ltv-predictor

Customer Lifetime Value Prediction Skill

An automated LTV predictive analysis tool based on the theory from Lesson 3 of "Data Analysis with Kage: Ten Talks," providing a complete solution from RFM feature engineering to regression modeling.

✨ Core Features

🔍 RFM Feature Engineering

• R Value Calculation: Recency analysis of the most recent purchase interval
• F Value Calculation: Purchase frequency statistics and analysis
• M Value Calculation: Purchase amount aggregation and tiering
• Time Window: Predict long-term value based on short-term data
• Customer Segmentation: Automated customer value tiering

🤖 Regression Algorithm Modeling

• Linear Regression: Basic regression analysis model
• Random Forest: High-performance ensemble learning algorithm
• Model Comparison: Multi-algorithm performance evaluation and comparison
• Cross-Validation: Reliable model performance evaluation
• Hyperparameter Optimization: Automated model tuning

📊 LTV Prediction Engine

• Time Series Forecasting: Predict future LTV based on historical data
• Batch Prediction: Support large-scale batch customer processing
• Confidence Interval: Provide uncertainty assessment of prediction results
• Feature Importance: Explain the key factors affecting LTV

📈 Visualization Analysis

• RFM Distribution Chart: Visualization of customer value distribution
• Prediction Performance Comparison: Scatter plot of actual vs predicted values
• Feature Importance: Analysis of key feature contribution
• Model Performance Comparison: Comparison chart of multiple algorithm performance

📋 Professional Reports

• HTML Report: Interactive analysis report
• Markdown Document: Lightweight analysis summary
• Excel Export: Convenient for business departments
• API Interface: Support system integration calls

🚀 Quick Start

1. Environment Installation

# 安装基础依赖
pip install pandas numpy scikit-learn matplotlib seaborn

# 安装可选依赖（用于高级功能）
pip install xgboost lightgbm joblib openpyxl

2. Basic Usage

from scripts.ltv_predictor import LTVPredictor
from scripts.data_processor import DataProcessor

# 1. 初始化处理器
processor = DataProcessor()
predictor = LTVPredictor()

# 2. 加载和预处理数据
data = processor.load_order_data('your_orders.csv')
rfm_data = processor.calculate_rfm_features(data,
                                          feature_period='3M',
                                          prediction_period='12M')

# 3. 训练LTV预测模型
model_results = predictor.train_models(rfm_data)

# 4. 进行LTV预测
predictions = predictor.predict_ltv(new_customer_data)

# 5. 生成分析报告
report_path = predictor.generate_report(predictions, 'ltv_analysis_report.html')

3. Quick Example

from scripts.quick_analysis import quick_ltv_analysis

# 一键完成完整LTV分析流程
results = quick_ltv_analysis(
    order_data_path='ecommerce_orders.csv',
    feature_period_months=3,
    prediction_period_months=12,
    output_dir='ltv_analysis_results'
)

print(f"最佳模型R²分数: {results['best_model_r2']:.4f}")
print(f"预测客户数: {len(results['predictions'])}")
print(f"分析报告: {results['report_path']}")

📁 Skill Structure

ltv-predictor/
├── scripts/                   # 核心功能模块
│   ├── data_processor.py     # 数据预处理和RFM计算
│   ├── ltv_predictor.py      # LTV预测核心引擎
│   ├── regression_models.py  # 回归算法实现
│   ├── visualizer.py         # 可视化生成器
│   └── report_generator.py   # 报告生成器
├── examples/                 # 示例脚本
│   ├── ecommerce_ltv_analysis.py   # 电商完整分析示例
│   ├── quick_ltv_prediction.py     # 快速预测示例
│   └── model_comparison.py         # 模型对比示例
├── data/                     # 示例数据
│   └── sample_orders.csv     # 示例订单数据
├── tests/                    # 测试脚本
│   ├── test_rfm_analysis.py  # RFM分析测试
│   └── test_prediction.py    # 预测功能测试
├── SKILL.md                  # 技能说明文档
└── README.md                 # 使用说明

🎯 Application Scenarios

🛒 E-commerce Retail

• Customer Value Tiering: Tier customers into gold/silver/bronze based on LTV
• Marketing Budget Allocation: Optimize marketing investment based on LTV prediction results
• Inventory Forecasting: Plan product inventory based on customer value predictions
• Personalized Recommendations: Provide precise recommendations for high-value customers

💰 Financial Services

• Credit Assessment: Combine LTV for customer credit rating
• Product Design: Design differentiated products for customers of different values
• Customer Retention: Identify high-value customers for prioritized maintenance
• Risk Control: Conduct risk assessment based on customer value

🎯 Marketing Strategy

• Customer Acquisition Cost Analysis: Calculate the LTV/CAC ratio for different channels
• Customer Lifecycle Management: Develop full lifecycle marketing strategies
• Repurchase Rate Improvement: Identify low-frequency customers and develop improvement strategies
• Customer Win-back: Predict churn risk and develop win-back plans

⚙️ Configuration Options

RFM Analysis Configuration

config = {
    'feature_period_months': 3,      # 特征计算时间窗口（月）
    'prediction_period_months': 12,  # 预测时间窗口（月）
    'r_weight': 0.2,                 # R值权重
    'f_weight': 0.3,                 # F值权重
    'm_weight': 0.5,                 # M值权重
    'customer_segments': 5           # 客户分层数量
}

Model Training Configuration

config = {
    'test_size': 0.2,               # 测试集比例
    'cv_folds': 5,                  # 交叉验证折数
    'random_state': 42,             # 随机种子
    'enable_hyperparameter_tuning': True,  # 是否调参
    'n_iter_search': 50,            # 超参数搜索次数
    'scoring_metric': 'r2'          # 评估指标
}

Prediction Configuration

config = {
    'confidence_interval': 0.95,    # 置信区间
    'batch_size': 1000,             # 批处理大小
    'feature_importance_threshold': 0.01,  # 特征重要性阈值
    'prediction_uncertainty': True   # 是否计算预测不确定性
}

📊 Data Format Requirements

Order Data Format

订单号,产品码,消费日期,产品说明,数量,单价,用户码,城市
536374,21258,2022-06-01 09:09,绿联usb分线器,32,10.95,15100,北京
536376,22114,2022-06-01 09:32,加大男装T恤,48,50.45,15291,上海

Required Fields:

• 用户码: Unique customer identifier
• 消费日期: Purchase time (supports multiple date formats)
• 数量: Purchase quantity
• 单价: Product unit price

Optional Fields:

• 订单号: Unique order identifier
• 产品码: Product identifier
• 产品说明: Product description
• 城市: Customer city information

🧪 Model Performance Benchmarks

Based on measured data from Lesson 3:

• Data Scale: 37,060 order records, 370 unique customers
• Time Window: 3 months of data to predict 12-month LTV
• Linear Regression: R² = 0.4778 (test set)
• Random Forest: R² = 0.5899 (test set)
• Performance Improvement: 23.4% (relative to linear regression)
• Feature Importance: M value (monetary) contributes 78.53%, F value (frequency) contributes 16.32%

🔧 Advanced Features

Automated Feature Engineering

• Time series feature generation
• Sliding window computation
• Seasonal pattern recognition
• Outlier detection and handling

Model Interpretability

• SHAP value analysis
• Partial dependence plots
• Feature interactions
• Prediction path tracing

Business Insights

• Customer value trend analysis
• Product association analysis
• Geographic value distribution
• Temporal value patterns

📋 Best Practices

Data Quality

• Ensure temporal continuity of order data
• Handle missing values and outliers
• Verify uniqueness of customer identifiers
• Check data time coverage

Model Selection

• Prefer linear regression for small datasets
• Recommend random forest or XGBoost for large datasets
• Choose linear models when model interpretability is important
• Use ensemble learning when pursuing prediction accuracy

Business Application

• Retrain the model regularly (monthly recommended)
• Adjust prediction results in combination with business rules
• Establish model monitoring and alerting mechanisms
• Continuously track prediction accuracy

🔄 Changelog

v1.0.0 (2025-01-19)

• Initial version release
• Complete RFM analysis functionality
• Linear regression and random forest algorithms
• Basic visualization and reporting functionality
• E-commerce order data support

Future Plans

• Support more regression algorithms (XGBoost, LightGBM)
• Add deep learning models
• Real-time prediction API
• More industry data templates
• Automated model deployment

🤝 Contribution Guide

Contributions of code, issue reports, or improvement suggestions are welcome:

1. Fork the project
2. Create a feature branch
3. Commit changes
4. Open a Pull Request

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgments

• The theoretical foundation provided by "Data Analysis with Kage: Ten Talks"
• The machine learning algorithms provided by Scikit-learn
• The data processing capabilities provided by Pandas and NumPy
• The support and feedback from the data science community

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With this skill, you can: ✅ Quickly perform customer RFM analysis ✅ Build accurate LTV prediction models ✅ Gain interpretable business insights ✅ Generate professional analysis reports ✅ Support data-driven business decisions