Features

This chapter covers the core features and capabilities of Payment Analytics.

Features

• Monthly Recurring Revenue (MRR) with breakdown: new, expansion, contraction
• Annual Recurring Revenue (ARR) and average revenue per customer (ARPC)
• Customer churn rate and revenue churn rate
• Customer Lifetime Value (LTV): average, median, and P90
• Failed payment analysis with error code ranking and revenue at risk
• Revenue forecasting with month-over-month growth projection
• Full dashboard combining all metrics in one report
• Built-in demo with 12 months of realistic simulated data
• Python stdlib only — zero dependencies

Quick Start

# Run the full demo (generates sample data automatically)
python src/payment_analytics.py --action demo

# Full dashboard from your data
python src/payment_analytics.py --action dashboard --input examples/payments.json

# MRR breakdown
python src/payment_analytics.py --action mrr --input examples/payments.json

# Churn analysis
python src/payment_analytics.py --action churn --input examples/payments.json

# Customer lifetime value
python src/payment_analytics.py --action ltv --input examples/payments.json

# Failed payment analysis
python src/payment_analytics.py --action failures --input examples/payments.json

# Revenue forecast (next 12 months)
python src/payment_analytics.py --action forecast --input examples/payments.json --months 12

CLI Reference

Follow this guide to get Payment Analytics up and running in your environment.

CLI Reference

Input Format

Payment records should be a JSON array (or {"payments": [...]}) where each record has: