E-commerce has become an AI battleground. Companies using AI for personalization, pricing, and inventory management are seeing 20-30% revenue increases while competitors struggle with generic experiences and stockouts.
Personalized Product Recommendations
Impact: 35% of Amazon’s revenue comes from recommendations.
Types of Recommendation Systems:
1. Collaborative Filtering
“Users like you also bought…“
2. Content-Based
“Similar products based on features”
3. Hybrid
Combination of both
Basic Recommendation Engine Implementation
class RecommendationEngine:
def recommend_products(self, user_id, context):
# User profile
user_profile = self.get_user_profile(user_id)
# Collaborative filtering
collaborative_recs = self.collaborative_filter(
user_id,
similar_users_count=100
)
# Content-based filtering
browsing_history = self.get_browsing_history(user_id)
content_recs = self.content_based_filter(browsing_history)
# Context-aware adjustments
if context.season == "winter":
# Boost seasonal items
seasonal_boost = self.apply_seasonal_weights(
collaborative_recs + content_recs
)
# Hybrid ranking
final_recs = self.hybrid_rank(
collaborative_recs,
content_recs,
user_profile,
context
)
return final_recs[:10]
Advanced Real-Time Personalization
# Real-time personalization with session data
def real_time_recommendations(user_session):
# Track real-time behavior
recent_views = user_session.get_recent_views(minutes=30)
cart_items = user_session.cart
# Intent prediction
intent = ml_model.predict_intent(recent_views, cart_items)
if intent == "researching":
# Show comparison and reviews
return get_comparison_products(recent_views)
elif intent == "ready_to_buy":
# Show complementary products
return get_complementary_products(cart_items)
else:
# Show trending in category
return get_trending_products(recent_views[0].category)
Dynamic Pricing
AI adjusts prices in real-time based on demand, competition, and inventory.
Strategy:
class DynamicPricingEngine:
def calculate_optimal_price(self, product_id):
# Get competitor prices
competitor_prices = self.get_competitor_prices(product_id)
# Current demand
demand_score = self.predict_demand(
product_id,
time_of_day=datetime.now().hour,
day_of_week=datetime.now().weekday(),
seasonality=self.get_seasonal_factor()
)
# Inventory level
stock_level = self.get_stock_level(product_id)
# ML pricing model
optimal_price = pricing_model.predict({
"base_price": product.base_price,
"competitor_avg": np.mean(competitor_prices),
"competitor_min": min(competitor_prices),
"demand_score": demand_score,
"stock_level": stock_level,
"margin_target": product.target_margin
})
# Apply business rules
final_price = self.apply_constraints(
optimal_price,
min_price=product.cost * 1.2, # 20% minimum margin
max_price=product.msrp
)
return final_price
Competitor Price Monitoring:
def monitor_competitor_prices(product_name):
# Search for product on competitor sites
search_results = serp_api.search(
query=f"{product_name} buy online",
num=20
)
prices = []
for result in search_results:
if is_ecommerce_site(result.domain):
# Extract price from page
content = reader_api.extract(result.url)
price = extract_price(content)
if price:
prices.append({
"merchant": result.domain,
"price": price,
"url": result.url,
"timestamp": datetime.now()
})
return prices
Learn about building price monitoring systems.
Intelligent Search
AI-powered search understands intent, not just keywords.
Features:
- Semantic search
- Visual search (upload image, find product)
- Voice search
- Auto-correct and suggestions
class IntelligentSearch:
def search(self, query, user_context):
# Query understanding
parsed_query = self.parse_query(query)
# Intent detection
intent = self.detect_intent(parsed_query)
if intent.type == "navigational":
# User wants specific category
return self.get_category_results(intent.category)
elif intent.type == "transactional":
# User ready to buy
results = self.product_search(parsed_query)
return self.rank_by_conversion_probability(results, user_context)
else: # informational
# Show guides, comparisons
return self.content_search(parsed_query)
def semantic_search(self, query):
# Convert query to embedding
query_embedding = embedding_model.encode(query)
# Vector similarity search
similar_products = vector_db.similarity_search(
query_embedding,
k=50
)
# Rerank with business logic
ranked = self.rerank(similar_products, query)
return ranked
Inventory Optimization
AI predicts demand and optimizes stock levels.
class InventoryOptimizer:
def optimize_inventory(self, product_id):
# Demand forecast
forecast = self.forecast_demand(
product_id,
horizon_days=90
)
# External factors
external_data = {
"competitor_stock": self.check_competitor_stock(product_id),
"trending_score": self.get_trend_score(product_id),
"seasonality": self.get_seasonal_factor(product_id),
"promotional_calendar": self.get_upcoming_promotions()
}
# Optimal order quantity
optimal_order = self.calculate_order_quantity(
forecast,
current_stock=self.get_stock_level(product_id),
lead_time=product.supplier_lead_time,
holding_cost=product.holding_cost,
stockout_cost=product.stockout_cost
)
return {
"recommended_order": optimal_order,
"reorder_point": forecast.mean * product.lead_time,
"safety_stock": forecast.std * 1.96 # 95% service level
}
Conversational Commerce
AI chatbots guide shopping journeys.
class ShoppingAssistant:
def handle_conversation(self, user_message, session):
# Intent classification
intent = self.classify_intent(user_message)
if intent == "product_inquiry":
# Extract product details from query
product_info = self.extract_product_requirements(user_message)
# Search and recommend
products = self.search_products(product_info)
return {
"message": f"I found {len(products)} products matching your criteria",
"products": products[:5],
"follow_up": "Would you like me to narrow down the options?"
}
elif intent == "comparison":
# Compare products
products = self.extract_products_to_compare(user_message)
comparison = self.generate_comparison(products)
return {
"message": "Here's how they compare:",
"comparison_table": comparison,
"recommendation": self.recommend_best_fit(products, session.user_profile)
}
elif intent == "order_tracking":
order_id = self.extract_order_id(user_message)
status = self.get_order_status(order_id)
return {
"message": f"Your order is {status.current_stage}",
"expected_delivery": status.estimated_delivery,
"tracking_url": status.tracking_link
}
Visual Search and AR
Upload a photo, find the product.
def visual_search(image):
# Extract features from image
image_embedding = vision_model.encode(image)
# Find similar products
similar_products = vector_db.similarity_search(
image_embedding,
k=20
)
# Rerank by exact match confidence
reranked = visual_reranker.rank(image, similar_products)
return reranked
Augmented Reality:
- Virtual try-on (clothes, makeup)
- Furniture placement (see sofa in your room)
- Size visualization
Customer Lifetime Value Prediction
Identify high-value customers and personalize accordingly.
def predict_customer_ltv(customer_id):
# Historical data
purchase_history = get_purchase_history(customer_id)
engagement = get_engagement_metrics(customer_id)
# Feature engineering
features = {
"recency": days_since_last_purchase(customer_id),
"frequency": len(purchase_history),
"monetary": sum([p.amount for p in purchase_history]),
"avg_order_value": np.mean([p.amount for p in purchase_history]),
"product_diversity": len(set([p.category for p in purchase_history])),
"engagement_score": engagement.email_open_rate * 0.3 +
engagement.site_visits * 0.7
}
# Predict LTV
predicted_ltv = ltv_model.predict(features)
# Segment customer
if predicted_ltv > 5000:
segment = "VIP"
treatment = "white_glove_service"
elif predicted_ltv > 1000:
segment = "High_Value"
treatment = "loyalty_program"
else:
segment = "Standard"
treatment = "retention_campaigns"
return {
"predicted_ltv": predicted_ltv,
"segment": segment,
"recommended_treatment": treatment
}
Fraud Detection
Protect against payment fraud and account takeovers.
class EcommerceFraudDetector:
def evaluate_order(self, order):
# Behavioral signals
signals = {
"velocity": self.check_order_velocity(order.user_id),
"device_match": self.check_device_history(order.device_id),
"shipping_address": self.check_address_legitimacy(order.shipping),
"payment_method": self.check_payment_risk(order.payment)
}
# ML fraud score
fraud_score = fraud_model.predict(signals)
if fraud_score > 0.8:
return {"action": "BLOCK", "reason": "High fraud risk"}
elif fraud_score > 0.5:
return {"action": "MANUAL_REVIEW"}
else:
return {"action": "APPROVE"}
Email Marketing Optimization
AI personalizes email campaigns.
def optimize_email_campaign(recipient_list):
personalized_emails = []
for recipient in recipient_list:
# Predict best send time
send_time = predict_optimal_send_time(recipient.id)
# Predict best subject line
subject_variants = generate_subject_lines(recipient.segment)
best_subject = predict_best_subject(subject_variants, recipient.profile)
# Predict best products to feature
products = recommend_products(recipient.id)
# Generate personalized content
email = {
"to": recipient.email,
"subject": best_subject,
"send_time": send_time,
"products": products,
"discount": calculate_optimal_discount(recipient.ltv)
}
personalized_emails.append(email)
return personalized_emails
Conversion Rate Optimization
AI tests and optimizes every element.
A/B Testing Automation:
class AutoABTest:
def run_experiment(self, page_element):
# Generate variants
variants = self.generate_variants(page_element)
# Multi-armed bandit algorithm
while not self.has_statistical_significance():
# Allocate traffic based on performance
traffic_allocation = self.calculate_allocation(variants)
# Serve variants
self.serve_variants(variants, traffic_allocation)
# Update performance metrics
self.update_metrics()
# Declare winner
winner = self.select_winner(variants)
self.deploy_winner(winner)
Supply Chain Optimization
AI predicts delays and optimizes logistics.
def optimize_delivery_route(orders):
# Cluster orders by location
clusters = clustering_algorithm.cluster(orders)
# Route optimization for each cluster
optimized_routes = []
for cluster in clusters:
route = vehicle_routing_solver.solve(
orders=cluster,
constraints={
"max_capacity": 100,
"max_duration": 8 * 60, # 8 hours
"traffic_data": get_real_time_traffic()
}
)
optimized_routes.append(route)
return optimized_routes
Performance Metrics
| Metric | Before AI | After AI | Improvement |
|---|---|---|---|
| Conversion rate | 2.3% | 3.8% | +65% |
| Average order value | $75 | $95 | +27% |
| Cart abandonment | 69% | 52% | -25% |
| Customer satisfaction | 78% | 91% | +17% |
| Inventory turnover | 8x/year | 12x/year | +50% |
Implementation Roadmap
Month 1-2: Product recommendations Month 3-4: Dynamic pricing Month 5-6: Intelligent search Month 7-9: Conversational commerce Month 10-12: Full personalization
Tech Stack:
- Recommendations: TensorFlow, PyTorch
- Search: Elasticsearch + embeddings
- Pricing: Custom ML models
- Data: SERP API for competitor monitoring
AI in e-commerce is no longer optional—it’s table stakes. Companies that master AI personalization will dominate their markets.
Related Resources
E-commerce AI:
Infrastructure:
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