[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-retention-curve::en":3,"gloss-cluster-retention-curve::en":23,"gloss-next-retention-curve::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"retention-curve","analytics","Retention Curve","A retention curve plots the percentage of a cohort still active N days (or weeks) after they signed up, tracing how usage decays over time. Its shape tells you more than any single number: a curve that keeps sloping toward zero means you have a leaky bucket and no product-market fit, while one that *flattens* into a horizontal plateau means a stable core of users found lasting value — the classic sign builders look for. The height of that plateau is roughly your ceiling for organic growth. Two definitions matter: N-day retention (active on exactly day N, strict) versus unbounded\u002Frange retention (active anytime in a window, more forgiving) — pick one and label it, because they produce very different-looking curves. Practical note: measure retention on your genuine core action (the thing that delivers value), not just logins, and always segment by cohort and acquisition source, since a blended curve hides that one channel is churning while another sticks. Flattening beats a high day-one number.","A retention curve plots what share of a cohort is still active N days after signup — a curve that flattens means product-market fit, one sloping to zero doesn't.",null,[11,14,17,20],{"slug":12,"name":13},"churn","Churn",{"slug":15,"name":16},"cohort","Cohort",{"slug":18,"name":19},"north-star-metric","North Star Metric",{"slug":21,"name":22},"product-analytics","Product Analytics",[24,28,31,35,38,41,44,47,50,53,56,59],{"slug":25,"category":5,"name":26,"updated_at":27},"ab-testing","A\u002FB Testing","2026-08-24T02:46:38+00:00",{"slug":29,"category":5,"name":30,"updated_at":27},"active-user","Active User (DAU, WAU, MAU)",{"slug":32,"category":5,"name":33,"updated_at":34},"autocapture","Autocapture","2026-08-24T02:46:37+00:00",{"slug":36,"category":5,"name":37,"updated_at":27},"cost-per-resolution","Cost per Resolution",{"slug":39,"category":5,"name":40,"updated_at":34},"customer-data-platform","Customer Data Platform (CDP)",{"slug":42,"category":5,"name":43,"updated_at":27},"deflection-rate","Deflection Rate",{"slug":45,"category":5,"name":46,"updated_at":27},"guardrail-metric","Guardrail Metric",{"slug":48,"category":5,"name":49,"updated_at":34},"identity-resolution","Identity Resolution",{"slug":51,"category":5,"name":52,"updated_at":34},"multi-touch-attribution","Multi-Touch Attribution",{"slug":54,"category":5,"name":55,"updated_at":27},"novelty-effect","Novelty Effect",{"slug":57,"category":5,"name":58,"updated_at":27},"sample-ratio-mismatch","Sample Ratio Mismatch (SRM)",{"slug":60,"category":5,"name":61,"updated_at":27},"seat-utilization","Seat Utilization"]