Which machine learning models are supported?

It currently supports Linear Regression and Random Forest models, providing automated performance comparisons and R² scoring.

Does it provide visual insights?

Yes, the skill generates RFM distribution charts, actual vs. predicted value scatter plots, and feature importance visualizations.

What data format is required for LTV Predictor?

The skill requires transaction data (typically CSV) containing unique user IDs, purchase dates, quantities, and unit prices.

Can I adjust the prediction timeframe?

Yes, you can configure specific months for both the feature observation period and the future prediction window to suit your business cycle.

LTV Predictor & RFM Analytics

Name: LTV Predictor & RFM Analytics
Author: liangdabiao

byliangdabiao

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数据科学与机器学习

Predicts customer lifetime value and performs RFM analysis using regression algorithms for e-commerce and retail data.

关于

LTV Predictor is a specialized data science tool that automates the process of calculating Customer Lifetime Value (LTV) through RFM (Recency, Frequency, Monetary) modeling. It transforms raw transaction logs into actionable business intelligence by performing automated feature engineering and training machine learning models like Random Forest and Linear Regression. This skill is ideal for growth teams and analysts who need to segment their customer base, optimize marketing spend based on predicted revenue, and generate professional visual reports without manual data processing.

主要功能

Interactive visualization of customer value distribution and model performance metrics
Multi-model regression analysis including Linear Regression and Random Forest algorithms
Comprehensive reporting in HTML, Markdown, and Excel formats for business stakeholders
Customizable time-window configurations for both feature observation and value prediction
Automated RFM feature engineering from raw transaction and order history data
78 GitHub stars

使用场景

Optimizing marketing ROI by identifying and targeting high-LTV customer segments
Forecasting future revenue trends based on historical purchase patterns and frequency
Developing retention strategies by identifying at-risk high-value customers through RFM scoring

关于

主要功能

Interactive visualization of customer value distribution and model performance metrics
Multi-model regression analysis including Linear Regression and Random Forest algorithms
Comprehensive reporting in HTML, Markdown, and Excel formats for business stakeholders
Customizable time-window configurations for both feature observation and value prediction
Automated RFM feature engineering from raw transaction and order history data
78 GitHub stars

使用场景

Optimizing marketing ROI by identifying and targeting high-LTV customer segments
Forecasting future revenue trends based on historical purchase patterns and frequency
Developing retention strategies by identifying at-risk high-value customers through RFM scoring