Definition
A PLG funnel is a structured view of how users move from discovering your product to activating, engaging, and eventually expanding their usage.
Instead of focusing only on marketing or sales stages, a PLG funnel is anchored in in-product milestones and behaviors.
Common stages in a PLG funnel
Typical PLG funnel stages include: acquisition, signup or self-serve signup, onboarding, activation, feature adoption, and expansion.
Many teams also add intermediate stages such as "aha moment" or PQL creation, depending on their product.
Some products add a "habit" stage between activation and expansion to track when users become regular users.
PLG funnel vs traditional sales funnel
Traditional funnels: Lead → MQL → SQL → Opportunity → Closed Won. Stages are based on marketing and sales activities.
PLG funnels: Visitor → Signup → Activated → Engaged → PQL → Expanded. Stages are based on product usage.
PLG funnels put the product at the center, measuring whether users are actually getting value.
In a traditional funnel, a "qualified" lead is someone who expressed interest through a form fill or took a sales call. In a PLG funnel, a qualified lead is someone who has demonstrated value realization through product usage.
Traditional funnels are controlled by marketing and sales teams. PLG funnels require product, engineering, and growth teams to collaborate because the product itself is the primary conversion mechanism.
The PLG funnel does not eliminate the need for sales—it changes when and how sales gets involved. Instead of creating interest from scratch, sales engages users who have already experienced value and are ready to expand.
Key metrics at each funnel stage
Acquisition: Track visitor volume, traffic sources, and cost per visitor. Understand which channels bring users who are most likely to activate, not just the most total visitors.
Signup: Measure signup conversion rate (visitors to signups), time to complete signup, and signup drop-off by step. Each additional form field or verification step reduces conversion.
Onboarding: Track onboarding completion rate, time to complete onboarding, and step-by-step drop-off rates. Identify which steps lose the most users.
Activation: Measure activation rate (signups who reach the activation event), time to activation, and the correlation between activation and long-term retention.
Engagement: Track daily/weekly/monthly active users, feature adoption breadth, and usage frequency. Look for the habits that indicate a user has integrated the product into their workflow.
Expansion: Measure PQL conversion rate, expansion revenue per account, and time from activation to expansion. This is where PLG revenue growth happens.
Common PLG funnel leaks and how to fix them
Leak: High visitor-to-signup drop-off. Fix: Simplify your signup flow, add social login options, remove unnecessary form fields, and ensure your landing page clearly communicates the value users will get after signing up.
Leak: Users sign up but never start onboarding. Fix: Send a well-timed welcome email within minutes, use in-app prompts to guide first actions, and reduce the gap between signup and the first meaningful action.
Leak: Users start onboarding but drop off before activation. Fix: Shorten the onboarding path, use progress indicators to show how close users are to completion, offer skip options for non-essential steps, and provide sample data so users can experience value before committing their own data.
Leak: Users activate but do not return. Fix: Set up re-engagement triggers (email, push notifications) based on inactivity, investigate whether the activation event truly correlates with long-term value, and look for missing "habit loops" that would bring users back regularly.
Leak: Engaged users do not convert to paid. Fix: Review your pricing and packaging to ensure the free-to-paid boundary aligns with natural expansion points, surface upgrade prompts at moments of value rather than arbitrary limits, and ensure sales has visibility into PQL signals.
Measuring your PLG funnel
Track conversion rates between each stage to identify bottlenecks.
Measure time spent in each stage to identify where users stall.
Segment funnel metrics by acquisition channel, use case, and plan to understand which paths work best.
Build a single dashboard that shows the full funnel with conversion rates between each stage. Review it weekly as a cross-functional team.
Implementation notes
- Define clear, measurable events for each funnel stage before building dashboards.
- Start with a simple 4–5 stage funnel and add complexity only when you have clear hypotheses to test.
- Review funnel metrics weekly to spot trends early.
- Assign an owner to each funnel stage so there is clear accountability for improving conversion at that step.
- Use cohort analysis within each funnel stage to track whether conversion rates are improving over time, not just looking at aggregate numbers.