PLG metrics

Traditional sales metrics focus on opportunity stages and deal values. PLG metrics focus on user and account behavior over time: what people do in the product, how often, and with what outcomes.

Because the product experience drives growth in PLG, you need metrics that describe that experience directly instead of only tracking what happens in CRM stages.

At the funnel level, you can map acquisition, onboarding, activation, retention, and expansion to concrete metrics such as signup-to-activation rate, time-to-activation, and expansion revenue.

At the loop level, you track recurring behaviors such as weekly active usage, feature adoption, collaboration events, and upgrade triggers to see whether value compounding is actually happening.

Acquisition: total signups, visitor-to-signup conversion, and volume of product-qualified leads (PQLs) by segment or channel.

Activation: activation rate at the user and account level, time-to-activation, and activation broken down by persona, plan, or acquisition source.

Retention: N-day retention, cohort-based retention, DAU/WAU/MAU ratios, and stickiness (for example DAU/MAU).

Monetization and expansion: free-to-paid conversion, ARPU or ARPA, expansion revenue, and net revenue retention (NRR).

For AI products, you also need metrics that describe model quality and user trust: acceptance rates for AI outputs, correction or override rates, and the share of outputs that lead to successful downstream actions.

Qualitative signals such as user feedback, satisfaction ratings, and support tickets about AI behavior should be tracked alongside quantitative metrics, not treated as anecdotes.

In early stages, focus on a minimal set: activation rate, time-to-activation, a small number of retention points (such as D7 and D30), and a qualitative understanding of why users stay or leave.

As you scale, add more segmentation, cohort analysis, and unit economics so you can see which segments and use cases are driving sustainable growth and which are burning resources.

Define a small, stable set of core events that map to your journeys and milestones, and keep naming conventions consistent so that analyses remain interpretable over time.

Treat data quality as part of the product: missing or inconsistent events are PLG bugs because they prevent you from seeing and improving how the system behaves.

Use metrics to identify the current bottleneck (acquisition, activation, retention, or expansion) before choosing tactics. Optimizing a stage that is not the constraint rarely moves the business.

Design experiments with explicit primary and secondary metrics, plus clear time windows and cohort definitions, so you can distinguish real effects from noise and avoid overfitting to short-term changes.