• What is feature usage analytics?

  A plain-language guide to feature usage analytics: what it is, how it differs from event analytics, and why product teams use it to decide what to build, fix, or drop.

• The vocabulary of feature usage analytics

  A plain-language guide to the terms you meet in feature usage analytics — event, attribute, segment, cohort, adoption, retention — built up in the order they depend on each other, with a reference table at the end.

• Measuring feature usage: adoption, active users, retention, churn

  The core metrics of feature usage analytics explained in plain language — adoption, active users, unique users vs. usage count, frequency, retention, and churn — plus two pairs of metrics that are easy to mistake for each other.

• Segment, cohort, dimension: terms people mix up

  The pairs of analytics terms that get confused most often — segment vs. cohort, event vs. feature, attribute vs. segment, active users vs. usage count, adoption vs. engagement, user vs. account — each explained with the distinction that matters.

• Feature usage analytics vs. event analytics

  Two different ways to measure what users do in your product. Feature usage analytics is feature-centric and decision-ready; event analytics is flexible but high-effort. Here's how they differ and when to use each.

• How to Choose Segments for Feature Usage Analytics

  Which dimensions should you split usage data into before sending it to UsageLens? A guide to choosing segments by application type — SaaS, desktop, mobile, on-premise, open-source — and by the business properties that matter most.