How to Measure Product-Led Growth (PLG) Funnel Metrics

Why a Unified Data Approach Matters

Product-led growth spans multiple tools and departments. Marketing might track website sign-ups, product teams log in-app events, sales/RevOps manage CRM and billing systems. If these data streams stay siloed, no one gets the full story. RevOps and executives need a single source of truth that blends all relevant data to understand how users move through the funnel.

Combining data from CRM, marketing analytics, product usage logs, and billing systems into one analysis environment provides a complete, end-to-end view of the PLG funnel. By unifying data sources, you can trace a customer's journey from first touch all the way to revenue expansion.

For example, you might merge CRM lead data (e.g. Salesforce or HubSpot), product event data (in-app behaviors), marketing data (campaign or website analytics), and billing/subscription data (from Stripe or your billing database). This allows you to calculate funnel conversion rates and identify drop-offs that wouldn't be visible in any single system alone.

Acquisition (Sign-Ups and Lead Generation)

Key metrics to track:

• Sign-up volume – How many new users sign up in a given period (daily, weekly, monthly).
• Conversion rate – The percentage of website visitors or leads that convert to sign-ups. For example, visitor-to-signup conversion or lead-to-trial conversion.
• Source attribution – Where are the sign-ups coming from (e.g. organic, paid ads, referrals)? Measuring sign-ups by source helps identify high-quality channels.

Data sources to combine:

Acquisition metrics require blending marketing and sales data with product sign-up data. You might pull website analytics (e.g. Google Analytics or marketing platform data for sessions and traffic sources) and ad campaign data (for spend and clicks), then join that with product database records or CRM leads that represent actual sign-ups.

How to measure (example):

A practical approach is to create a funnel report showing the drop-off from website visit → sign-up → activation (and later stages). In SQL, you might join your web analytics data with your user database.

SELECT s.source, COUNT(*) AS signups
FROM signups s
GROUP BY s.source;

This yields the number of sign-ups by source. You could further join to a marketing spend table to compute cost per acquisition, or join to activation data to see quality. The main point is to connect the dots between marketing input and product sign-up output.

Activation (First Value Moment)

Key metrics to track:

• Activation rate – The percentage of new sign-ups who achieve the key activation action within a given timeframe.
• Time-to-activate – How long it takes on average for users to reach the activation milestone after sign-up.
• PQL (Product-Qualified Lead) volume – In many PLG companies, an activated user who meets certain criteria becomes a Product-Qualified Lead.

Data sources to combine:

Measuring activation requires product usage data above all – you need to know whether a user performed the key action. This usually comes from your product's event tracking (e.g. an event like "Project Created" or "Invite Sent" logged in an analytics database or data warehouse).

How to measure (example):

To compute activation rate, you can use SQL to identify which users performed the activation event:

SELECT 
  COUNT(DISTINCT user_id) AS signups,
  COUNT(DISTINCT CASE WHEN e.event_name = 'PROJECT_CREATED' THEN user_id END) AS activated_users
FROM users u
LEFT JOIN events e ON u.id = e.user_id 
  AND e.event_name = 'PROJECT_CREATED' 
  AND e.timestamp <= u.signup_date + INTERVAL '7' DAY;

In this example, we join a users table with an events table and count how many users had the "project created" event within 7 days of signing up. This would give a 7-day activation rate.

Engagement (Ongoing Product Usage)

Key metrics to track:

• Active users – Count of users active in a given period (DAU, WAU, MAU).
• Engagement depth – How much activity users perform, such as number of sessions, actions, or time spent.
• Feature adoption – Usage of specific important features or modules.
• DAU/MAU ratio (stickiness) – The ratio of daily active to monthly active users, which indicates usage frequency.

How to measure (example):

A simple way to measure engagement is to calculate active user counts over time:

SELECT DATE_TRUNC('week', event_date) AS week, 
       COUNT(DISTINCT user_id) AS weekly_active_users
FROM product_events
WHERE event_date >= CURRENT_DATE - INTERVAL '12 weeks'
GROUP BY DATE_TRUNC('week', event_date);

To measure feature adoption, you would filter by specific event types:

SELECT COUNT(DISTINCT user_id) AS users_shared_project
FROM product_events
WHERE event_name = 'SHARE_PROJECT'
  AND event_date >= CURRENT_DATE - INTERVAL '30' DAY;

Retention (Ongoing Retention and Churn)

Key metrics to track:

• User retention rate – The percentage of users who remain active after a certain time period.
• Churn rate – The opposite of retention. For product usage, you might define churn as not returning to the app for a prolonged period.
• Revenue retention (RR) – For paid customers, track retention in terms of revenue including Gross Revenue Retention (GRR) and Net Revenue Retention (NRR).

How to measure (example):

To calculate user retention cohort-wise, you can use a self-join or window functions in SQL:

-- Cohort retention example: users active in January vs still active in February
WITH january_users AS (
  SELECT DISTINCT user_id 
  FROM product_events 
  WHERE DATE_TRUNC('month', event_date) = '2025-01-01'
),
feb_active AS (
  SELECT DISTINCT user_id 
  FROM product_events 
  WHERE DATE_TRUNC('month', event_date) = '2025-02-01'
)
SELECT 
  COUNT(*) AS users_active_in_jan,
  COUNT(CASE WHEN u.user_id IN (SELECT user_id FROM feb_active) THEN 1 END) AS also_active_in_feb
FROM january_users u;

Expansion (Upsells, Conversions, and Revenue Growth)

Key metrics to track:

• Free-to-paid conversion rate – In a freemium or free-trial model, track what percentage of users convert from free usage to a paid plan.
• Upgrade rate / Upsell count – How many customers upgrade their plan or purchase add-ons in a given period.
• Expansion MRR/ARR – The amount of new revenue generated from existing customers through expansions.
• Net Revenue Retention (NRR) – If NRR > 100%, it implies expansion revenue exceeded churn.

How to measure (example):

A straightforward analysis is the free-to-paid conversion funnel:

SELECT COUNT(DISTINCT user_id) AS trial_users,
       COUNT(DISTINCT CASE WHEN plan = 'Paid' THEN user_id END) AS converted_to_paid
FROM user_plans
WHERE signup_date BETWEEN '2025-01-01' AND '2025-03-31';

For expansion within the customer base, you might look at accounts exceeding their plan limits:

SELECT a.account_id, a.plan_name, a.current_license_limit,
       COUNT(DISTINCT u.user_id) AS active_users
FROM accounts a
JOIN product_usage u ON a.account_id = u.account_id 
  AND u.activity_date >= CURRENT_DATE - INTERVAL '30' DAY
GROUP BY a.account_id, a.plan_name, a.current_license_limit
HAVING COUNT(DISTINCT u.user_id) > a.current_license_limit;

Putting It All Together

Measuring PLG funnel metrics is a cross-functional effort. By looking at Acquisition, Activation, Engagement, Retention, and Expansion in sequence, you gain visibility into where your product-led motion is thriving and where it needs attention.

The true power comes from connecting these stages through data: