Competitor Price Intelligence with AI Web Scraping

from scrapegraph_py import ScrapeGraphAI
from datetime import datetime
import json
import time
 
class AdvancedPriceIntelligence:
    """
    Comprehensive price intelligence system that captures
    every pricing signal from competitor websites
    """
    
    def __init__(self, api_key):
        self.sgai = ScrapeGraphAI(api_key=api_key)
        self.price_history = {}
        self.competitor_strategies = {}
        
    def collect_complete_pricing_data(self, competitor_url, product_category=None):
        """
        Collect comprehensive pricing intelligence, not just base prices
        """
        
        # Advanced prompt that captures ALL pricing signals
        comprehensive_pricing_prompt = """
        Extract COMPLETE pricing information with maximum detail:
        
        For each product:
        1. Product identification:
           - Product name and SKU/model number
           - Product category and subcategory
           - Brand name
           
        2. Price architecture:
           - Current display price (what customer sees)
           - Original/list price (if shown)
           - Strikethrough/was price (psychological anchor)
           - Discount amount and percentage
           - Price per unit (if bulk item)
           
        3. Alternative pricing:
           - Subscription/recurring price options
           - Bundle pricing (bought together deals)
           - Volume discounts (buy 2, get X% off)
           - Membership pricing (Prime, Pro, etc.)
           - Student/senior/military discounts
           
        4. Price context:
           - Stock availability (in stock, low stock, out of stock)
           - Shipping cost or free shipping threshold
           - Delivery timeframe
           - Return policy and associated costs
           - Warranty or guarantee mentions
           
        5. Psychological pricing elements:
           - Urgency indicators ("Only 3 left!", "Sale ends today")
           - Social proof ("Best seller", "#1 rated")
           - Scarcity signals ("Limited edition", "While supplies last")
           - Value framing ("Save $50", "70% off")
           
        6. Competitive positioning:
           - Rating/review score and count
           - Comparison to similar products
           - "Our price vs competitors" messaging
           - Price match guarantees
           
        Extract this for ALL products visible on the page.
        """
        
        try:
            response = self.sgai.extract(
                url=competitor_url,
                prompt=comprehensive_pricing_prompt
            )
            
            # Structure the collected data
            pricing_intelligence = {
                'competitor': competitor_url,
                'timestamp': datetime.now().isoformat(),
                'products': response,
                'metadata': {
                    'collection_method': 'advanced_scraping',
                    'data_completeness': self.assess_data_completeness(response)
                }
            }
            
            # Store in history
            self.store_price_history(competitor_url, pricing_intelligence)
            
            return pricing_intelligence
            
        except Exception as e:
            return {
                'competitor': competitor_url,
                'timestamp': datetime.now().isoformat(),
                'error': str(e),
                'status': 'failed'
            }
    
    def collect_multi_page_pricing(self, base_url, category_pages):
        """
        Collect pricing across multiple category pages
        Essential for comprehensive market coverage
        """
        all_pricing_data = []
        
        for category in category_pages:
            url = f"{base_url}/{category}"
            
            print(f"📊 Collecting pricing data from: {category}")
            
            pricing_data = self.collect_complete_pricing_data(url)
            all_pricing_data.append(pricing_data)
            
            # Rate limiting to be respectful
            time.sleep(2)
        
        return {
            'competitor': base_url,
            'total_categories': len(category_pages),
            'timestamp': datetime.now().isoformat(),
            'data': all_pricing_data
        }
    
    def collect_temporal_pricing_patterns(self, competitor_url, days=30):
        """
        Collect pricing data over time to identify patterns
        Critical for understanding pricing strategies
        """
        
        print(f"📈 Starting temporal analysis for {days} days")
        print(f"🎯 Target: {competitor_url}")
        
        # In production, this would run continuously
        # For demo, we'll simulate with multiple collections
        temporal_data = []
        
        for day in range(days):
            # Collect pricing data
            daily_data = self.collect_complete_pricing_data(competitor_url)
            temporal_data.append(daily_data)
            
            # In production: sleep 24 hours
            # For demo: just append to history
            
        # Analyze patterns
        patterns = self.analyze_temporal_patterns(temporal_data)
        
        return {
            'competitor': competitor_url,
            'analysis_period_days': days,
            'patterns_detected': patterns,
            'pricing_volatility': self.calculate_volatility(temporal_data),
            'strategy_insights': self.infer_pricing_strategy(patterns)
        }
    
    def analyze_temporal_patterns(self, temporal_data):
        """
        Identify pricing patterns over time
        """
        patterns = {
            'day_of_week': {},
            'time_of_day': {},
            'seasonal': {},
            'event_based': []
        }
        
        # Analyze day-of-week patterns
        # Example: Prices drop on Mondays, increase on Fridays
        
        # Analyze time-of-day patterns  
        # Example: Flash sales at specific times
        
        # Analyze seasonal patterns
        # Example: Q4 holiday pricing, back-to-school sales
        
        # Detect event-based pricing
        # Example: Competitor response to your price changes
        
        return patterns
    
    def store_price_history(self, competitor, data):
        """Store pricing data for historical analysis"""
        if competitor not in self.price_history:
            self.price_history[competitor] = []
        
        self.price_history[competitor].append(data)
        
        # Keep last 90 days
        if len(self.price_history[competitor]) > 90:
            self.price_history[competitor] = self.price_history[competitor][-90:]
    
    def assess_data_completeness(self, data):
        """
        Assess how complete the collected data is
        Helps identify gaps in intelligence
        """
        completeness_score = 0
        max_score = 100
        
        # Check for presence of key data points
        if data:
            if 'price' in str(data).lower():
                completeness_score += 20
            if 'discount' in str(data).lower():
                completeness_score += 15
            if 'stock' in str(data).lower():
                completeness_score += 10
            if 'shipping' in str(data).lower():
                completeness_score += 10
            if 'review' in str(data).lower():
                completeness_score += 10
            if 'bundle' in str(data).lower():
                completeness_score += 15
            if 'subscription' in str(data).lower():
                completeness_score += 20
        
        return {
            'score': completeness_score,
            'percentage': (completeness_score / max_score) * 100,
            'quality': 'excellent' if completeness_score > 80 else 'good' if
                completeness_score > 60 else 'needs_improvement'
        }
 
# Example usage: Comprehensive competitive pricing analysis
intelligence = AdvancedPriceIntelligence(api_key="your-scrapegraphai-key")
 
# Collect from main competitor
competitor_data = intelligence.collect_complete_pricing_data(
    competitor_url="https://competitor.com/products"
)
 
print(json.dumps(competitor_data, indent=2))
 

class PriceStrategyAnalyzer:
    """
    Analyzes competitor pricing strategies and tactics
    Reveals the "why" behind the pricing
    """
    
    def __init__(self):
        self.known_strategies = {
            'penetration': self.detect_penetration_pricing,
            'skimming': self.detect_price_skimming,
            'psychological': self.detect_psychological_pricing,
            'dynamic': self.detect_dynamic_pricing,
            'value_based': self.detect_value_based_pricing,
            'competitive': self.detect_competitive_pricing,
            'freemium': self.detect_freemium_strategy,
            'good_better_best': self.detect_tiered_pricing
        }
    
    def analyze_pricing_strategy(self, competitor_pricing_data):
        """
        Identify what pricing strategy competitor is using
        """
        detected_strategies = []
        
        for strategy_name, detector_func in self.known_strategies.items():
            confidence = detector_func(competitor_pricing_data)
            if confidence > 0.6:
                detected_strategies.append({
                    'strategy': strategy_name,
                    'confidence': confidence,
                    'indicators': self.get_strategy_indicators(strategy_name,
                        competitor_pricing_data)
                })
        
        return {
            'primary_strategy': max(detected_strategies, key=lambda x: x['confidence'])
                if detected_strategies else None,
            'all_strategies': detected_strategies,
            'recommendation': self.generate_counter_strategy(detected_strategies)
        }
    
    def detect_penetration_pricing(self, data):
        """
        Detect if competitor is using penetration pricing
        (Low prices to gain market share)
        """
        indicators = []
        confidence = 0.0
        
        # Check if prices are significantly below market
        if self.is_below_market_average(data, threshold=0.15):
            confidence += 0.3
            indicators.append("Prices 15%+ below market average")
        
        # Check for aggressive promotions
        if self.has_heavy_discounting(data):
            confidence += 0.2
            indicators.append("Frequent aggressive promotions")
        
        # Check for "new to market" signals
        if self.appears_new_to_market(data):
            confidence += 0.2
            indicators.append("New competitor trying to gain share")
        
        # Check for low-margin indicators
        if self.shows_low_margin_strategy(data):
            confidence += 0.3
            indicators.append("Pricing suggests low-margin, high-volume strategy")
        
        return confidence
    
    def detect_psychological_pricing(self, data):
        """
        Detect psychological pricing tactics
        (Charm pricing, anchoring, decoy pricing, etc.)
        """
        confidence = 0.0
        indicators = []
        
        # Check for charm pricing (.99, .95, .97)
        charm_usage = self.calculate_charm_pricing_usage(data)
        if charm_usage > 0.7:
            confidence += 0.4
            indicators.append(f"Charm pricing used in {charm_usage:.0%} of products")
        
        # Check for price anchoring (crossed-out prices)
        if self.has_price_anchoring(data):
            confidence += 0.3
            indicators.append("Heavy use of crossed-out original prices")
        
        # Check for decoy pricing
        if self.has_decoy_pricing(data):
            confidence += 0.3
            indicators.append("Decoy products to make others look better")
        
        return confidence
    
    def detect_tiered_pricing(self, data):
        """
        Detect Good-Better-Best tiered pricing strategy
        """
        confidence = 0.0
        indicators = []
        
        # Look for 3-tier structure
        tiers = self.identify_pricing_tiers(data)
        
        if len(tiers) == 3:
            confidence += 0.4
            indicators.append("Clear 3-tier pricing structure")
            
            # Check for strategic middle tier pricing
            if self.middle_tier_is_sweet_spot(tiers):
                confidence += 0.3
                indicators.append("Middle tier appears to be designed as sweet spot")
            
            # Check for decoy bottom tier
            if self.bottom_tier_is_decoy(tiers):
                confidence += 0.3
                indicators.append("Bottom tier may be decoy to anchor middle")
        
        return confidence
    
    def generate_counter_strategy(self, detected_strategies):
        """
        Generate recommendations to counter competitor strategy
        """
        if not detected_strategies:
            return "No clear strategy detected - collect more data"
        
        primary = detected_strategies[0]['strategy']
        
        counter_strategies = {
            'penetration': {
                'response': 'Value differentiation',
                'tactics': [
                    'Emphasize quality over price',
                    'Bundle premium features',
                    'Build brand loyalty before they establish',
                    'Target different customer segment',
                    'Highlight total cost of ownership'
                ],
                'avoid': 'Do not engage in price war - they have lower margins'
            },
            'skimming': {
                'response': 'Value alternative positioning',
                'tactics': [
                    'Position as "smart choice" alternative',
                    'Emphasize price-performance ratio',
                    'Target early majority, not early adopters',
                    'Create "premium lite" option',
                    'Offer flexible payment terms'
                ],
                'avoid': 'Do not try to out-premium them'
            },
            'psychological': {
                'response': 'Transparency and trust',
                'tactics': [
                    'Use honest, straightforward pricing',
                    'Emphasize "no tricks" positioning',
                    'Build trust through pricing transparency',
                    'Appeal to sophisticated buyers',
                    'Highlight long-term value'
                ],
                'avoid': 'Do not engage in psychological warfare'
            },
            'good_better_best': {
                'response': 'Simplified choice architecture',
                'tactics': [
                    'Offer 2-tier pricing (simple choice)',
                    'Position as anti-complexity alternative',
                    'Emphasize ease of decision',
                    'Create "all-inclusive" single option',
                    'Target decision-fatigued buyers'
                ],
                'avoid': 'Do not add more tiers to compete'
            }
        }
        
        return counter_strategies.get(primary, {
            'response': 'Custom strategy needed',
            'tactics': ['Analyze further', 'Test multiple approaches'],
            'avoid': 'Reactive pricing without strategy'
        })
    
    def calculate_charm_pricing_usage(self, data):
        """Calculate percentage of products using charm pricing"""
        # Implementation would analyze actual prices
        # For demo, return placeholder
        return 0.75  # 75% usage
    
    def has_price_anchoring(self, data):
        """Detect presence of crossed-out original prices"""
        # Check if data contains "was" prices, strikethrough, etc.
        return True  # Placeholder
    
    def identify_pricing_tiers(self, data):
        """Identify distinct pricing tiers"""
        # Analyze price distribution and cluster into tiers
        return [
            {'name': 'Basic', 'price': 29, 'features': 'limited'},
            {'name': 'Pro', 'price': 79, 'features': 'recommended'},
            {'name': 'Enterprise', 'price': 199, 'features': 'complete'}
        ]
 
# Example: Analyze competitor pricing strategy
analyzer = PriceStrategyAnalyzer()
strategy_analysis = analyzer.analyze_pricing_strategy(competitor_data)
 
print("Detected Pricing Strategy:")
print(json.dumps(strategy_analysis, indent=2))
 

class PredictivePriceIntelligence:
    """
    Predict future competitor price changes
    Enables proactive rather than reactive pricing
    """
    
    def __init__(self, api_key):
        self.sgai = ScrapeGraphAI(api_key=api_key)
        self.price_history = {}
        self.prediction_models = {}
    
    def predict_competitor_price_changes(self, competitor, product_category):
        """
        Predict when and how competitor will change prices
        """
        
        # Collect leading indicators
        indicators = self.collect_price_change_indicators(competitor)
        
        # Analyze historical patterns
        patterns = self.analyze_historical_patterns(competitor)
        
        # Generate predictions
        predictions = {
            'next_7_days': self.predict_short_term(indicators, patterns),
            'next_30_days': self.predict_medium_term(indicators, patterns),
            'next_quarter': self.predict_long_term(indicators, patterns)
        }
        
        return predictions
    
    def collect_price_change_indicators(self, competitor_url):
        """
        Collect signals that often precede price changes
        """
        
        # Indicator 1: Inventory levels
        inventory_prompt = """
        Extract inventory signals:
        - Stock status for products (in stock, low stock, out of stock)
        - "Only X left" messaging
        - Backorder or pre-order availability
        - Shipping delays mentioned
        """
        
        # Indicator 2: Promotional language
        promo_prompt = """
        Extract promotional indicators:
        - "Sale ending soon" messages
        - Countdown timers
        - Seasonal promotion mentions
        - New product announcements
        - Clearance or closeout language
        """
        
        # Indicator 3: Competitive positioning
        positioning_prompt = """
        Extract competitive positioning:
        - "Lowest price guaranteed" claims
        - Price match policies
        - "Compare to" competitor mentions
        - Value proposition changes
        """
        
        # Collect all indicators
        inventory_data = self.sgai.extract(
            url=competitor_url,
            prompt=inventory_prompt
        )
        
        promo_data = self.sgai.extract(
            url=competitor_url,
            prompt=promo_prompt
        )
        
        positioning_data = self.sgai.extract(
            url=competitor_url,
            prompt=positioning_prompt
        )
        
        return {
            'inventory': inventory_data,
            'promotions': promo_data,
            'positioning': positioning_data,
            'collected_at': datetime.now().isoformat()
        }
    
    def predict_short_term(self, indicators, patterns):
        """
        Predict price changes in next 7 days
        """
        predictions = []
        
        # Check for imminent sale end
        if self.detect_sale_ending_soon(indicators):
            predictions.append({
                'event': 'price_increase',
                'probability': 0.89,
                'timing': '2-3 days',
                'magnitude': '+15-25%',
                'trigger': 'Current sale ending',
                'recommendation': 'Maintain current pricing to capture switchers'
            })
        
        # Check for low inventory
        if self.detect_low_inventory(indicators):
            predictions.append({
                'event': 'price_increase_or_stockout',
                'probability': 0.76,
                'timing': '3-5 days',
                'magnitude': '+10-20%',
                'trigger': 'Low inventory levels',
                'recommendation': 'Highlight your availability in marketing'
            })
        
        # Check for new promotion signals
        if self.detect_new_promotion_prep(indicators):
            predictions.append({
                'event': 'price_decrease',
                'probability': 0.82,
                'timing': '4-7 days',
                'magnitude': '-20-30%',
                'trigger': 'New promotional campaign',
                'recommendation': 'Prepare counter-promotion'
            })
        
        return predictions
    
    def generate_proactive_recommendations(self, predictions):
        """
        Generate specific actions to take based on predictions
        """
        recommendations = []
        
        for prediction in predictions:
            if prediction['event'] == 'price_increase' and prediction['probability'] >
                0.7:
                recommendations.append({
                    'action': 'maintain_pricing',
                    'timing': 'immediate',
                    'rationale': f"Competitor likely to increase prices
                        {prediction['timing']}",
                    'expected_impact': '+12-18% conversion from price shoppers',
                    'duration': '7-10 days',
                    'marketing_message': 'Lock in current pricing before market
                        increases'
                })
            
            elif prediction['event'] == 'price_decrease' and prediction['probability'] >
                 0.7:
                recommendations.append({
                    'action': 'value_differentiation',
                    'timing': prediction['timing'],
                    'rationale': f"Competitor preparing price drop of
                        {prediction['magnitude']}",
                    'expected_impact': 'Minimize share loss through value emphasis',
                    'response_options': [
                        'Match price temporarily',
                        'Add bonus value without price cut',
                        'Emphasize quality/service differences',
                        'Create limited-time bundle'
                    ]
                })
        
        return recommendations
 
# Example: Predict and prepare for competitor moves
predictor = PredictivePriceIntelligence(api_key="your-key")
predictions = predictor.predict_competitor_price_changes(
    competitor="https://competitor.com",
    product_category="electronics"
)
 
print("Price Change Predictions:")
print(json.dumps(predictions, indent=2))
 

class PriceElasticityAnalyzer:
    """
    Estimate price elasticity from competitive intelligence
    Even without your own A/B test data
    """
    
    def __init__(self, api_key):
        self.sgai = ScrapeGraphAI(api_key=api_key)
    
    def estimate_market_elasticity(self, product_category):
        """
        Estimate price elasticity by analyzing competitor experiments
        """
        
        # Collect competitive pricing and performance data
        competitors = self.get_competitors_in_category(product_category)
        
        elasticity_estimates = []
        
        for competitor in competitors:
            # Collect pricing data
            pricing_data = self.collect_pricing_history(competitor)
            
            # Collect performance proxies (reviews, ratings, bestseller rank)
            performance_prompt = """
            Extract demand indicators:
            - Number of customer reviews (proxy for sales volume)
            - Review velocity (new reviews per day)
            - Bestseller rank or popularity indicators
            - Stock status changes over time
            - Waitlist or backorder mentions
            """
            
            performance_data = self.sgai.extract(
                url=competitor['url'],
                prompt=performance_prompt
            )
            
            # Estimate elasticity from price-volume relationship
            elasticity = self.calculate_elasticity(pricing_data, performance_data)
            
            elasticity_estimates.append({
                'competitor': competitor['name'],
                'estimated_elasticity': elasticity,
                'confidence': self.calculate_confidence(pricing_data, performance_data)
            })
        
        # Aggregate to market-level estimate
        market_elasticity = self.aggregate_elasticities(elasticity_estimates)
        
        return {
            'market_elasticity': market_elasticity,
            'interpretation': self.interpret_elasticity(market_elasticity),
            'pricing_implications': self.derive_pricing_strategy(market_elasticity),
            'competitor_estimates': elasticity_estimates
        }
    
    def interpret_elasticity(self, elasticity_value):
        """
        Translate elasticity number into actionable insight
        """
        if elasticity_value < -2.0:
            return {
                'category': 'Highly elastic',
                'meaning': 'Demand very sensitive to price',
                'implication': 'Small price cuts drive large volume increases',
                'strategy': 'Penetration pricing, volume focus',
                'risk': 'Price wars highly destructive'
            }
        elif elasticity_value < -1.0:
            return {
                'category': 'Elastic',
                'meaning': 'Demand sensitive to price',
                'implication': 'Price matters significantly in purchase decisions',
                'strategy': 'Competitive pricing important',
                'risk': 'Vulnerable to price competition'
            }
        elif elasticity_value < -0.5:
            return {
                'category': 'Moderately inelastic',
                'meaning': 'Demand somewhat insensitive to price',
                'implication': 'Non-price factors important',
                'strategy': 'Value-based pricing, differentiation',
                'risk': 'Limited volume gains from price cuts'
            }
        else:
            return {
                'category': 'Highly inelastic',
                'meaning': 'Demand very insensitive to price',
                'implication': 'Price has minimal impact on purchase',
                'strategy': 'Premium pricing, margin maximization',
                'risk': 'May be leaving money on table'
            }
    
    def derive_optimal_price(self, elasticity, costs, competitive_prices):
        """
        Calculate optimal price given elasticity estimate
        """
        # Implement optimal pricing formula
        # Optimal price = Cost / (1 + 1/elasticity)
        
        if elasticity >= -1.0:
            # Inelastic - price above competition
            recommended_position = 1.15  # 15% premium
        else:
            # Elastic - price competitively
            recommended_position = 0.98  # 2% discount
        
        competitive_avg = sum(competitive_prices) / len(competitive_prices)
        optimal_price = competitive_avg * recommended_position
        
        return {
            'optimal_price': optimal_price,
            'vs_competition': f"{((optimal_price/competitive_avg - 1) * 100):+.1f}%",
            'expected_margin': self.calculate_expected_margin(optimal_price, costs),
            'expected_volume_impact': self.estimate_volume_change(optimal_price,
                competitive_avg, elasticity)
        }
 
# Example: Determine optimal pricing using elasticity
elasticity_analyzer = PriceElasticityAnalyzer(api_key="your-key")
elasticity_analysis = elasticity_analyzer.estimate_market_elasticity("laptop_computers")
 
print("Market Elasticity Analysis:")
print(json.dumps(elasticity_analysis, indent=2))
 

class PsychologicalPricingOptimizer:
    """
    Optimize prices using psychological pricing principles
    """
    
    def __init__(self):
        self.psychological_principles = {
            'charm_pricing': self.apply_charm_pricing,
            'price_anchoring': self.optimize_anchoring,
            'relative_pricing': self.optimize_relative_pricing,
            'round_number_bias': self.leverage_round_numbers,
            'left_digit_effect': self.optimize_left_digit
        }
    
    def optimize_price_psychologically(self, base_price, context):
        """
        Transform mathematical optimal price into psychologically optimal price
        """
        optimizations = []
        
        # Apply charm pricing (.99 ending)
        charm_price = self.apply_charm_pricing(base_price)
        optimizations.append({
            'technique': 'charm_pricing',
            'original': base_price,
            'optimized': charm_price,
            'expected_lift': '+2-3% conversion',
            'explanation': 'Left-digit effect: $99.99 perceived much lower than $100'
        })
        
        # Optimize anchor pricing
        if context.get('show_original_price'):
            anchor_strategy = self.optimize_anchoring(base_price)
            optimizations.append(anchor_strategy)
        
        # Optimize relative to competition
        if context.get('competitive_prices'):
            relative_strategy = self.optimize_relative_pricing(
                base_price,
                context['competitive_prices']
            )
            optimizations.append(relative_strategy)
        
        # Select best optimization
        best_optimization = max(optimizations, key=lambda x: self.score_optimization(x))
        
        return {
            'recommended_price': best_optimization['optimized'],
            'psychological_techniques': optimizations,
            'expected_impact': self.estimate_psychological_impact(optimizations),
            'implementation_notes': self.generate_implementation_guide(optimizations)
        }
    
    def apply_charm_pricing(self, price):
        """
        Apply charm pricing (.99, .95, .97 endings)
        """
        # Round to nearest dollar, then subtract $0.01
        if price >= 100:
            # For larger amounts, use .99
            charm_price = int(price) - 0.01
        elif price >= 20:
            # For medium amounts, use .95
            charm_price = int(price) - 0.05
        else:
            # For small amounts, use .97
            charm_price = int(price) - 0.03
        
        return charm_price
    
    def optimize_anchoring(self, actual_price):
        """
        Calculate optimal anchor (crossed-out) price
        """
        # Research shows 20-40% anchor discount is most effective
        # Too small: not impressive
        # Too large: not credible
        
        optimal_anchor = actual_price * 1.35  # 35% anchor
        
        return {
            'technique': 'price_anchoring',
            'original': actual_price,
            'anchor_price': optimal_anchor,
            'display': f"<strike>${optimal_anchor:.2f}</strike> ${actual_price:.2f}",
            'savings_message': f"Save ${optimal_anchor - actual_price:.2f} (35%)",
            'expected_lift': '+8-12% conversion',
            'explanation': 'Anchor makes actual price feel like bargain'
        }
    
    def optimize_relative_pricing(self, your_price, competitive_prices):
        """
        Position price optimally relative to competition
        """
        competitive_avg = sum(competitive_prices) / len(competitive_prices)
        competitive_min = min(competitive_prices)
        competitive_max = max(competitive_prices)
        
        # Calculate optimal positioning
        if your_price < competitive_avg * 0.9:
            # You're significantly cheaper
            positioning = {
                'strategy': 'value_leader',
                'display_price': self.apply_charm_pricing(your_price),
                'messaging': f"${int(competitive_avg - your_price)} less than
                    competitors",
                'expected_lift': '+15-20% from price-sensitive buyers'
            }
        elif your_price > competitive_avg * 1.15:
            # You're premium priced
            positioning = {
                'strategy': 'premium_positioning',
                'display_price': int(your_price),  # Round number for premium
                'messaging': 'Premium quality, premium price',
                'expected_lift': '+5-8% from quality-focused buyers'
            }
        else:
            # You're competitively priced
            positioning = {
                'strategy': 'competitive_parity',
                'display_price': self.apply_charm_pricing(your_price),
                'messaging': 'Competitively priced with better value',
                'expected_lift': '+3-5% from balanced buyers'
            }
        
        return {
            'technique': 'relative_pricing',
            'positioning': positioning,
            'competitive_context': {
                'your_price': your_price,
                'market_avg': competitive_avg,
                'vs_average': f"{((your_price/competitive_avg - 1) * 100):+.1f}%",
                'your_rank': self.calculate_price_rank(your_price, competitive_prices)
            }
        }
    
    def generate_ab_test_variants(self, base_price):
        """
        Generate price variants for A/B testing
        """
        variants = []
        
        # Variant A: Charm pricing
        variants.append({
            'name': 'Charm',
            'price': self.apply_charm_pricing(base_price),
            'hypothesis': 'Left-digit effect increases perceived value',
            'expected_winner_probability': 0.45
        })
        
        # Variant B: Round number (premium perception)
        variants.append({
            'name': 'Round',
            'price': round(base_price),
            'hypothesis': 'Round number conveys quality/premium',
            'expected_winner_probability': 0.25
        })
        
        # Variant C: With anchor
        anchor_price = base_price * 1.35
        variants.append({
            'name': 'Anchored',
            'price': self.apply_charm_pricing(base_price),
            'anchor': round(anchor_price),
            'display': f"Was ${round(anchor_price)}, Now
                ${self.apply_charm_pricing(base_price)}",
            'hypothesis': 'Anchor increases perceived value',
            'expected_winner_probability': 0.30
        })
        
        return {
            'base_price': base_price,
            'test_variants': variants,
            'recommended_sample_size': self.calculate_sample_size(variants),
            'expected_test_duration': '7-14 days'
        }
 
# Example: Optimize pricing psychologically
psych_optimizer = PsychologicalPricingOptimizer()
 
optimized = psych_optimizer.optimize_price_psychologically(
    base_price=127.50,
    context={
        'show_original_price': True,
        'competitive_prices': [119.99, 129.99, 139.99, 149.99]
    }
)
 
print("Psychological Pricing Optimization:")
print(json.dumps(optimized, indent=2))
 

class RealTimeDynamicPricing:
    """
    Autonomous dynamic pricing system
    Adjusts prices in real-time based on market conditions
    """
    
    def __init__(self, api_key):
        self.sgai = ScrapeGraphAI(api_key=api_key)
        self.pricing_rules = {}
        self.performance_metrics = {}
    
    def calculate_optimal_price_realtime(self, product_id, context):
        """
        Calculate optimal price considering all real-time factors
        """
        
        # Factor 1: Competitive prices (real-time)
        competitive_prices = self.get_current_competitive_prices(product_id)
        
        # Factor 2: Demand indicators
        demand_level = self.assess_current_demand(product_id)
        
        # Factor 3: Inventory position
        inventory_status = self.get_inventory_status(product_id)
        
        # Factor 4: Time-based factors
        time_factors = self.get_time_factors()
        
        # Factor 5: Customer segment
        customer_segment = context.get('customer_segment', 'general')
        
        # Calculate base optimal price
        base_price = self.calculate_base_optimal_price(
            competitive_prices,
            demand_level,
            inventory_status
        )
        
        # Apply dynamic adjustments
        adjusted_price = self.apply_dynamic_adjustments(
            base_price,
            time_factors,
            customer_segment,
            demand_level
        )
        
        # Apply constraints and rules
        final_price = self.apply_business_rules(
            adjusted_price,
            product_id,
            context
        )
        
        return {
            'optimal_price': final_price,
            'base_price': base_price,
            'adjustments_applied': self.get_adjustment_details(),
            'expected_impact': self.estimate_impact(final_price, base_price),
            'confidence': self.calculate_pricing_confidence(),
            'valid_until': self.calculate_price_validity()
        }
    
    def apply_dynamic_adjustments(self, base_price, time_factors, customer_segment,
        demand_level):
        """
        Apply real-time adjustments to base price
        """
        adjusted_price = base_price
        adjustments = []
        
        # Time-based adjustments
        if time_factors['is_peak_hours']:
            adjusted_price *= 1.08  # 8% premium during peak
            adjustments.append({
                'type': 'peak_hours_premium',
                'multiplier': 1.08,
                'reason': 'Higher demand during peak shopping hours'
            })
        
        if time_factors['is_weekend']:
            adjusted_price *= 1.05  # 5% premium on weekends
            adjustments.append({
                'type': 'weekend_premium',
                'multiplier': 1.05,
                'reason': 'Increased weekend shopping activity'
            })
        
        # Demand-based adjustments
        if demand_level > 1.5:  # High demand
            adjusted_price *= 1.12  # 12% premium
            adjustments.append({
                'type': 'high_demand_premium',
                'multiplier': 1.12,
                'reason': f'Demand {demand_level:.1f}x normal levels'
            })
        elif demand_level < 0.7:  # Low demand
            adjusted_price *= 0.93  # 7% discount to stimulate
            adjustments.append({
                'type': 'demand_stimulation_discount',
                'multiplier': 0.93,
                'reason': f'Demand only {demand_level:.1f}x normal, discount to
                    stimulate'
            })
        
        # Customer segment adjustments
        segment_adjustments = {
            'new_customer': 0.95,  # 5% new customer discount
            'vip': 0.92,  # 8% VIP discount
            'price_sensitive': 0.97,  # 3% for price shoppers
            'general': 1.00  # No adjustment
        }
        
        segment_multiplier = segment_adjustments.get(customer_segment, 1.00)
        if segment_multiplier != 1.00:
            adjusted_price *= segment_multiplier
            adjustments.append({
                'type': 'segment_adjustment',
                'multiplier': segment_multiplier,
                'reason': f'Optimized for {customer_segment} segment'
            })
        
        return adjusted_price
    
    def autonomous_pricing_engine(self, product_id):
        """
        Fully autonomous pricing that continuously optimizes
        """
        
        print(f"🤖 Autonomous Pricing Engine Started for Product {product_id}")
        print("📊 Monitoring competitive prices and market conditions")
        print("⚡ Adjusting prices every 15 minutes")
        print("-" * 60)
        
        cycle = 0
        
        while True:
            cycle += 1
            
            try:
                # Calculate optimal price
                pricing_decision = self.calculate_optimal_price_realtime(
                    product_id=product_id,
                    context={
                        'customer_segment': 'general',
                        'sales_channel': 'website'
                    }
                )
                
                current_price = self.get_current_price(product_id)
                new_price = pricing_decision['optimal_price']
                
                # Check if price change is warranted
                price_change_pct = abs(new_price - current_price) / current_price
                
                if price_change_pct > 0.02:  # More than 2% change
                    # Significant change - update price
                    print(f"\n💰 Cycle #{cycle} - Price Update Recommended")
                    print(f"   Current: ${current_price:.2f}")
                    print(f"   Optimal: ${new_price:.2f}")
                    print(f"   Change: {((new_price/current_price - 1) * 100):+.1f}%")
                    print(f"   Reason: {pricing_decision['adjustments_applied']}")
                    
                    # In production: execute price change via API
                    # self.update_price(product_id, new_price)
                    
                    # Log the decision
                    self.log_pricing_decision(
                        product_id,
                        current_price,
                        new_price,
                        pricing_decision)
                    
                else:
                    print(f"✓ Cycle #{cycle} - Price optimal, no change needed")
                
                # Wait for next cycle (15 minutes)
                time.sleep(15 * 60)
                
            except Exception as e:
                print(f"✗ Error in pricing cycle: {e}")
                time.sleep(60)  # Wait 1 minute before retry
 
# Deploy autonomous pricing
dynamic_pricer = RealTimeDynamicPricing(api_key="your-key")
 
# Start autonomous engine for a product
# dynamic_pricer.autonomous_pricing_engine(product_id="PROD-12345")
 

class PriceIntelligenceROI:
    """
    Calculate complete ROI of price intelligence system
    """
    
    def calculate_roi(self, baseline_metrics, current_metrics, costs):
        """
        Comprehensive ROI calculation
        """
        
        # Revenue impact
        margin_improvement = (
            current_metrics['gross_margin'] - baseline_metrics['gross_margin']
        )
        annual_revenue = current_metrics['annual_revenue']
        revenue_impact = annual_revenue * margin_improvement
        
        # Cost savings
        analyst_time_saved = (
            baseline_metrics['analyst_hours_per_week'] - 
            current_metrics['analyst_hours_per_week']
        )
        analyst_cost_savings = analyst_time_saved * 52 * 75  # $75/hour
        
        # Opportunity capture
        opportunities_captured = (
            current_metrics['opportunities_captured'] - 
            baseline_metrics['opportunities_captured']
        )
        opportunity_value = opportunities_captured * 50000  # $50k per opportunity
        
        # Competitive advantages
        market_share_gain = (
            current_metrics['market_share'] - 
            baseline_metrics['market_share']
        )
        market_share_value = market_share_gain * annual_revenue * 10  # 10x revenue
        
        # Total benefits
        total_benefits = (
            revenue_impact +
            analyst_cost_savings +
            opportunity_value +
            market_share_value
        )
        
        # ROI calculation
        roi_percentage = ((total_benefits - costs['annual_cost']) /
            costs['annual_cost']) * 100
        
        return {
            'total_benefits': total_benefits,
            'revenue_impact': revenue_impact,
            'cost_savings': analyst_cost_savings,
            'opportunity_value': opportunity_value,
            'market_share_value': market_share_value,
            'total_costs': costs['annual_cost'],
            'net_value': total_benefits - costs['annual_cost'],
            'roi_percentage': roi_percentage,
            'payback_period_months': (costs['annual_cost'] / (total_benefits/12)),
            'summary': self.generate_roi_summary(roi_percentage, total_benefits, costs)
        }
 
# Example ROI calculation
roi_calculator = PriceIntelligenceROI()
 
baseline = {
    'gross_margin': 0.32,  # 32%
    'annual_revenue': 50_000_000,  # $50M
    'analyst_hours_per_week': 40,
    'opportunities_captured': 15,
    'market_share': 0.12  # 12%
}
 
current = {
    'gross_margin': 0.39,  # 39% (+7 percentage points)
    'annual_revenue': 50_000_000,
    'analyst_hours_per_week': 5,  # 35 hours saved
    'opportunities_captured': 45,  # +30 opportunities
    'market_share': 0.15  # 15% (+3 percentage points)
}
 
costs = {
    'annual_cost': 120_000  # ScrapeGraphAI + infrastructure + 0.5 FTE
}
 
roi = roi_calculator.calculate_roi(baseline, current, costs)
 
print(f"ROI: {roi['roi_percentage']:.0f}%")
print(f"Annual Value: ${roi['net_value']:,.0f}")
print(f"Payback Period: {roi['payback_period_months']:.1f} months")
 

from scrapegraph_py import ScrapeGraphAI
from datetime import datetime
 
# 1. Initialize
sgai = ScrapeGraphAI(api_key="your-scrapegraph-api-key")
 
# 2. Collect competitive prices
competitors = [
    "https://competitor1.com/products",
    "https://competitor2.com/products"
]
 
for competitor in competitors:
    pricing = sgai.extract(
        url=competitor,
        prompt="""
        Extract all product prices, discounts, 
        bundles, and promotional information
        """
    )
    
    print(f"Competitor: {competitor}")
    print(f"Pricing Data: {pricing}")
    print(f"Collected: {datetime.now()}")
    print("-" * 60)
 
# That's it! You're now gathering price intelligence.
# Next: Build this into a continuous monitoring system.