The PLG metrics dashboard: 12 metrics every product-led team should track

Key takeaways

• PLG teams need a measurement system built around the product, organized into four layers: acquisition, activation, engagement, and revenue
• The 12 essential metrics include signup rate and traffic-to-signup conversion (acquisition); activation rate and time-to-value (activation); DAU/MAU ratio and feature adoption (engagement); plus revenue metrics like NRR, CAC payback, and expansion revenue
• Activation can be a useful leading indicator; validate its definition against later retention cohorts and prioritize the constraint shown by your own funnel
• External bands are not interchangeable across definitions; establish cohort baselines, product cadence and a stable measurement window before setting targets

Sales-led companies track pipeline, win rates, and quota attainment. Product-led companies need a fundamentally different measurement system because the product is the primary driver of acquisition, activation, and expansion.

Most PLG teams get it wrong in one of two ways: too few metrics (just MRR and signups), or too many (a 40-metric dashboard nobody reads). This post lays out the 12 metrics that actually matter, organized into four layers, with benchmarks and practical guidance on how to improve each one.

For a broader overview of PLG metrics, start there. This post goes deeper into each metric and how to build a dashboard around them.

Why PLG needs different metrics

In a sales-led model, a human guides the buyer through evaluation, negotiation, and onboarding. Metrics reflect that: pipeline velocity, average deal size, sales cycle length.

In PLG, the product does most of that work. Users self-discover, self-evaluate, and self-onboard. Metrics need to capture how well the product performs each of those jobs:

• Acquisition: Is the product attracting the right users?
• Activation: Are new users reaching value quickly enough?
• Engagement: Are activated users building habits?
• Revenue: Is usage converting to revenue and expanding over time?

These four layers form the structure of your PLG dashboard.

Layer 1: Acquisition metrics

1. Signup rate

Definition: The percentage of website visitors who create an account.

Comparison: Track a fixed eligible cohort and conversion window by acquisition source and offer.

How to improve:

• Reduce signup form fields to the absolute minimum (email only, or SSO)
• Show the product in action on your landing page (screenshots, interactive demos, video)
• A/B test your CTA copy. "Start free" consistently outperforms "Request a demo" for PLG
• Remove credit card requirements for trial signups

Why it matters: A low signup rate means either your messaging is off, your target audience is wrong, or your signup flow has too much friction. Fix this before optimizing anything downstream.

2. Traffic-to-signup conversion

Definition: Unique visitors divided by new signups, segmented by acquisition channel.

Comparison: Compare channels using the same signup definition, attribution rule and observation window.

How to improve:

• Segment by channel to find which sources bring high-intent traffic
• Create dedicated landing pages for each major acquisition channel
• Align ad copy with the actual product experience to reduce expectation gaps

Layer 2: Activation metrics

3. Activation rate

Definition: The percentage of new signups who complete the key actions required to experience core product value. See the full definition at activation rate.

Comparison: Use a stable activation event and window, then compare cohorts and segments.

How to improve:

• Define a clear activation event (not "logged in" but "completed first meaningful action")
• Reduce the number of steps between signup and activation
• Add onboarding checklists that guide users to activation milestones
• Use progressive disclosure: show only what the user needs now, hide advanced features

Why it matters: This is the single most important metric on this list. Compare retention for activated and non-activated cohorts, then validate the candidate event on a later cohort; association alone does not identify the cause.

4. Time-to-value (TTV)

Definition: The elapsed time between a user signing up and completing their activation event.

Benchmark: Under 5 minutes for simple tools. Under 24 hours for complex B2B products. Under 3 days for products requiring data imports or integrations.

How to improve:

• Pre-populate accounts with sample data so users see value before importing their own
• Offer templates, wizards, or guided setup flows
• Defer non-essential configuration (settings, billing, profile details) until after activation
• Track TTV by cohort to see if product changes are actually reducing it

Layer 3: Engagement metrics

5. DAU/MAU ratio

Definition: Daily active users divided by monthly active users. A measure of how frequently users return. More detail at DAU/MAU ratio.

Comparison: Interpret DAU/MAU against the product’s intended cadence. A monthly workflow should not inherit a daily-use target.

How to improve:

• Build features that create daily usage habits (notifications, digests, dashboards)
• Identify your most engaged users and study what they do differently
• Add collaborative features that give users reasons to return (comments, shared workspaces)

Why it matters: A high DAU/MAU ratio means your product is part of the user's daily workflow. A low ratio means users check in occasionally but it is not sticky enough to drive reliable retention or expansion.

6. Feature adoption rate

Definition: The percentage of active users who use a specific feature within a given time period.

Comparison: Define eligible users for each feature and evaluate adoption alongside frequency, outcome and strategic purpose. Low usage alone does not prove a feature should be removed.

How to improve:

• Surface underused features contextually (when the user would benefit from them, not on first login)
• Add feature discovery prompts after users complete related actions
• Remove or simplify features with near-zero adoption

7. Breadth of use

Definition: The number of distinct features or modules used per user or account over a 30-day period.

Benchmark: Varies by product complexity. Track the trend, not the absolute number.

How to improve:

• Cross-promote related features within the product
• Create workflows that naturally lead users through multiple features
• Use onboarding to expose users to 3-4 core features, not just one

Layer 4: Revenue metrics

8. Trial-to-paid conversion

Definition: The percentage of free trial users who convert to a paid plan.

Comparison: Measure conversion and retained revenue for each offer with the same eligibility and follow-up rules.

How to improve:

• Ensure users hit activation before the trial expires
• Send trial expiration reminders with a summary of value received
• Offer annual billing at a discount to reduce decision friction
• Consider extending trials for users who are active but have not activated yet

9. Expansion revenue

Definition: Revenue generated from existing customers through upgrades, add-ons, or increased usage. See expansion revenue for the full definition.

Comparison: Report expansion separately from new-logo ARR and assess mix against your model and prior periods.

How to improve:

• Design usage limits that align with value (when users hit limits, they have already experienced enough value to justify paying more)
• Offer team plans with per-seat pricing that grows with adoption
• Build features that unlock at higher tiers and are visible but gated in lower tiers
• Track which features drive upgrades and invest in those

10. Net revenue retention (NRR)

Definition: The percentage of revenue retained from existing customers after accounting for expansion, contraction, and churn. Full definition at NRR.

Interpretation: 100% means expansion exactly offsets contraction and churn in starting recurring revenue. It does not mean the business is profitable. Compare consistently defined cohorts and periods.

How to improve:

• Focus on expansion revenue (covered above)
• Reduce involuntary churn (failed payments, expired cards)
• Identify at-risk accounts early using product usage signals
• Build retention loops that keep users engaged over time

11. Revenue per user (ARPU)

Definition: Total revenue divided by total users (or accounts) in a given period.

Benchmark: Depends entirely on your market. Track the trend and segment by cohort.

How to improve:

• Identify your highest-ARPU segments and acquire more of them
• Build pricing tiers that capture willingness-to-pay across segments
• Increase feature usage breadth, which correlates with higher ARPU

12. Payback period

Definition: The number of months it takes to recover the cost of acquiring a customer.

Comparison: Calculate payback from contribution margin and fully loaded acquisition cost, then compare with your cash constraints, contract term and prior cohorts. A PLG label does not guarantee lower CAC or shorter payback than a sales-led motion.

How to improve:

• Reduce CAC by investing in organic acquisition channels
• Increase first-year revenue through faster activation and earlier expansion
• Shorten time-to-paid by optimizing trial-to-paid conversion

Building the dashboard

Tool selection

You do not need a custom-built analytics platform. Most PLG teams use one of these:

• Amplitude: Best for product analytics with built-in funnel and cohort analysis. Strong for activation and engagement metrics.
• Mixpanel: Similar to Amplitude, with good event-based tracking. Slightly easier to set up for smaller teams.
• PostHog: Open-source alternative with session recording, feature flags, and product analytics in one platform. Good for teams that want to own their data.
• Looker / Metabase + data warehouse: Best for teams that want full control over metric definitions and need to combine product data with revenue data from Stripe or billing systems.

Dashboard layout

Organize your dashboard into the four layers. For each metric, show:

• Current value (this month or rolling 30 days)
• Trend (last 3-6 months, line chart)
• Segmentation (by plan, by acquisition channel, by user role)

Put activation rate and NRR at the top. These are the two metrics that most directly predict long-term PLG health.

Review cadence

• Weekly: Signup rate, activation rate, trial-to-paid conversion
• Monthly: All 12 metrics, with cohort analysis on activation and retention
• Quarterly: NRR decomposition (expansion, contraction, churn) and payback period trends

Common mistakes

Tracking too many metrics: If your dashboard has more than 15 metrics, nobody will look at it. Start with the 12 listed here and add only when you have a specific question a new metric would answer.

Vanity metrics: Signups, page views, and total registered users feel good but do not indicate product health. Always pair volume metrics with quality metrics (signup rate with activation rate, total users with DAU/MAU).

No segmentation: Averages hide the truth. A 25% activation rate might mean enterprise users activate at 45% and SMB users at 10%. Without segmentation, you will optimize for the wrong audience.

Measuring too late: If you wait until revenue metrics are declining to investigate, you have missed 2-3 months of upstream signals. Activation rate and engagement metrics lead; revenue lags behind them.

Not connecting metrics to actions: Every metric on your dashboard should have an owner and a hypothesis for how to improve it. A dashboard without accountability is just a screensaver.

The best PLG dashboards keep every number tied to a team, an initiative, and an outcome someone owns. Metric count has nothing to do with it.