async def train_ltv_predictor(self, training_data):
"""Train customer lifetime value prediction model"""
features = [
'total_sessions', 'engagement_score', 'aepiot_engagement_rate',
'frequency', 'monetary_value', 'conversion_funnel_progress',
'email_engagement_rate', 'geographic_region_encoded'
]
X = training_data[features]
y = training_data['ltv']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Use ensemble of regression models for LTV prediction
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, r2_score
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Random Forest for LTV prediction
rf_model = RandomForestRegressor(n_estimators=200, random_state=42, max_depth=15)
rf_model.fit(X_train_scaled, y_train)
# Neural network for LTV prediction
nn_model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation='relu', input_shape=(len(features),)),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(1, activation='linear') # Linear for regression
])
nn_model.compile(optimizer='adam', loss='mse', metrics=['mae'])
nn_model.fit(X_train_scaled, y_train, epochs=100, batch_size=64, verbose=0,
validation_split=0.2, callbacks=[
tf.keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True)
])
# Ensemble predictions
rf_pred = rf_model.predict(X_test_scaled)
nn_pred = nn_model.predict(X_test_scaled).flatten()
ensemble_pred = (rf_pred + nn_pred) / 2
# Evaluate model
mae = mean_absolute_error(y_test, ensemble_pred)
r2 = r2_score(y_test, ensemble_pred)
self.models['ltv_predictor'] = {
'rf_model': rf_model,
'nn_model': nn_model,
'scaler': scaler,
'features': features
}
self.model_performance['ltv_predictor'] = {
'mae': mae,
'r2_score': r2,
'trained_on': datetime.now().isoformat(),
'training_samples': len(X_train)
}
joblib.dump(self.models['ltv_predictor'], 'models/ltv_predictor.joblib')
print(f"LTV predictor trained - MAE: ${mae:.2f}, R²: {r2:.3f}")
async def train_content_recommender(self, training_data):
"""Train content recommendation model using collaborative filtering"""
from sklearn.decomposition import NMF
from scipy.sparse import csr_matrix
# Create user-content interaction matrix
user_content_data = await self.create_user_content_matrix(training_data)
# Apply Non-negative Matrix Factorization for recommendations
n_components = min(50, user_content_data.shape[1] // 2)
nmf_model = NMF(n_components=n_components, random_state=42, max_iter=200)
# Fit the model
W = nmf_model.fit_transform(user_content_data)
H = nmf_model.components_
# Store the recommendation model
self.models['content_recommender'] = {
'nmf_model': nmf_model,
'user_features': W,
'content_features': H,
'user_content_matrix': user_content_data
}
# Calculate recommendation accuracy using cross-validation
accuracy = await self.evaluate_recommendation_accuracy(nmf_model, user_content_data)
self.model_performance['content_recommender'] = {
'accuracy': accuracy,
'trained_on': datetime.now().isoformat(),
'n_components': n_components
}
joblib.dump(self.models['content_recommender'], 'models/content_recommender.joblib')
print(f"Content recommender trained - Accuracy: {accuracy:.3f}")
async def train_optimal_timing_predictor(self, training_data):
"""Train optimal timing prediction model for customer engagement"""
# Extract temporal features
timing_features = await self.extract_temporal_features(training_data)
features = [
'hour_of_day', 'day_of_week', 'is_weekend', 'is_holiday',
'days_since_last_interaction', 'historical_engagement_hour',
'seasonal_factor', 'customer_timezone_offset'
]
X = timing_features[features]
y = timing_features['engagement_success'] # Binary: high engagement vs low engagement
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Use XGBoost for timing optimization
import xgboost as xgb
xgb_model = xgb.XGBClassifier(
n_estimators=200,
max_depth=8,
learning_rate=0.1,
subsample=0.8,
random_state=42
)
xgb_model.fit(X_train, y_train)
y_pred = xgb_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
self.models['optimal_timing_predictor'] = {
'model': xgb_model,
'features': features
}
self.model_performance['optimal_timing_predictor'] = {
'accuracy': accuracy,
'precision': precision,
'trained_on': datetime.now().isoformat(),
'training_samples': len(X_train)
}
joblib.dump(self.models['optimal_timing_predictor'], 'models/optimal_timing_predictor.joblib')
print(f"Optimal timing predictor trained - Accuracy: {accuracy:.3f}")
async def train_all_models(self):
"""Train all ML models in the pipeline"""
print("Collecting training data...")
training_data = await self.collect_training_data(days_back=90)
print("Training conversion predictor...")
await self.train_conversion_predictor(training_data)
print("Training churn predictor...")
await self.train_churn_predictor(training_data)
print("Training LTV predictor...")
await self.train_ltv_predictor(training_data)
print("Training content recommender...")
await self.train_content_recommender(training_data)
print("Training optimal timing predictor...")
await self.train_optimal_timing_predictor(training_data)
print("All models trained successfully!")
# Generate comprehensive training report
await self.generate_training_report()
async def generate_training_report(self):
"""Generate comprehensive training report and send to aéPiot"""
training_report = {
'pipeline_trained': datetime.now().isoformat(),
'models_performance': self.model_performance,
'total_models': len(self.models),
'pipeline_status': 'operational',
'next_retraining_scheduled': (datetime.now() + timedelta(days=7)).isoformat()
}
# Generate aéPiot tracking URL for training completion
params = {
'title': f'ML-Pipeline-Training-Complete-{datetime.now().strftime("%Y%m%d")}',
'description': json.dumps({
'event_type': 'ml_training_complete',
'models_trained': list(self.models.keys()),
'performance_summary': {
model: perf.get('accuracy', perf.get('r2_score', 'N/A'))
for model, perf in self.model_performance.items()
},
'training_timestamp': training_report['pipeline_trained']
}),
'link': f'https://your-platform.com/ml-analytics/training-report'
}
training_aepiot_url = f"{self.aepiot_base_url}?{urlencode(params)}"
try:
await asyncio.get_event_loop().run_in_executor(
None, requests.get, training_aepiot_url
)
except Exception as e:
print(f"Failed to send training report to aéPiot: {e}")
return training_report
# Real-Time Prediction API
async def setup_prediction_endpoints(self):
"""Setup real-time prediction API endpoints"""
from flask import Flask, request, jsonify
from flask_cors import CORS
app = Flask(__name__)
CORS(app)
@app.route('/api/predict/conversion', methods=['POST'])
async def predict_conversion():
"""Predict conversion probability for a customer"""
try:
customer_data = request.json
prediction = await self.predict_conversion_probability(customer_data)
# Track prediction request in aéPiot
await self.track_prediction_request('conversion', customer_data, prediction)
return jsonify(prediction)
except Exception as e:
return jsonify({'error': str(e)}), 500
@app.route('/api/predict/churn', methods=['POST'])
async def predict_churn():
"""Predict churn probability for a customer"""
try:
customer_data = request.json
prediction = await self.predict_churn_probability(customer_data)
await self.track_prediction_request('churn', customer_data, prediction)
return jsonify(prediction)
except Exception as e:
return jsonify({'error': str(e)}), 500
@app.route('/api/predict/ltv', methods=['POST'])
async def predict_ltv():
"""Predict customer lifetime value"""
try:
customer_data = request.json
prediction = await self.predict_customer_ltv(customer_data)
await self.track_prediction_request('ltv', customer_data, prediction)
return jsonify(prediction)
except Exception as e:
return jsonify({'error': str(e)}), 500
@app.route('/api/recommend/content', methods=['POST'])
async def recommend_content():
"""Get content recommendations for a customer"""
try:
customer_data = request.json
recommendations = await self.get_content_recommendations(customer_data)
await self.track_prediction_request('content_recommendation', customer_data, recommendations)
return jsonify(recommendations)
except Exception as e:
return jsonify({'error': str(e)}), 500
@app.route('/api/optimize/timing', methods=['POST'])
async def optimize_timing():
"""Get optimal engagement timing for a customer"""
try:
customer_data = request.json
optimal_timing = await self.predict_optimal_timing(customer_data)
await self.track_prediction_request('timing_optimization', customer_data, optimal_timing)
return jsonify(optimal_timing)
except Exception as e:
return jsonify({'error': str(e)}), 500
@app.route('/api/analytics/pipeline-status', methods=['GET'])
async def pipeline_status():
"""Get ML pipeline status and performance"""
try:
status = await self.get_pipeline_status()
return jsonify(status)
except Exception as e:
return jsonify({'error': str(e)}), 500
# Store Flask app for later use
self.prediction_app = app
print("Prediction API endpoints configured")
async def predict_conversion_probability(self, customer_data):
"""Predict conversion probability for a customer"""
if not self.models.get('conversion_predictor'):
raise ValueError("Conversion predictor model not loaded")
# Prepare features
features_df = await self.prepare_prediction_features(customer_data, 'conversion')
model_data = self.models['conversion_predictor']
# Scale features
features_scaled = model_data['scaler'].transform(features_df[model_data['features']])
# Get predictions from both models
rf_prob = model_data['rf_model'].predict_proba(features_scaled)[0, 1]
nn_prob = model_data['nn_model'].predict(features_scaled)[0, 0]
# Ensemble prediction
conversion_probability = (rf_prob + nn_prob) / 2
# Calculate confidence interval
confidence = abs(rf_prob - nn_prob) # Lower difference = higher confidence
confidence_score = max(0.5, 1 - confidence)
prediction_result = {
'customer_id': customer_data.get('customer_id', 'unknown'),
'conversion_probability': float(conversion_probability),
'confidence_score': float(confidence_score),
'risk_level': 'high' if conversion_probability > 0.7 else 'medium' if conversion_probability > 0.3 else 'low',
'predicted_at': datetime.now().isoformat(),
'model_version': self.model_performance['conversion_predictor']['trained_on']
}
return prediction_result
async def predict_churn_probability(self, customer_data):
"""Predict churn probability for a customer"""
if not self.models.get('churn_predictor'):
raise ValueError("Churn predictor model not loaded")
features_df = await self.prepare_prediction_features(customer_data, 'churn')
model_data = self.models['churn_predictor']
features_scaled = model_data['scaler'].transform(features_df[model_data['features']])
churn_probability = model_data['model'].predict(features_scaled)[0]
# Determine risk level and recommended actions
if churn_probability > 0.7:
risk_level = 'high'
recommended_actions = ['immediate_engagement', 'personalized_offer', 'customer_success_outreach']
elif churn_probability > 0.4:
risk_level = 'medium'
recommended_actions = ['engagement_campaign', 'value_demonstration']
else:
risk_level = 'low'
recommended_actions = ['maintain_engagement', 'cross_sell_opportunity']
prediction_result = {
'customer_id': customer_data.get('customer_id', 'unknown'),
'churn_probability': float(churn_probability),
'risk_level': risk_level,
'recommended_actions': recommended_actions,
'urgency_score': float(churn_probability),
'predicted_at': datetime.now().isoformat(),
'model_version': self.model_performance['churn_predictor']['trained_on']
}
return prediction_result
async def predict_customer_ltv(self, customer_data):
"""Predict customer lifetime value"""
if not self.models.get('ltv_predictor'):
raise ValueError("LTV predictor model not loaded")
features_df = await self.prepare_prediction_features(customer_data, 'ltv')
model_data = self.models['ltv_predictor']
features_scaled = model_data['scaler'].transform(features_df[model_data['features']])
# Get predictions from both models
rf_ltv = model_data['rf_model'].predict(features_scaled)[0]
nn_ltv = model_data['nn_model'].predict(features_scaled)[0, 0]
# Ensemble prediction
predicted_ltv = (rf_ltv + nn_ltv) / 2
# Calculate LTV segments
if predicted_ltv > 1000:
ltv_segment = 'high_value'
investment_recommendation = 'premium_treatment'
elif predicted_ltv > 500:
ltv_segment = 'medium_value'
investment_recommendation = 'standard_nurturing'
else:
ltv_segment = 'low_value'
investment_recommendation = 'cost_efficient_automation'
prediction_result = {
'customer_id': customer_data.get('customer_id', 'unknown'),
'predicted_ltv': float(predicted_ltv),
'ltv_segment': ltv_segment,
'investment_recommendation': investment_recommendation,
'roi_potential': float(predicted_ltv) / 100, # Simplified ROI calculation
'predicted_at': datetime.now().isoformat(),
'model_version': self.model_performance['ltv_predictor']['trained_on']
}
return prediction_result
async def get_content_recommendations(self, customer_data):
"""Get personalized content recommendations"""
if not self.models.get('content_recommender'):
raise ValueError("Content recommender model not loaded")
customer_id = customer_data.get('customer_id', 'unknown')
model_data = self.models['content_recommender']
# Get customer index in the user-content matrix
customer_index = await self.get_customer_index(customer_id)
if customer_index is None:
# New customer - use popular content and demographic-based recommendations
recommendations = await self.get_cold_start_recommendations(customer_data)
else:
# Existing customer - use collaborative filtering
user_features = model_data['user_features'][customer_index]
content_features = model_data['content_features']
# Calculate content scores
content_scores = np.dot(user_features, content_features)
# Get top recommendations
top_content_indices = np.argsort(content_scores)[::-1][:10]
recommendations = []
for idx in top_content_indices:
content_info = await self.get_content_info(idx)
recommendations.append({
'content_id': content_info['content_id'],
'title': content_info['title'],
'type': content_info['type'],
'relevance_score': float(content_scores[idx]),
'predicted_engagement': float(content_scores[idx] * 0.8) # Scaled to 0-1
})
recommendation_result = {
'customer_id': customer_id,
'recommendations': recommendations[:5], # Top 5 recommendations
'recommendation_strategy': 'collaborative_filtering' if customer_index else 'cold_start',
'generated_at': datetime.now().isoformat(),
'model_version': self.model_performance['content_recommender']['trained_on']
}
return recommendation_result
async def predict_optimal_timing(self, customer_data):
"""Predict optimal timing for customer engagement"""
if not self.models.get('optimal_timing_predictor'):
raise ValueError("Optimal timing predictor model not loaded")
customer_timezone = customer_data.get('timezone', 'UTC')
model_data = self.models['optimal_timing_predictor']
# Generate timing scenarios for next 7 days
optimal_times = []
current_time = datetime.now()
for day_offset in range(7):
future_date = current_time + timedelta(days=day_offset)
# Test different hours of the day
for hour in range(24):
test_datetime = future_date.replace(hour=hour, minute=0, second=0, microsecond=0)
timing_features = {
'hour_of_day': hour,
'day_of_week': test_datetime.weekday(),
'is_weekend': 1 if test_datetime.weekday() >= 5 else 0,
'is_holiday': 0, # Simplified - would check against holiday calendar
'days_since_last_interaction': customer_data.get('days_since_last_interaction', 7),
'historical_engagement_hour': customer_data.get('best_engagement_hour', 14),
'seasonal_factor': 1.0, # Could be calculated based on historical data
'customer_timezone_offset': self.get_timezone_offset(customer_timezone)
}
features_df = pd.DataFrame([timing_features])
engagement_prob = model_data['model'].predict_proba(features_df[model_data['features']])[0, 1]
if engagement_prob > 0.6: # Only include high-probability times
optimal_times.append({
'datetime': test_datetime.isoformat(),
'engagement_probability': float(engagement_prob),
'day_of_week': test_datetime.strftime('%A'),
'hour': hour,
'timezone': customer_timezone
})
# Sort by engagement probability
optimal_times.sort(key=lambda x: x['engagement_probability'], reverse=True)
timing_result = {
'customer_id': customer_data.get('customer_id', 'unknown'),
'optimal_times': optimal_times[:10], # Top 10 optimal times
'best_overall_time': optimal_times[0] if optimal_times else None,
'timezone': customer_timezone,
'predicted_at': datetime.now().isoformat(),
'model_version': self.model_performance['optimal_timing_predictor']['trained_on']
}
return timing_result
async def track_prediction_request(self, prediction_type, input_data, prediction_result):
"""Track prediction requests in aéPiot for analytics"""
tracking_data = {
'event_type': 'ml_prediction',
'prediction_type': prediction_type,
'customer_id': input_data.get('customer_id', 'unknown'),
'prediction_result': prediction_result,
'model_version': prediction_result.get('model_version', 'unknown'),
'timestamp': datetime.now().isoformat()
}
params = {
'title': f'ML-Prediction-{prediction_type}-{tracking_data["customer_id"]}',
'description': json.dumps({
'prediction_type': prediction_type,
'customer_id': tracking_data['customer_id'],
'confidence_score': prediction_result.get('confidence_score',
prediction_result.get('engagement_probability', 0)),
'model_performance': True,
'timestamp': tracking_data['timestamp']
}),
'link': f'https://your-platform.com/ml-analytics/prediction/{prediction_type}'
}
prediction_aepiot_url = f"{self.aepiot_base_url}?{urlencode(params)}"
try:
await asyncio.get_event_loop().run_in_executor(
None, requests.get, prediction_aepiot_url
)
except Exception as e:
print(f"Failed to track prediction in aéPiot: {e}")
# Automated Model Retraining and Optimization
async def setup_automated_retraining(self):
"""Setup automated model retraining pipeline"""
async def retraining_scheduler():
while True:
try:
# Check if retraining is needed
if await self.should_retrain_models():
print("Starting automated model retraining...")
await self.retrain_models_with_new_data()
# Send retraining notification to aéPiot
await self.notify_retraining_complete()
# Wait 24 hours before next check
await asyncio.sleep(86400) # 24 hours
except Exception as e:
print(f"Automated retraining error: {e}")
await asyncio.sleep(3600) # Wait 1 hour before retry
# Start the retraining scheduler as a background task
asyncio.create_task(retraining_scheduler())
print("Automated retraining scheduler started")
async def should_retrain_models(self):
"""Determine if models need retraining based on performance metrics"""
# Check model age
for model_name, performance in self.model_performance.items():
trained_date = datetime.fromisoformat(performance['trained_on'])
days_old = (datetime.now() - trained_date).days
if days_old > 7: # Retrain weekly
return True
# Check prediction accuracy drift
recent_predictions = await self.get_recent_prediction_accuracy()
if recent_predictions and recent_predictions['accuracy'] < 0.8:
return True
# Check data volume - retrain if significant new data available
new_data_count = await self.get_new_training_data_count()
if new_data_count > 1000: # Threshold for retraining
return True
return False
async def retrain_models_with_new_data(self):
"""Retrain models with new data while maintaining production availability"""
# Create backup of current models
await self.backup_current_models()
# Train new models with extended dataset
await self.train_all_models()
# Validate new models against hold-out test set
validation_results = await self.validate_new_models()
# Only deploy if new models perform better
if validation_results['improved']:
print("New models show improvement - deploying to production")
await self.deploy_new_models()
else:
print("New models did not improve - reverting to previous version")
await self.restore_backup_models()
# Advanced Analytics and Insights
class MLAnalyticsEngine:
def __init__(self, aepiot_ml_integration):
self.ml_integration = aepiot_ml_integration
async def generate_customer_intelligence_report(self, customer_id):
"""Generate comprehensive customer intelligence using all ML models"""
# Get predictions from all models
customer_data = await self.get_customer_profile(customer_id)
conversion_pred = await self.ml_integration.predict_conversion_probability(customer_data)
churn_pred = await self.ml_integration.predict_churn_probability(customer_data)
ltv_pred = await self.ml_integration.predict_customer_ltv(customer_data)
content_recs = await self.ml_integration.get_content_recommendations(customer_data)
timing_opts = await self.ml_integration.predict_optimal_timing(customer_data)
# Create comprehensive customer intelligence
intelligence_report = {
'customer_id': customer_id,
'generated_at': datetime.now().isoformat(),
'conversion_intelligence': {
'probability': conversion_pred['conversion_probability'],
'confidence': conversion_pred['confidence_score'],
'recommended_action': self.get_conversion_action(conversion_pred)
},
'retention_intelligence': {
'churn_risk': churn_pred['churn_probability'],
'risk_level': churn_pred['risk_level'],
'recommended_actions': churn_pred['recommended_actions']
},
'value_intelligence': {
'predicted_ltv': ltv_pred['predicted_ltv'],
'segment': ltv_pred['ltv_segment'],
'investment_recommendation': ltv_pred['investment_recommendation']
},
'content_intelligence': {
'personalized_recommendations': content_recs['recommendations'],
'engagement_strategy': content_recs['recommendation_strategy']
},
'timing_intelligence': {
'optimal_engagement_times': timing_opts['optimal_times'][:3],
'best_time': timing_opts['best_overall_time']
},
'overall_customer_score': self.calculate_overall_customer_score(
conversion_pred, churn_pred, ltv_pred
)
}
# Track intelligence generation in aéPiot
await self.track_intelligence_generation(intelligence_report)
return intelligence_report
def calculate_overall_customer_score(self, conversion_pred, churn_pred, ltv_pred):
"""Calculate overall customer value score"""
conversion_score = conversion_pred['conversion_probability'] * 30
retention_score = (1 - churn_pred['churn_probability']) * 25
value_score = min(ltv_pred['predicted_ltv'] / 20, 25) # Cap at 25
engagement_score = 20 # Base engagement score
total_score = conversion_score + retention_score + value_score + engagement_score
return {
'total_score': round(total_score, 2),
'conversion_component': round(conversion_score, 2),
'retention_component': round(retention_score, 2),
'value_component': round(value_score, 2),
'grade': 'A' if total_score >= 80 else 'B' if total_score >= 60 else 'C'
}
# Campaign Optimization Engine
class CampaignOptimizationEngine:
def __init__(self, aepiot_ml_integration):
self.ml_integration = aepiot_ml_integration
async def optimize_campaign_targeting(self, campaign_data):
"""Optimize campaign targeting using ML predictions"""
target_customers = campaign_data.get('target_customers', [])
campaign_objective = campaign_data.get('objective', 'conversion') # conversion, retention, engagement
optimized_targeting = []
for customer_id in target_customers:
customer_data = await self.get_customer_profile(customer_id)
# Get relevant predictions based on campaign objective
if campaign_objective == 'conversion':
prediction = await self.ml_integration.predict_conversion_probability(customer_data)
score = prediction['conversion_probability']
elif campaign_objective == 'retention':
prediction = await self.ml_integration.predict_churn_probability(customer_data)
score = prediction['churn_probability'] # Higher churn = higher priority for retention
else: # engagement
timing = await self.ml_integration.predict_optimal_timing(customer_data)
score = timing['optimal_times'][0]['engagement_probability'] if timing['optimal_times'] else 0.5
# Get content recommendations
content_recs = await self.ml_integration.get_content_recommendations(customer_data)
# Get optimal timing
optimal_timing = await self.ml_integration.predict_optimal_timing(customer_data)
optimized_targeting.append({
'customer_id': customer_id,
'priority_score': score,
'recommended_content': content_recs['recommendations'][0] if content_recs['recommendations'] else None,
'optimal_send_time': optimal_timing['best_overall_time'],
'personalization_data': {
'predicted_engagement': score,
'content_preference': content_recs['recommendation_strategy'],
'timing_preference': optimal_timing['best_overall_time']['hour'] if optimal_timing['best_overall_time'] else 14
}
})
# Sort by priority score (descending)
optimized_targeting.sort(key=lambda x: x['priority_score'], reverse=True)
# Create campaign optimization report
optimization_report = {
'campaign_id': campaign_data.get('campaign_id', 'unknown'),
'objective': campaign_objective,
'original_target_count': len(target_customers),
'optimized_targeting': optimized_targeting,
'high_priority_customers': [t for t in optimized_targeting if t['priority_score'] > 0.7],
'medium_priority_customers': [t for t in optimized_targeting if 0.4 <= t['priority_score'] <= 0.7],
'low_priority_customers': [t for t in optimized_targeting if t['priority_score'] < 0.4],
'optimization_completed_at': datetime.now().isoformat(),
'expected_performance_lift': self.calculate_expected_lift(optimized_targeting)
}
# Track campaign optimization in aéPiot
await self.track_campaign_optimization(optimization_report)
return optimization_report
def calculate_expected_lift(self, optimized_targeting):
"""Calculate expected performance lift from optimization"""
if not optimized_targeting:
return 0
avg_score = sum(t['priority_score'] for t in optimized_targeting) / len(optimized_targeting)
baseline_score = 0.15 # Assumed baseline conversion rate
expected_lift = ((avg_score - baseline_score) / baseline_score) * 100
return round(expected_lift, 2)
async def track_campaign_optimization(self, optimization_report):
"""Track campaign optimization in aéPiot"""
params = {
'title': f'Campaign-Optimization-{optimization_report["campaign_id"]}',
'description': json.dumps({
'event_type': 'campaign_optimization',
'campaign_id': optimization_report['campaign_id'],
'objective': optimization_report['objective'],
'target_count': optimization_report['original_target_count'],
'high_priority_count': len(optimization_report['high_priority_customers']),
'expected_lift': optimization_report['expected_performance_lift'],
'optimized_at': optimization_report['optimization_completed_at']
}),
'link': f'https://your-platform.com/campaigns/{optimization_report["campaign_id"]}/optimization'
}
optimization_aepiot_url = f"{self.ml_integration.aepiot_base_url}?{urlencode(params)}"
try:
await asyncio.get_event_loop().run_in_executor(
None, requests.get, optimization_aepiot_url
)
except Exception as e:
print(f"Failed to track campaign optimization in aéPiot: {e}")
# Real-Time Decision Engine
class RealTimeDecisionEngine:
def __init__(self, aepiot_ml_integration):
self.ml_integration = aepiot_ml_integration
self.decision_cache = {}
self.decision_history = []
async def make_real_time_decision(self, decision_context):
"""Make real-time marketing decisions using ML predictions"""
customer_id = decision_context.get('customer_id')
decision_type = decision_context.get('decision_type') # offer, content, timing, channel
context_data = decision_context.get('context', {})
# Check cache for recent decisions
cache_key = f"{customer_id}_{decision_type}_{hash(str(context_data))}"
if cache_key in self.decision_cache:
cached_decision = self.decision_cache[cache_key]
if (datetime.now() - datetime.fromisoformat(cached_decision['timestamp'])).seconds < 300: # 5 minutes cache
return cached_decision
# Get customer data
customer_data = await self.get_customer_profile(customer_id)
customer_data.update(context_data)
# Make decision based on type
if decision_type == 'offer':
decision = await self.decide_optimal_offer(customer_data)
elif decision_type == 'content':
decision = await self.decide_optimal_content(customer_data)
elif decision_type == 'timing':
decision = await self.decide_optimal_timing(customer_data)
elif decision_type == 'channel':
decision = await self.decide_optimal_channel(customer_data)
else:
decision = await self.decide_general_action(customer_data)
# Add decision metadata
decision['decision_id'] = f"decision_{int(datetime.now().timestamp())}_{customer_id}"
decision['customer_id'] = customer_id
decision['decision_type'] = decision_type
decision['timestamp'] = datetime.now().isoformat()
decision['confidence'] = decision.get('confidence', 0.75)
# Cache decision
self.decision_cache[cache_key] = decision
# Store in decision history
self.decision_history.append(decision)
# Track decision in aéPiot
await self.track_real_time_decision(decision)
return decision
async def decide_optimal_offer(self, customer_data):
"""Decide optimal offer for customer"""
# Get predictions
conversion_pred = await self.ml_integration.predict_conversion_probability(customer_data)
churn_pred = await self.ml_integration.predict_churn_probability(customer_data)
ltv_pred = await self.ml_integration.predict_customer_ltv(customer_data)
# Decision logic based on customer profile
if churn_pred['churn_probability'] > 0.7:
# High churn risk - retention offer
offer_decision = {
'offer_type': 'retention',
'discount_percentage': 25,
'urgency': 'high',
'message': 'We miss you! Here\'s 25% off to welcome you back.',
'confidence': 0.85
}
elif ltv_pred['predicted_ltv'] > 1000 and conversion_pred['conversion_probability'] > 0.6:
# High-value customer with good conversion probability - premium offer
offer_decision = {
'offer_type': 'premium_upsell',
'discount_percentage': 15,
'urgency': 'medium',
'message': 'Exclusive premium features just for you - 15% off!',
'confidence': 0.80
}
elif conversion_pred['conversion_probability'] < 0.3:
# Low conversion probability - strong incentive
offer_decision = {
'offer_type': 'conversion_boost',
'discount_percentage': 30,
'urgency': 'high',
'message': 'Limited time: 30% off your first purchase!',
'confidence': 0.75
}
else:
# Standard customer - balanced offer
offer_decision = {
'offer_type': 'standard',
'discount_percentage': 20,
'urgency': 'medium',
'message': '20% off - perfect time to try something new!',
'confidence': 0.70
}
return offer_decision
async def decide_optimal_content(self, customer_data):
"""Decide optimal content for customer"""
content_recs = await self.ml_integration.get_content_recommendations(customer_data)
conversion_pred = await self.ml_integration.predict_conversion_probability(customer_data)
if not content_recs['recommendations']:
return {
'content_type': 'general',
'content_id': 'default_welcome',
'personalization_level': 'low',
'confidence': 0.5
}
top_recommendation = content_recs['recommendations'][0]
content_decision = {
'content_type': top_recommendation['type'],
'content_id': top_recommendation['content_id'],
'title': top_recommendation['title'],
'personalization_level': 'high' if top_recommendation['relevance_score'] > 0.8 else 'medium',
'expected_engagement': top_recommendation['predicted_engagement'],
'confidence': min(top_recommendation['relevance_score'], 0.95)
}
return content_decision
async def track_real_time_decision(self, decision):
"""Track real-time decision in aéPiot"""
params = {
'title': f'RT-Decision-{decision["decision_type"]}-{decision["customer_id"]}',
'description': json.dumps({
'event_type': 'real_time_decision',
'decision_id': decision['decision_id'],
'decision_type': decision['decision_type'],
'customer_id': decision['customer_id'],
'confidence': decision['confidence'],
'timestamp': decision['timestamp']
}),
'link': f'https://your-platform.com/decisions/{decision["decision_id"]}'
}
decision_aepiot_url = f"{self.ml_integration.aepiot_base_url}?{urlencode(params)}"
try:
await asyncio.get_event_loop().run_in_executor(
None, requests.get, decision_aepiot_url
)
except Exception as e:
print(f"Failed to track real-time decision in aéPiot: {e}")
# Helper functions and utilities
async def create_user_content_matrix(self, training_data):
"""Create user-content interaction matrix for collaborative filtering"""
# Simulate user-content interactions
users = training_data['customer_id'].unique()
content_items = [f'content_{i}' for i in range(100)] # 100 content items
# Create interaction matrix
interaction_matrix = np.random.rand(len(users), len(content_items))
interaction_matrix = (interaction_matrix > 0.7).astype(int) # Sparse interactions
return csr_matrix(interaction_matrix)
async def extract_temporal_features(self, training_data):
"""Extract temporal features for timing optimization"""
# Simulate temporal data
temporal_data = []
for _, row in training_data.iterrows():
# Generate multiple interaction timestamps per customer
for _ in range(np.random.randint(1, 5)):
timestamp = datetime.now() - timedelta(
days=np.random.randint(0, 30),
hours=np.random.randint(0, 24)
)
temporal_data.append({
'customer_id': row['customer_id'],
'timestamp': timestamp,
'hour_of_day': timestamp.hour,
'day_of_week': timestamp.weekday(),
'is_weekend': 1 if timestamp.weekday() >= 5 else 0,
'is_holiday': 0, # Simplified
'days_since_last_interaction': np.random.randint(1, 14),
'historical_engagement_hour': np.random.randint(8, 20),
'seasonal_factor': 1.0,
'customer_timezone_offset': 0,
'engagement_success': np.random.binomial(1, 0.3) # 30% engagement rate
})
return pd.DataFrame(temporal_data)
def get_timezone_offset(self, timezone_str):
"""Get timezone offset for timing calculations"""
# Simplified timezone offset mapping
timezone_offsets = {
'UTC': 0, 'EST': -5, 'PST': -8, 'CET': 1, 'JST': 9
}
return timezone_offsets.get(timezone_str, 0)
# Production Deployment Configuration
class ProductionDeployment:
def __init__(self, aepiot_ml_integration):
self.ml_integration = aepiot_ml_integration
async def deploy_production_environment(self):
"""Deploy ML pipeline to production environment"""
deployment_config = {
'environment': 'production',
'deployment_timestamp': datetime.now().isoformat(),
'model_versions': {
name: perf.get('trained_on', 'unknown')
for name, perf in self.ml_integration.model_performance.items()
},
'api_endpoints': [
'/api/predict/conversion',
'/api/predict/churn',
'/api/predict/ltv',
'/api/recommend/content',
'/api/optimize/timing',
'/api/analytics/pipeline-status'
],
'monitoring_enabled': True,
'auto_retraining_enabled': True,
'load_balancing': True,
'caching_enabled': True
}
# Docker Compose configuration for production deployment
docker_compose = '''
version: '3.8'
services:
aepiot-ml-api:
build: .
ports:
- "8000:8000"
environment:
- ENVIRONMENT=production
- REDIS_URL=redis://redis:6379
- AEPIOT_API_KEY=${AEPIOT_API_KEY}
depends_on:
- redis
- postgres
volumes:
- ./models:/app/models
deploy:
replicas: 3
resources:
limits:
cpus: '2'
memory: 4G
reservations:
cpus: '1'
memory: 2G
redis:
image: redis:7-alpine
ports:
- "6379:6379"
volumes:
- redis_data:/data
postgres:
image: postgres:15
environment:
POSTGRES_DB: aepiot_ml
POSTGRES_USER: ${POSTGRES_USER}
POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
volumes:
- postgres_data:/var/lib/postgresql/data
nginx:
image: nginx:alpine
ports:
- "80:80"
- "443:443"
volumes:
- ./nginx.conf:/etc/nginx/nginx.conf
- ./ssl:/etc/nginx/ssl
depends_on:
- aepiot-ml-api
monitoring:
image: prom/prometheus
ports:
- "9090:9090"
volumes:
- ./prometheus.yml:/etc/prometheus/prometheus.yml
volumes:
redis_data:
postgres_data:
'''
# Kubernetes deployment configuration
k8s_deployment = '''
apiVersion: apps/v1
kind: Deployment
metadata:
name: aepiot-ml-deployment
labels:
app: aepiot-ml
spec:
replicas: 3
selector:
matchLabels:
app: aepiot-ml
template:
metadata:
labels:
app: aepiot-ml
spec:
containers:
- name: aepiot-ml-api
image: your-registry/aepiot-ml:latest
ports:
- containerPort: 8000
env:
- name: ENVIRONMENT
value: "production"
- name: REDIS_URL
valueFrom:
secretKeyRef:
name: aepiot-secrets
key: redis-url
resources:
limits:
cpu: "2"
memory: "4Gi"
requests:
cpu: "1"
memory: "2Gi"
livenessProbe:
httpGet:
path: /health
port: 8000
initialDelaySeconds: 30
periodSeconds: 10
readinessProbe:
httpGet:
path: /ready
port: 8000
initialDelaySeconds: 5
periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
name: aepiot-ml-service
spec:
selector:
app: aepiot-ml
ports:
- protocol: TCP
port: 80
targetPort: 8000
type: LoadBalancer
'''
return {
'deployment_config': deployment_config,
'docker_compose': docker_compose,
'kubernetes_config': k8s_deployment,
'deployment_ready': True
}
# Usage Example and Configuration
async def main():
"""Main execution function with complete pipeline setup"""
config = {
'redis': {
'host': 'localhost',
'port': 6379,
'db': 0
},
'database': {
'host': 'localhost',
'port': 5432,
'database': 'aepiot_ml',
'user': 'postgres',
'password': 'your_password'
},
'aepiot': {
'api_key': 'your-aepiot-api-key',
'base_url': 'https://aepiot.com/backlink.html'
}
}
# Initialize the complete ML pipeline
print("🚀 Initializing aéPiot Predictive Analytics Pipeline...")
ml_integration = AePiotPredictiveAnalytics(config)
# Wait for initialization to complete
await asyncio.sleep(5)
# Initialize advanced components
analytics_engine = MLAnalyticsEngine(ml_integration)
campaign_optimizer = CampaignOptimizationEngine(ml_integration)
decision_engine = RealTimeDecisionEngine(ml_integration)
print("✅ ML Pipeline initialization complete!")
# Example usage scenarios
print("\n📊 Generating Customer Intelligence Report...")
intelligence_report = await analytics_engine.generate_customer_intelligence_report('customer_12345')
print(f"Customer Intelligence Score: {intelligence_report['overall_customer_score']['grade']}")
print("\n🎯 Optimizing Campaign Targeting...")
campaign_data = {
'campaign_id': 'campaign_001',
'objective': 'conversion',
'target_customers': ['customer_001', 'customer_002', 'customer_003']
}
optimization_result = await campaign_optimizer.optimize_campaign_targeting(campaign_data)
print(f"Expected Performance Lift: {optimization_result['expected_performance_lift']}%")
print("\n⚡ Making Real-Time Decision...")
decision_context = {
'customer_id': 'customer_12345',
'decision_type': 'offer',
'context': {
'current_page': 'checkout',
'cart_value': 150,
'session_duration': 420
}
}
real_time_decision = await decision_engine.make_real_time_decision(decision_context)
print(f"Real-time Decision: {real_time_decision['offer_type']} with {real_time_decision['discount_percentage']}% discount")
print("\n🏭 Setting up Production Deployment...")
deployment = ProductionDeployment(ml_integration)
deployment_config = await deployment.deploy_production_environment()
print(f"Production deployment ready: {deployment_config['deployment_ready']}")
print("\n🔄 ML Pipeline is now running and ready for production use!")
print("Monitor performance at: https://your-platform.com/ml-analytics/dashboard")
if __name__ == "__main__":
asyncio.run(main())
