unit-economics

You can analyze revenue quality and customer economics by ingesting CSV data to compute LTV/CAC, net retention, and cohort metrics. This process generates cohort-based insights to help private equity professionals evaluate portfolio or target company health.

An ARR cohort waterfall visualizes retention behavior by customer vintage. It decomposes recurring revenue movements across cohorts, allowing you to track expansion, contraction, and churn within specific customer acquisition timeframes to assess overall revenue stability.

Yes, you can calculate LTV and CAC metrics using Python with pandas and numpy. The analysis processes raw customer data to compute lifetime value and acquisition costs, producing metrics tailored for private equity revenue quality assessments.

To decompose net revenue retention for a trailing 12-month period, you analyze cohort data to separate expansion, downgrade, and churn components. This breakdown reveals the underlying drivers of net dollar retention over the past year.

Yes, cohort analysis works for assessing private equity target companies by profiling cohort data and generating a revenue quality scorecard. It evaluates customer economics and net retention metrics to support informed investment decisions.

The best way to visualize customer retention metrics using matplotlib is by building an ARR cohort waterfall. This approach graphs retention behavior by vintage, allowing you to visually compare revenue quality across different customer groups.