PQL Scoring: Identifying Product-Qualified Leads from Usage Data

Your product has 10,000 free users. Sales wants to know which ones to call.

You could hand them the entire list. They'd waste weeks chasing students, tire-kickers, and people who signed up once and never returned. Or you could identify the 50 users showing buying signals based on actual product behavior.

That's the power of Product-Qualified Lead (PQL) scoring. It separates users genuinely evaluating your product from casual browsers, giving sales a qualified pipeline generated from product usage rather than marketing forms.

Most companies get PQL scoring wrong. They either make it too simple (anyone who hits a usage threshold) or too complex (16-variable models nobody understands). The effective middle ground combines behavioral signals, firmographic data, and engagement patterns into a clear, actionable score.

Here's how to build PQL scoring that actually identifies buyers.

Why Traditional MQL Scoring Fails for PLG

Marketing-Qualified Leads (MQLs) score prospects based on demographic data and content engagement. Downloaded three whitepapers? MQL. Attended a webinar? MQL. Works for companies over 500 employees? MQL.

This approach breaks in product-led growth:

Problem 1: Job titles lie. Someone with "Director" in their title might have zero buying authority. The IC engineer using your product daily might have more influence.

Problem 2: Content engagement doesn't predict product fit. Reading blog posts doesn't mean they'll actually use your product. Using your product daily does.

Problem 3: Form-fill data is limited. You know their email domain and self-reported company size. You don't know if they're getting value from your product.

PQLs flip the model: instead of scoring based on who people claim to be, you score based on what they actually do in your product.

The PQL Scoring Framework

Effective PQL scoring combines three signal categories:

Category 1: Product Engagement Signals

These indicate the user understands and is actively using your product.

Key activation: Did they complete the core workflow that delivers value?

• For a CRM: Added contacts and logged their first activity
• For analytics: Installed tracking and viewed their first dashboard
• For collaboration tools: Invited teammates and completed a shared project

Usage frequency: How often do they return?

• Daily active users score higher than weekly users
• Consistent usage over 2-3 weeks signals habit formation
• Sporadic usage suggests tire-kicking

Feature depth: Are they using advanced capabilities or staying surface-level?

• Users exploring premium features show buying intent
• Power users adopting multiple feature sets are serious evaluators
• Users stuck on basic features may not see enough value

Scoring approach:

• Completed activation event: +25 points
• Daily active for 7+ days: +20 points
• Used 3+ premium features: +15 points
• Shared with teammates: +20 points

Category 2: Buying Intent Signals

These indicate the user is evaluating solutions and potentially has budget.

Pricing page visits: Users researching costs are closer to purchase decisions. Multiple visits to pricing signal active evaluation.

Enterprise feature exploration: Testing SSO, admin controls, or bulk user management suggests organizational deployment planning.

Integration setup: Connecting your product to their existing tools indicates intent to adopt long-term, not just test.

Support inquiries about billing or contracts: Questions about invoicing, payment terms, or contract structures signal procurement involvement.

Scoring approach:

• Visited pricing 3+ times: +15 points
• Tested enterprise features: +25 points
• Set up 2+ integrations: +20 points
• Contacted sales/support about buying: +30 points

Category 3: Account Fit Signals

These indicate the account matches your ICP and has revenue potential.

Company size: Does employee count match your target segment?

• For enterprise products: 500+ employees scores high
• For mid-market: 50-500 employees
• For SMB: Under 50

Email domain credibility: Free email domains (gmail.com) score lower than company domains that indicate real organizations.

Technographic signals: Do they use complementary technologies that indicate good fit?

• If you integrate with Salesforce, users with Salesforce accounts score higher
• If you target e-commerce, Shopify users indicate fit

Geographic fit: Do they match your supported regions and go-to-market territories?

Scoring approach:

• Company size matches ICP: +20 points
• Corporate email domain: +10 points
• Uses complementary tech stack: +15 points
• Located in target markets: +10 points

Building Your PQL Score

Combine these signals into a 0-100 point scale:

0-30 points: Browsing - Signed up but showing minimal engagement or fit. Keep in nurture programs but don't prioritize for sales outreach.

31-60 points: Evaluating - Active product users with some buying signals. Monitor for increases in score. Consider low-touch sales outreach.

61-80 points: High Intent - Strong product usage + buying signals + account fit. Prime candidates for sales conversations.

81-100 points: Hot PQLs - Daily active users from ICP accounts showing clear buying intent. Sales should contact immediately.

Calibrating Your Model

Your initial scoring model will be wrong. Calibrate it based on actual outcomes:

Step 1: Baseline validation Score your existing customers retroactively. Where did they score before buying? If your best customers only scored 40 points, your thresholds are wrong.

Step 2: Conversion analysis Track conversion rates by score bracket. Do 80+ point PQLs convert at 10x the rate of 40-point PQLs? If not, adjust weights.

Step 3: Sales feedback loop Have sales rate the quality of PQLs they contact. Are high-scoring PQLs actually better prospects? Adjust model based on their input.

Step 4: Cohort analysis Different user segments may exhibit different patterns. Enterprise buyers might score high on account fit but lower on product usage early. SMB users might show opposite pattern.

Recalibrate quarterly as you learn what predicts actual purchases.

When to Route PQLs to Sales

Not every PQL should go to sales immediately:

80+ points + Enterprise account: Immediate sales outreach. High-touch sales motion.

60-79 points + Mid-market account: Sales-assist motion. Product-led with human help available.

60-79 points + SMB account: Self-serve with automated upgrade prompts. Light-touch sales if requested.

40-59 points: Automated nurture campaigns. Not ready for sales time.

The goal is efficiency: sales talks to users actually ready to buy, while others stay in product-led or automated nurture tracks.

Operationalizing PQL Scoring

Tool integration: Connect your product analytics platform to your CRM. Segment, Mixpanel, or Amplitude can push PQL scores to Salesforce/HubSpot automatically.

Sales workflows: Create views in your CRM showing PQLs above threshold. Assign ownership based on account tier and geography.

Automated alerts: When users cross PQL threshold, trigger notifications to account owners. "Company X just became a PQL based on recent product usage."

PQL dashboards: Give sales visibility into PQL pipeline: how many new PQLs this week, conversion rates, average time from PQL to close.

Common PQL Scoring Mistakes

Mistake 1: Scoring too early Don't score users on day 1. Give them time to activate and show usage patterns. Score after 7-14 days minimum.

Mistake 2: Over-weighting firmographics A perfectly-fit company that never uses your product won't buy. Product engagement should be weighted higher than company size.

Mistake 3: Static models User behavior changes. Update your scoring model quarterly based on what actually predicts purchases.

Mistake 4: No negative scoring Declining usage should reduce PQL scores. If someone was 70 points but hasn't logged in for 2 weeks, adjust downward.

Mistake 5: Forgetting to expire PQLs have shelf life. If sales doesn't engage within 2 weeks and usage stays flat, deprioritize them.

Measuring PQL Program Success

Track these metrics:

PQL-to-opportunity conversion rate: What percentage of PQLs become sales opportunities? Target: 15-30%.

PQL-to-customer conversion rate: What percentage ultimately buy? Target: 5-15%.

Sales efficiency: How much faster do PQL-sourced deals close vs. traditional pipeline? PQLs should close 30-50% faster.

Deal size: Do PQL-sourced deals differ in size from other channels? Often they're smaller but higher velocity.

The Reality

PQL scoring isn't magic. It's a framework for systematically identifying which free/trial users are actually evaluating your product versus casually browsing.

When done well, it dramatically improves sales efficiency. Instead of cold calling into a list of company names, sales talks to users who are already getting value from your product and showing signs they're ready to buy.

That's the promise of product-led growth: let the product qualify the leads, then bring in sales to close the deal.