Skene
PLG term

Net revenue retention (NRR)

Net revenue retention (NRR) measures how your existing customers grow or shrink over time. It looks at starting recurring revenue from a cohort, subtracts churn and contraction, and adds expansion from upsells and cross-sells. In strong PLG companies, NRR above 100% signals that product usage is driving enough expansion to more than offset churn.

Revenue
Also called: Net dollar retention, NDR
About this term

This page is part of the Skene PLG glossary. Use it as a reference when writing specs, dashboards, or playbooks that rely on this concept.

Canonical glossary index: /resources/glossary

Definition and formula

Net revenue retention (NRR) tells you how your recurring revenue from existing customers changes over a period, usually a month, quarter, or year.

A common formula is: (Starting MRR + Expansion MRR – Churned MRR – Contraction MRR) ÷ Starting MRR.

How to interpret NRR in PLG

NRR above 100% means your existing customers are growing their spend over time; NRR below 100% means churn and downgrades outweigh expansions.

High NRR is often a hallmark of mature PLG companies where product usage naturally leads to more seats, more usage, or higher-value plans.

Levers to improve net revenue retention

Increase expansion by tying pricing to usage or outcomes that grow as customers succeed (e.g., seats, transactions, active projects).

Reduce churn by improving activation, onboarding journeys, and feature adoption so more customers reach and maintain value.

How Skene influences NRR

Skene measures completion of key journeys and milestones that are often precursors to expansion events, such as adding users, enabling integrations, or rolling out to new teams.

By making these product signals visible, Skene helps you identify at-risk accounts early and design expansion plays rooted in real product usage.

Implementation notes

  • Always analyze NRR by segment (e.g., by plan, industry, or company size) to understand where PLG is strongest or weakest.
  • Pair NRR with leading indicators like activation rate, time-to-value, and feature adoption to move from lagging to leading insights.