ltv-predictor
CommunityPredict LTV with RFM-powered models.
System Documentation
What problem does it solve?
This Skill automates Customer Lifetime Value analysis by turning raw order data into RFM features and LTV predictions, reducing manual data wrangling and modeling time.
Core Features & Use Cases
- RFM feature engineering: compute R, F, M and segment customers.
- Multiple regression models: linear regression and random forest with model comparison and CV.
- Batch predictions & reports: generate LTV predictions for many customers and export HTML/Markdown/Excel reports.
- Business impact: enables data-driven lifecycle management, targeted marketing, and retention strategies.
Quick Start
Quick Start: Use the LTV Predictor to load order data, compute RFM features, train models, and predict LTV for new customers with a single command. For example: from scripts.quick_analysis import quick_ltv_analysis; quick_ltv_analysis(file_path='data/sample_orders.csv', feature_period_months=3, prediction_period_months=12, output_dir='./ltv_results')
Dependency Matrix
Required Modules
Components
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: ltv-predictor Download link: https://github.com/liangdabiao/claude-data-analysis-ultra-main/archive/main.zip#ltv-predictor Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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