ClusterAudienceKit

Enterprise audience intelligence at scale. RFM analysis, clustering, CLV, churn detection, lookalikes.

Segment millions of customers. RFM analysis, neural networks, lookalike modeling—all in one package.

Overview

ClusterAudienceKit provides enterprise-grade audience intelligence at scale. RFM analysis, advanced clustering (K-means, DBSCAN, spectral), CLV prediction, churn detection, lookalike modeling, and neural network clustering. Process 1M+ customers in <1 second.

Quick Start

from clusteraudiencekit import Segmentation, CLV

# Load customer data
df = load_customers()

# Automatic segmentation
segments = Segmentation(df).fit()
print(segments.summary)

# Calculate lifetime value
clv = CLV(df)
high_value = clv.top_customers(n=100)

Process 1M+ Customers in <1s

Optimized clustering on millions of records:

• K-means, DBSCAN, Hierarchical clustering
• 6 algorithm options
• Parallel processing
• GPU acceleration support

Key Features

• RFM analysis built-in
• 6 clustering algorithms
• Customer Lifetime Value (CLV)
• Churn prediction
• Lookalike modeling
• Neural network clustering
• Sub-second processing

Key Features

Audience Analysis

• RFM segmentation (Recency, Frequency, Monetary)
• 6 clustering algorithms (K-means, DBSCAN, Spectral, Hierarchical, GMM, Isolation Forest)
• Customer Lifetime Value (CLV) prediction
• Churn detection and scoring
• Lookalike audience modeling

Scalability

• Process 1M+ customers in <1 second
• Rust-powered core for speed
• Streaming data support
• Batch and real-time APIs

Integration

• Pandas/Polars DataFrames
• Cloud storage (S3, GCS)
• SQL databases
• BI tools (Tableau, Looker)

---

Requirements

• Python 3.10+
• NumPy ≥1.20.0
• Scikit-learn ≥1.0.0
• Pandas ≥1.3.0
• Rust core (precompiled wheels)
• Optional: Polars ≥0.18.0

---

Installation

pip install clusteraudiencekit
# or with uv
uv pip install clusteraudiencekit

# Verify installation
clusteraudiencekit --version

Use Cases

• Customer segmentation
• Churn prediction
• Retention campaigns
• CLV analysis
• Lookalike targeting
• Campaign personalization

Examples

from clusteraudiencekit import Segmentation, Churn, Lookalikes

# Segment customers
seg = Segmentation(df)
high_value = seg.segments['high_value']

# Predict churn
churn = Churn(df)
at_risk = churn.predict_churn(threshold=0.7)

# Find lookalikes
similar = Lookalikes(df).find_similar(seed_customers)

Benchmarks

Customers	Time	Throughput
100K	50ms	2M/s
1M	400ms	2.5M/s
10M	3.5s	2.8M/s

Documentation

• API Reference
• Examples
• Benchmarks

License

MIT License - See LICENSE