ltvtoolkit 0.1.0

Customer lifetime value modeling with cohort curves, RFMT, and gradient boosting

LTVToolkit

Customer Lifetime Value modeling with cohort curves, RFMT, and gradient boosting.

Installation

pip install ltvtoolkit

For full functionality (XGBoost, LightGBM, Lifetimes):

pip install ltvtoolkit[full]

Quick Start

import pandas as pd
import asyncio
from ltvtoolkit import run_ltv_model

# Load your transaction data
transactions = pd.read_csv('transactions.csv')

# Required columns: customer_id, order_date, value
# Optional: acquisition_date, acquisition_channel

# Convert to bytes
data_bytes = transactions.to_csv(index=False).encode()

# Run LTV model
async def analyze():
    results = await run_ltv_model(data_bytes)
    return results

results = asyncio.run(analyze())

# Access predictions
customer_predictions = results['individual_files']['ltv_projection_by_customer']
print(customer_predictions)

Features

• Multi-model ensemble: Combines cohort curves, gradient boosted trees, and RFMT/BG-NBD models
• Flexible data formats: Supports various transaction schemas
• 36-month projections: Customer and cohort-level LTV forecasts
• Production-ready: Comprehensive validation and error handling

Data Requirements

Your CSV/DataFrame must have:

• customer_id: Unique customer identifier
• order_date: Transaction date
• value: Transaction amount

Optional columns:

• acquisition_date: Customer acquisition date
• acquisition_channel: Marketing channel

Output

The model returns a dictionary with:

• individual_files: Dict of output datasets
  • ltv_projection_by_customer: Customer-level predictions
  • ltv_projection_by_cohort: Cohort curves (M0-M36)
  • ltv_model_performance_metrics: Performance metrics
  • ltv_feature_importance: Feature importance scores
  • And more...
• summary_file: Combined summary report

Use Cases

• Marketing ROI analysis
• Customer segmentation
• Revenue forecasting
• Channel optimization
• CAC payback analysis

License

MIT License - see LICENSE file for details.

Links

• GitHub: https://github.com/yourusername/ltvtoolkit
• PyPI: https://pypi.org/project/ltvtoolkit/
• Issues: https://github.com/yourusername/ltvtoolkit/issues

Version History

0.1.0 (2025-01-XX)

• Initial release
• Cohort revenue curve model
• Gradient boosted trees model
• RFMT/BG-NBD model
• Ensemble prediction strategy