Definition
A funnel models a sequence of steps users take (visit -> signup -> activate -> pay) and the conversion rates between steps.
How to use it
- Use funnels to identify the largest drop-off points.
- Analyze by segment (channel, device, geo) to find specific issues.
- Track time between steps to spot friction, not just conversion rates.
Common mistakes
- Using different definitions of each step across reports.
- Optimizing one step while hurting overall conversion quality.
Why this matters
This term matters because small changes compound in SaaS metrics. Use consistent definitions by cohort and segment so you can diagnose retention, payback, and growth quality.
Practical checklist
- Write a 1-line definition for "Funnel" that your team will use consistently.
- Keep the time window consistent (weekly/monthly/quarterly) when comparing trends.
- Segment results (channel/plan/cohort) before drawing big conclusions from blended averages.
- Use a calculator that references this term (e.g., Activation Rate Calculator) to sanity-check assumptions.
- Read the related guide (e.g., Activation rate: definition, formula, and how to improve activation) for context and common pitfalls.
Where to use this on MetricKit
Calculators
- Activation Rate Calculator: Compute activation rate: what % of new signups reach your activation event (and what you need to hit a target).
- Trial-to-paid Conversion Calculator: Compute trial-to-paid conversion rate and estimate required conversions to hit a target.
Guides
- Activation rate: definition, formula, and how to improve activation: Activation rate explained: how to define activation, the activation rate formula, and practical ways to improve activation without vanity metrics.
- Trial-to-paid conversion: definition, formula, and how to improve it: Trial-to-paid conversion explained: how to calculate it, choose a window, and improve conversion without harming retention.
- PLG metrics hub: activation, trial conversion, stickiness, and adoption: A practical hub for product-led growth metrics: activation rate, trial-to-paid, DAU/MAU and WAU/MAU stickiness, feature adoption, and PQL-to-paid conversion.