Advanced Price Intelligence Strategies for 2026

class IntelligentPriceAnalyzer:
    """
    Automatically analyze price data and generate insights
    No human bottleneck - scales to millions of price points
    """
    
    def analyze_competitive_position(self, price_data):
        insights = {
            'opportunities': [],
            'threats': [],
            'recommendations': []
        }
        
        # Automatic opportunity detection
        for product in price_data:
            # Opportunity: We're significantly cheaper
            if product['our_price'] < product['competitor_avg'] * 0.85:
                insights['opportunities'].append({
                    'type': 'price_increase_opportunity',
                    'product': product['name'],
                    'current_price': product['our_price'],
                    'recommended_price': product['competitor_avg'] * 0.95,
                    'expected_margin_gain': self.calculate_margin_gain(product),
                    'risk_level': 'low'
                })
            
            # Threat: Competitor undercutting significantly
            if product['competitor_min'] < product['our_price'] * 0.90:
                insights['threats'].append({
                    'type': 'competitive_undercut',
                    'product': product['name'],
                    'our_price': product['our_price'],
                    'competitor_price': product['competitor_min'],
                    'recommended_action': 'match_or_differentiate',
                    'urgency': 'high'
                })
        
        return insights

from scrapegraph_py import ScrapeGraphAI
from datetime import datetime, timedelta
import statistics
import time
 
class AdvancedPriceIntelligence:
    """
    Complete price intelligence system with collection,
    analysis, and actionable recommendations
    """
    
    def __init__(self, api_key, competitors):
        self.sgai = ScrapeGraphAI(api_key=api_key)
        self.competitors = competitors
        self.price_history = {}
        self.alerts = []
        
    def collect_comprehensive_pricing(self, competitor_url):
        """
        Collect not just prices, but complete pricing intelligence
        """
        
        # Define comprehensive extraction prompt
        prompt = """
        Extract complete pricing intelligence:
        
        PRICING:
        - Product name and SKU
        - Current price
        - Original price (if on sale)
        - Discount percentage and promotion details
        - Price per unit (if applicable)
        
        AVAILABILITY:
        - In stock / out of stock status
        - Stock level indicators (low stock warnings, etc.)
        - Estimated delivery time
        - Shipping cost
        - Available sizes/variants
        
        VALUE SIGNALS:
        - Customer rating (average score)
        - Number of reviews
        - Best seller badges or rankings
        - Recently reduced badges
        - LIMITED TIME OFFER! indicators
        
        BUNDLE & PROMOTIONS:
        - Bundle deals (buy X get Y)
        - Volume discounts
        - Loyalty program benefits
        - Promo codes mentioned
        - Free shipping thresholds
        
        COMPETITIVE POSITIONING:
        - Brand positioning language
        - Quality indicators
        - Warranty information
        - Return policy highlights
        """
        
        try:
            response = self.sgai.extract(
                url=competitor_url,
                prompt=prompt
            )
            
            return {
                'competitor': competitor_url,
                'data': response,
                'timestamp': datetime.now().isoformat(),
                'status': 'success'
            }
            
        except Exception as e:
            return {
                'competitor': competitor_url,
                'error': str(e),
                'timestamp': datetime.now().isoformat(),
                'status': 'failed'
            }
    
    def collect_all_competitors(self):
        """
        Collect pricing data from all competitors
        """
        print(f"📊 Collecting pricing intelligence from {len(self.competitors)}
            competitors...")
        
        all_data = []
        
        for competitor in self.competitors:
            print(f"   → {competitor['name']}...", end=" ")
            data = self.collect_comprehensive_pricing(competitor['url'])
            all_data.append(data)
            print("✓")
        
        return all_data
    
    def analyze_competitive_position(self, collected_data):
        """
        Analyze your position vs competitors across all dimensions
        """
        analysis = {
            'timestamp': datetime.now().isoformat(),
            'opportunities': [],
            'threats': [],
            'insights': [],
            'market_position': {}
        }
        
        # Extract and compare prices
        competitor_prices = self.extract_all_prices(collected_data)
        
        for product, competitors_data in competitor_prices.items():
            if len(competitors_data) < 2:
                continue
            
            prices = [c['price'] for c in competitors_data]
            avg_price = statistics.mean(prices)
            min_price = min(prices)
            max_price = max(prices)
            
            # Find your price (assuming first competitor is you)
            your_price = competitors_data[0].get('price')
            
            if your_price:
                # Calculate position
                position_percentile = self.calculate_position_percentile(
                    your_price, prices
                )
                
                # Identify opportunities
                if your_price < avg_price * 0.90:
                    # You're significantly cheaper - opportunity to raise
                    potential_increase = avg_price * 0.95
                    margin_gain = (potential_increase - your_price) *
                        self.estimate_volume(product)
                    
                    analysis['opportunities'].append({
                        'type': 'price_increase',
                        'product': product,
                        'current_price': your_price,
                        'recommended_price': round(potential_increase, 2),
                        'market_avg': round(avg_price, 2),
                        'expected_margin_gain': round(margin_gain, 2),
                        'confidence': 'high',
                        'risk': 'low'
                    })
                
                elif your_price > min_price * 1.15:
                    # Competitor significantly undercutting - threat
                    undercutter = min(competitors_data, key=lambda x: x['price'])
                    
                    analysis['threats'].append({
                        'type': 'competitive_undercut',
                        'product': product,
                        'your_price': your_price,
                        'competitor': undercutter['competitor'],
                        'competitor_price': undercutter['price'],
                        'price_gap': round(your_price - undercutter['price'], 2),
                        'recommended_action': self.recommend_response(
                            product, your_price, undercutter
                        ),
                        'urgency': 'high'
                    })
                
                # Track market position
                analysis['market_position'][product] = {
                    'percentile': position_percentile,
                    'vs_average': round((your_price / avg_price - 1) * 100, 1),
                    'vs_cheapest': round((your_price / min_price - 1) * 100, 1),
                    'vs_most_expensive': round((your_price / max_price - 1) * 100, 1)
                }
        
        return analysis
    
    def detect_pricing_patterns(self, historical_data):
        """
        Identify patterns that predict future competitor moves
        """
        patterns = {
            'promotional_calendar': [],
            'inventory_signals': [],
            'pricing_rhythms': [],
            'predictions': []
        }
        
        # Analyze promotional patterns
        for competitor in self.competitors:
            promo_pattern = self.analyze_promotional_timing(
                competitor['name'],
                historical_data
            )
            
            if promo_pattern:
                patterns['promotional_calendar'].append(promo_pattern)
                
                # Predict next promotion
                if promo_pattern['frequency'] == 'weekly':
                    next_promo = promo_pattern['last_date'] + timedelta(days=7)
                    patterns['predictions'].append({
                        'competitor': competitor['name'],
                        'predicted_event': 'promotional_sale',
                        'predicted_date': next_promo,
                        'confidence': 0.78,
                        'recommended_prep': 'Secure inventory, prepare counter-offer'
                    })
        
        # Detect inventory signals
        inventory_signals = self.detect_inventory_signals(historical_data)
        patterns['inventory_signals'] = inventory_signals
        
        # Predict price movements based on inventory
        for signal in inventory_signals:
            if signal['status'] == 'low_stock':
                patterns['predictions'].append({
                    'competitor': signal['competitor'],
                    'product': signal['product'],
                    'predicted_event': 'price_increase',
                    'predicted_timeframe': '1-2 weeks',
                    'confidence': 0.82,
                    'recommended_action': 'Consider selective price increase'
                })
        
        return patterns
    
    def generate_dynamic_recommendations(self, analysis, patterns):
        """
        Generate specific, actionable recommendations
        """
        recommendations = {
            'immediate_actions': [],
            'strategic_moves': [],
            'monitoring_priorities': []
        }
        
        # Immediate actions from threats
        for threat in analysis['threats']:
            if threat['urgency'] == 'high':
                recommendations['immediate_actions'].append({
                    'priority': 'critical',
                    'action': f"Respond to {threat['competitor']} undercut",
                    'product': threat['product'],
                    'details': threat['recommended_action'],
                    'timeline': 'within 4 hours',
                    'expected_impact': 'Prevent customer switching'
                })
        
        # Strategic opportunities
        for opp in analysis['opportunities']:
            if opp['expected_margin_gain'] > 1000:
                recommendations['strategic_moves'].append({
                    'priority': 'high',
                    'action': 'Optimize pricing for margin expansion',
                    'product': opp['product'],
                    'details': f"Increase to ${opp['recommended_price']}",
                    'expected_gain': f"${opp['expected_margin_gain']}",
                    'timeline': 'within 24 hours'
                })
        
        # Predictions requiring monitoring
        for prediction in patterns['predictions']:
            recommendations['monitoring_priorities'].append({
                'what_to_watch': prediction['predicted_event'],
                'competitor': prediction['competitor'],
                'timeframe': prediction.get('predicted_timeframe', 'TBD'),
                'prep_action': prediction.get('recommended_prep', ''),
                'confidence': prediction['confidence']
            })
        
        return recommendations
    
    def run_continuous_intelligence(self, interval_minutes=15):
        """
        Run continuous price intelligence system
        """
        print("🚀 Advanced Price Intelligence System Started")
        print(f"🎯 Monitoring: {len(self.competitors)} competitors")
        print(f"⏱️  Update Frequency: Every {interval_minutes} minutes")
        print(f"🤖 Automated Analysis: Enabled")
        print("-" * 70)
        
        cycle = 0
        
        while True:
            cycle += 1
            cycle_start = datetime.now()
            
            print(f"\n{'='*70}")
            print(f"🔄 Intelligence Cycle #{cycle} - {cycle_start.strftime('%Y-%m-%d
                %H:%M:%S')}")
            print(f"{'='*70}")
            
            try:
                # Step 1: Collect data
                print("\n📥 COLLECTION PHASE")
                collected_data = self.collect_all_competitors()
                successful = sum(1 for d in collected_data if d['status'] == 'success')
                print(f"✓ Successfully collected from {successful}/{len(collected_data)}
                     sources")
                
                # Step 2: Analyze
                print("\n🧠 ANALYSIS PHASE")
                analysis = self.analyze_competitive_position(collected_data)
                print(f"✓ Identified {len(analysis['opportunities'])} opportunities")
                print(f"✓ Detected {len(analysis['threats'])} threats")
                
                # Step 3: Pattern detection
                print("\n🔮 PATTERN RECOGNITION")
                patterns = self.detect_pricing_patterns(self.price_history)
                print(f"✓ Generated {len(patterns['predictions'])} predictions")
                
                # Step 4: Recommendations
                print("\n💡 RECOMMENDATIONS")
                recommendations = self.generate_dynamic_recommendations(
                    analysis, patterns
                )
                
                # Display critical alerts
                if recommendations['immediate_actions']:
                    print("\n⚠️  CRITICAL ACTIONS REQUIRED:")
                    for action in recommendations['immediate_actions'][:5]:
                        print(f"   🔴 {action['action']}")
                        print(f"      Product: {action['product']}")
                        print(f"      Timeline: {action['timeline']}")
                        print()
                
                # Display opportunities
                if recommendations['strategic_moves']:
                    print("💰 MARGIN OPPORTUNITIES:")
                    for opp in recommendations['strategic_moves'][:5]:
                        print(f"   💵 {opp['action']}")
                        print(f"      Expected Gain: {opp['expected_gain']}")
                        print()
                
                # Store historical data
                self.price_history[datetime.now().isoformat()] = {
                    'collected': collected_data,
                    'analysis': analysis,
                    'patterns': patterns,
                    'recommendations': recommendations
                }
                
                # In production: Push to dashboard, trigger alerts, etc.
                self.push_to_systems(analysis, recommendations)
                
                cycle_duration = (datetime.now() - cycle_start).total_seconds()
                print(f"\n✓ Cycle completed in {cycle_duration:.1f} seconds")
                print(f"💤 Next cycle in {interval_minutes} minutes...")
                
            except Exception as e:
                print(f"\n✗ Error in intelligence cycle: {e}")
            
            # Wait for next cycle
            time.sleep(interval_minutes * 60)
    
    def extract_all_prices(self, collected_data):
        """Extract and organize all prices by product"""
        # Implementation would parse the collected data
        # and organize by product for comparison
        pass
    
    def calculate_position_percentile(self, your_price, all_prices):
        """Calculate what percentile your price is in"""
        sorted_prices = sorted(all_prices)
        position = sorted_prices.index(your_price)
        return (position / len(sorted_prices)) * 100
    
    def estimate_volume(self, product):
        """Estimate sales volume for margin calculations"""
        # Would use historical sales data
        return 100  # Placeholder
    
    def recommend_response(self, product, your_price, undercutter):
        """Generate specific response recommendation"""
        price_gap = your_price - undercutter['price']
        gap_percentage = (price_gap / your_price) * 100
        
        if gap_percentage > 20:
            return "URGENT: Match price or emphasize differentiation"
        elif gap_percentage > 10:
            return "Consider selective price reduction or bundle offer"
        else:
            return "Monitor closely, maintain current positioning"
    
    def analyze_promotional_timing(self, competitor, historical_data):
        """Analyze when competitor typically runs promotions"""
        # Would analyze historical promotion patterns
        pass
    
    def detect_inventory_signals(self, historical_data):
        """Detect inventory-related pricing signals"""
        # Would track stock level changes over time
        pass
    
    def push_to_systems(self, analysis, recommendations):
        """Push intelligence to dashboards, alerts, pricing systems"""
        # Integration with your business systems
        pass
 
# Deploy the system
if __name__ == "__main__":
    # Configure your competitors
    competitors = [
        {'name': 'Competitor A', 'url': 'https://competitor-a.com/products'},
        {'name': 'Competitor B', 'url': 'https://competitor-b.com/shop'},
        {'name': 'Competitor C', 'url': 'https://competitor-c.com/catalog'}
    ]
    
    # Initialize the system
    intelligence = AdvancedPriceIntelligence(
        api_key="your-scrapegraphai-api-key",
        competitors=competitors
    )
    
    # Start continuous intelligence gathering
    intelligence.run_continuous_intelligence(interval_minutes=15)
 

class PredictivePricingIntelligence:
    """
    Predict competitor price changes before they happen
    """
    
    def __init__(self, client):
        self.client = client
        self.prediction_models = {}
    
    def collect_predictive_signals(self, competitor_url):
        """
        Collect signals that predict future price changes
        """
        
        # Signal 1: Inventory levels
        inventory_prompt = """
        Extract inventory signals:
        - Product availability (in stock, low stock, out of stock)
        - Stock level indicators (e.g., "Only 3 left")
        - Restocking dates or backorder information
        - New arrival indicators
        - Clearance or discontinuation signals
        """
        
        # Signal 2: Supplier/Cost indicators
        cost_prompt = """
        Extract cost-related signals:
        - Shipping cost changes
        - Supplier mentions or partnerships
        - Import/tariff mentions
        - Raw material cost references
        - Currency or inflation mentions
        """
        
        # Signal 3: Competitive pressure
        market_prompt = """
        Extract competitive pressure signals:
        - Price match guarantees
        - Competitive comparison claims
        - Market positioning changes
        - New competitor mentions
        - Market share claims
        """
        
        # Collect all signals
        signals = {
            'inventory': self.sgai.extract(
                url=competitor_url,
                prompt=inventory_prompt
            ),
            'costs': self.sgai.extract(
                url=f"{competitor_url}/about",
                prompt=cost_prompt
            ),
            'market': self.sgai.extract(
                url=competitor_url,
                prompt=market_prompt
            )
        }
        
        return signals
    
    def predict_price_movements(self, signals, historical_patterns):
        """
        Predict likely price changes in next 1-4 weeks
        """
        predictions = []
        
        # Low inventory + high demand = price increase likely
        if signals['inventory'].get('low_stock_count', 0) > 10:
            predictions.append({
                'direction': 'increase',
                'magnitude': '5-15%',
                'timeframe': '1-2 weeks',
                'confidence': 0.82,
                'reasoning': 'Low inventory on high-demand items',
                'recommended_action': 'Maintain or increase prices on these items'
            })
        
        # Cost pressure signals = price increase coming
        if 'cost increase' in str(signals['costs']).lower():
            predictions.append({
                'direction': 'increase',
                'magnitude': '3-8%',
                'timeframe': '2-4 weeks',
                'confidence': 0.76,
                'reasoning': 'Supplier cost pressure mentioned',
                'recommended_action': 'Prepare for market-wide price increases'
            })
        
        # Promotional patterns
        if self.is_promotion_season(historical_patterns):
            predictions.append({
                'direction': 'decrease',
                'magnitude': '10-30% (promotional)',
                'timeframe': '1 week',
                'confidence': 0.91,
                'reasoning': 'Historical promotional pattern',
                'recommended_action': 'Prepare counter-promotional strategy'
            })
        
        return predictions

def calculate_true_competitive_price(product_data):
    """
    Calculate the TRUE price customers pay, not just list price
    """
    
    total_cost = {
        'base_price': product_data['price'],
        'shipping': product_data.get('shipping_cost', 0),
        'tax_estimate': product_data['price'] * 0.08,  # Varies by location
        'loyalty_discount': -product_data.get('loyalty_discount', 0),
        'promo_code_value': -product_data.get('promo_value', 0),
        'bundle_savings': -product_data.get('bundle_discount', 0),
        'financing_cost': product_data.get('financing_interest', 0)
    }
    
    # Calculate effective price
    effective_price = sum(total_cost.values())
    
    # Factor in value elements
    value_adjustments = {
        'warranty_value': product_data.get('warranty_years', 0) * 20,
        'return_policy_value': 15 if product_data.get('free_returns') else 0,
        'delivery_speed_value': 25 if product_data.get('fast_shipping') else 0,
        'quality_premium': (product_data.get('rating', 3) - 3) * 10
    }
    
    # Value-adjusted competitive price
    value_adjusted_price = effective_price - sum(value_adjustments.values())
    
    return {
        'list_price': product_data['price'],
        'effective_price': effective_price,
        'value_adjusted_price': value_adjusted_price,
        'cost_breakdown': total_cost,
        'value_factors': value_adjustments
    }

class AutomatedPricingEngine:
    """
    Automatically optimize prices based on competitive intelligence
    """
    
    def __init__(self, rules, authority_limits):
        self.rules = rules
        self.authority_limits = authority_limits
        self.pending_approvals = []
    
    def evaluate_pricing_rules(self, product, competitive_intel):
        """
        Apply automated pricing rules
        """
        
        recommendations = []
        
        # Rule 1: Maintain position in market
        if competitive_intel['position_percentile'] < 40:
            # We've become too cheap
            new_price = competitive_intel['market_avg'] * 0.95
            if new_price - product['current_price'] <
                self.authority_limits['max_increase']:
                recommendations.append({
                    'action': 'increase_price',
                    'new_price': new_price,
                    'approval': 'auto',
                    'reason': 'Maintaining 45th percentile market position'
                })
        
        # Rule 2: Respond to competitive threats
        if competitive_intel.get('undercut_amount', 0) > product['current_price'] *
            0.15:
            # Competitor undercutting by >15%
            match_price = competitive_intel['competitor_price'] * 1.02
            recommendations.append({
                'action': 'match_competitor',
                'new_price': match_price,
                'approval': 'required',  # Needs human approval for large drops
                'reason': f"Competitor undercutting by
                    {competitive_intel['undercut_amount']:.0%}"
            })
        
        # Rule 3: Optimize for margin
        if product.get('slow_moving') and competitive_intel['position_percentile'] > 70:
            # We're expensive and product is slow-moving
            new_price = competitive_intel['market_avg'] * 1.05
            recommendations.append({
                'action': 'optimize_margin',
                'new_price': new_price,
                'approval': 'auto',
                'reason': 'Improve competitiveness on slow-moving inventory'
            })
        
        return recommendations
    
    def execute_approved_changes(self, recommendations):
        """
        Execute pricing changes that meet automation criteria
        """
        executed = []
        
        for rec in recommendations:
            if rec['approval'] == 'auto':
                # Execute automatically
                success = self.update_price_in_system(
                    product_id=rec['product_id'],
                    new_price=rec['new_price']
                )
                
                if success:
                    executed.append(rec)
                    print(f"✓ Auto-updated {rec['product_name']} to
                        ${rec['new_price']}")
            
            else:
                # Queue for human approval
                self.pending_approvals.append(rec)
                print(f"⏳ Queued for approval: {rec['product_name']}")
        
        return executed
 

class AmazonPriceMonitor:
    """
    Specialized monitoring for Amazon's dynamic pricing
    """
    
    def __init__(self, api_key):
        self.sgai = ScrapeGraphAI(api_key=api_key)
        self.price_changes_today = 0
    
    def monitor_amazon_prices(self, product_asins):
        """
        Track Amazon prices in real-time
        """
        prompt = """
        Extract Amazon pricing data:
        - Current price
        - List price (crossed out)
        - Prime shipping availability
        - Subscribe & Save discount
        - Used/refurbished offers
        - Warehouse deals
        - Lightning deals or limited-time offers
        - Buy Box winner price
        """
        
        for asin in product_asins:
            amazon_url = f"https://www.amazon.com/dp/{asin}"
            
            data = self.sgai.extract(
                url=amazon_url,
                prompt=prompt
            )
            
            # Detect price changes
            if self.has_price_changed(asin, data):
                self.trigger_response(asin, data)

class SaaSCompetitiveIntelligence:
    """
    Monitor SaaS competitor pricing and packaging
    """
    
    def __init__(self, api_key):
        self.sgai = ScrapeGraphAI(api_key=api_key)
    
    def monitor_saas_competitor(self, competitor_url):
        """
        Comprehensive SaaS competitive intelligence
        """
        
        # Pricing intelligence
        pricing_prompt = """
        Extract SaaS pricing details:
        - All plan names and tiers
        - Monthly and annual pricing
        - Per-user or per-feature costs
        - Enterprise/custom pricing mentions
        - Free trial duration
        - Money-back guarantee period
        - Add-on or module pricing
        - Volume discount structure
        """
        
        # Feature intelligence
        features_prompt = """
        Extract feature and capability information:
        - Features included in each plan
        - Feature comparison across tiers
        - Recently added features (NEW badges)
        - Beta or upcoming features
        - Integration offerings
        - API access details
        - Support levels by plan
        - SLA commitments
        """
        
        # Market positioning
        positioning_prompt = """
        Extract market positioning signals:
        - Target customer size or industry
        - Use case examples
        - Competitive comparison claims
        - ROI or value propositions
        - Customer testimonials
        - Case study industries
        - Awards or certifications
        """
        
        intelligence = {
            'pricing': self.sgai.extract(
                url=f"{competitor_url}/pricing",
                prompt=pricing_prompt
            ),
            'features': self.sgai.extract(
                url=f"{competitor_url}/features",
                prompt=features_prompt
            ),
            'positioning': self.sgai.extract(
                url=f"{competitor_url}/customers",
                prompt=positioning_prompt
            ),
            'timestamp': datetime.now().isoformat()
        }
        
        return intelligence

# Quick starter template
 
from scrapegraph_py import ScrapeGraphAI
 
class QuickPriceIntelligence:
    def __init__(self, api_key, competitors):
        self.sgai = ScrapeGraphAI(api_key=api_key)
        self.competitors = competitors
    
    def collect_prices(self):
        """Simple price collection to get started"""
        results = []
        
        for comp in self.competitors:
            data = self.sgai.extract(
                url=comp['url'],
                prompt="""
                Extract:
                - Product names
                - Current prices
                - Original prices if discounted
                - Stock status
                """
            )
            results.append({'competitor': comp['name'], 'data': data})
        
        return results
 
# Start simple, then expand
intel = QuickPriceIntelligence(
    api_key="your-key",
    competitors=[
        {'name': 'Competitor 1', 'url': 'https://comp1.com'},
        {'name': 'Competitor 2', 'url': 'https://comp2.com'}
    ]
)
 
prices = intel.collect_prices()
print(prices)

from scrapegraph_py import ScrapeGraphAI
from datetime import datetime
 
# 1. Initialize
sgai = ScrapeGraphAI(api_key="your-scrapegraph-api-key")
 
# 2. Collect competitor prices
response = sgai.extract(
    url="https://your-competitor.com",
    prompt="Extract all product names and prices"
)
 
# 3. Analyze
print(f"Collected at: {datetime.now()}")
print(f"Competitor Data: {response}")
 
# That's it! You're now monitoring competitor prices.
# Add automation, alerts, and optimization from there.