Advanced Customer Segmentation System

System Architecture

A comprehensive customer segmentation system that combines multiple clustering approaches with real-time classification and behavioral analysis.

Core Components

1. Feature Engineering Pipeline

class CustomerFeatureProcessor:
    def __init__(self, config: Dict[str, Any]):
        self.transformers = {
            'behavioral': BehavioralFeatureGenerator(
                window_sizes=config['time_windows']
            ),
            'demographic': DemographicEncoder(
                encoding_config=config['encoding']
            ),
            'transactional': TransactionAggregator(
                agg_config=config['aggregation']
            )
        }
        self.scaler = RobustScaler()
        
    async def generate_features(
        self,
        customer_data: pd.DataFrame
    ) -> pd.DataFrame:
        feature_sets = []
        
        # Generate features in parallel
        feature_tasks = [
            transformer.transform(customer_data)
            for transformer in self.transformers.values()
        ]
        
        results = await asyncio.gather(*feature_tasks)
        features = pd.concat(results, axis=1)
        
        # Scale features
        scaled_features = self.scaler.fit_transform(features)
        return pd.DataFrame(
            scaled_features,
            columns=features.columns,
            index=features.index
        )

class BehavioralFeatureGenerator:
    def __init__(self, window_sizes: List[str]):
        self.windows = window_sizes
        self.event_processors = {
            'purchase': self._process_purchase_events,
            'browsing': self._process_browsing_events,
            'engagement': self._process_engagement_events
        }
        
    async def transform(
        self,
        events: pd.DataFrame
    ) -> pd.DataFrame:
        features = {}
        
        for window in self.windows:
            window_events = self._get_windowed_events(events, window)
            for event_type, processor in self.event_processors.items():
                features.update(
                    processor(window_events, window)
                )
                
        return pd.DataFrame.from_dict(features)

2. Clustering Engine

class ClusteringEngine:
    def __init__(self, config: Dict[str, Any]):
        self.models = {
            'kmeans': KMeansClusterer(config['kmeans_config']),
            'hierarchical': HierarchicalClusterer(config['hierarchical_config']),
            'dbscan': DBSCANClusterer(config['dbscan_config'])
        }
        self.ensemble = ClusterEnsemble(config['ensemble_config'])
        self.validator = ClusterValidator()
        
    async def generate_segments(
        self,
        features: pd.DataFrame
    ) -> SegmentationResults:
        # Run multiple clustering algorithms
        cluster_results = {}
        for name, model in self.models.items():
            cluster_results[name] = await model.fit_predict(features)
            
        # Validate clustering quality
        validation_results = await self.validator.validate_all(
            features,
            cluster_results
        )
        
        # Generate ensemble clustering
        ensemble_clusters = await self.ensemble.combine_clusters(
            cluster_results,
            validation_results
        )
        
        return SegmentationResults(
            clusters=ensemble_clusters,
            individual_results=cluster_results,
            validation=validation_results
        )

class ClusterValidator:
    def __init__(self):
        self.metrics = {
            'silhouette': silhouette_score,
            'calinski': calinski_harabasz_score,
            'davies': davies_bouldin_score
        }
        
    async def validate_all(
        self,
        features: pd.DataFrame,
        cluster_results: Dict[str, np.ndarray]
    ) -> Dict[str, Dict[str, float]]:
        validation_results = {}
        
        for model_name, clusters in cluster_results.items():
            validation_results[model_name] = {
                metric_name: metric_func(features, clusters)
                for metric_name, metric_func in self.metrics.items()
            }
            
        return validation_results

3. Segment Analysis

class SegmentAnalyzer:
    def __init__(self, config: Dict[str, Any]):
        self.profiler = SegmentProfiler(config['profile_config'])
        self.comparator = SegmentComparator()
        self.visualizer = SegmentVisualizer()
        
    async def analyze_segments(
        self,
        features: pd.DataFrame,
        segments: np.ndarray
    ) -> SegmentAnalysis:
        # Generate segment profiles
        profiles = await self.profiler.generate_profiles(
            features,
            segments
        )
        
        # Compare segments
        comparisons = await self.comparator.compare_segments(
            features,
            segments,
            profiles
        )
        
        # Generate visualizations
        visualizations = await self.visualizer.create_visualizations(
            features,
            segments,
            profiles
        )
        
        return SegmentAnalysis(
            profiles=profiles,
            comparisons=comparisons,
            visualizations=visualizations
        )

class SegmentProfiler:
    def __init__(self, config: Dict[str, Any]):
        self.summarizers = {
            'statistical': StatisticalSummarizer(),
            'behavioral': BehavioralSummarizer(),
            'demographic': DemographicSummarizer()
        }
        
    async def generate_profiles(
        self,
        features: pd.DataFrame,
        segments: np.ndarray
    ) -> Dict[int, SegmentProfile]:
        profiles = {}
        
        for segment_id in np.unique(segments):
            segment_mask = segments == segment_id
            segment_features = features[segment_mask]
            
            profile = {}
            for summarizer in self.summarizers.values():
                profile.update(
                    await summarizer.summarize(segment_features)
                )
                
            profiles[segment_id] = SegmentProfile(profile)
            
        return profiles

4. Real-time Classification

class SegmentClassifier:
    def __init__(self, config: Dict[str, Any]):
        self.feature_processor = CustomerFeatureProcessor(
            config['feature_config']
        )
        self.classifier = LightGBMClassifier(
            config['classifier_config']
        )
        self.cache = ClassificationCache(
            config['cache_config']
        )
        
    async def classify_customer(
        self,
        customer_data: Dict[str, Any]
    ) -> SegmentPrediction:
        # Check cache
        cached_result = await self.cache.get(
            customer_data['customer_id']
        )
        if cached_result:
            return cached_result
            
        # Generate features
        features = await self.feature_processor.generate_features(
            customer_data
        )
        
        # Predict segment
        segment_probs = self.classifier.predict_proba(features)
        prediction = SegmentPrediction(
            segment_id=np.argmax(segment_probs),
            probabilities=segment_probs
        )
        
        # Cache result
        await self.cache.store(
            customer_data['customer_id'],
            prediction
        )
        
        return prediction

Usage Example

# Initialize segmentation system
config = {
    'feature_config': {
        'time_windows': ['7d', '30d', '90d'],
        'encoding': {'max_categories': 10},
        'aggregation': {'functions': ['mean', 'sum', 'count']}
    },
    'clustering_config': {
        'kmeans_config': {'n_clusters': range(2, 15)},
        'hierarchical_config': {'n_clusters': range(2, 15)},
        'dbscan_config': {'eps': [0.3, 0.5, 0.7]}
    }
}

segmentation = CustomerSegmentation(config)

# Generate customer segments
features = await segmentation.generate_features(customer_data)
segmentation_results = await segmentation.generate_segments(features)

# Analyze segments
analysis = await segmentation.analyze_segments(
    features,
    segmentation_results.clusters
)

# Classify new customer
new_customer = {
    'customer_id': 'CUST123',
    'transactions': [...],
    'events': [...],
    'demographics': {...}
}

segment = await segmentation.classify_customer(new_customer)

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