'sonnet'
ROI Framework
Revenue Impact
| Use Case | Typical Impact |
|---|---|
| Product discovery | +15-25% conversion |
| Personalization | +20-35% AOV |
| Dynamic pricing | +5-15% margin |
| Cart abandonment | +10-20% recovery |
Cost Reduction
| Area | Typical Savings |
|---|---|
| Customer service | 40-60% cost reduction |
| Manual repricing | 80% time savings |
| Returns processing | 30-50% efficiency gain |
Quick ROI Calculation
Monthly Transactions: 1,000,000
Average Order Value: $75
Current Conversion Rate: 2.5%
With Agentic AI:
- Conversion improvement: +20% → 3.0%
- AOV improvement: +15% → $86.25
Monthly Revenue Impact:
Before: 1M × 2.5% × $75 = $1,875,000
After: 1M × 3.0% × $86.25 = $2,587,500
Lift: $712,500/month = $8.55M/year
Implementation Priorities
Phase 1: Quick Wins (Weeks 1-4)
- FAQ automation (GS)
- Order status inquiries (VE)
- Basic product search (GS)
Phase 2: Revenue Drivers (Weeks 5-12)
- Conversational commerce (GS + LSR)
- Personalized recommendations
- Cart abandonment recovery
Phase 3: Optimization (Weeks 13-20)
- Dynamic pricing
- Inventory optimization
- Advanced personalization
Document maintained by CODITECT Retail Practice