---
name: pql-definition
slug: pql-definition
description: This skill should be used when the user asks to "define PQL", "what is a PQL", "set PQL criteria", "build a PQL definition", "design product-qualified lead criteria", "define product-qualified lead", "when should a free user become a PQL", "create PQL standards", "set up PQL scoring from product usage", or any variation of defining, designing, or implementing product-qualified lead criteria for B2B SaaS with a free trial or freemium model.
category: general
---

# PQL Definition Framework

A PQL (Product-Qualified Lead) is a free trial or freemium user who has demonstrated enough product usage to indicate buying intent. Unlike MQLs (which are based on marketing engagement), PQLs are based on actual product behavior. A user who set up 3 integrations, invited 2 teammates, and ran their first campaign is signaling readiness to buy through actions, not form fills.

The principle: PQL scoring is built from product usage patterns that correlate with conversion. The best PQL criteria come from analyzing what converted customers did in their first 7-14 days of product use, then defining the threshold that separates tire-kickers from future buyers.

## PQL vs MQL

| Dimension | MQL | PQL |
|-----------|-----|-----|
| Signal source | Marketing engagement (content, ads, forms) | Product usage (features, frequency, depth) |
| Intent signal | Indirect (researching, browsing, downloading) | Direct (using the product, experiencing value) |
| Typical conversion rate | 15-25% MQL → Opportunity | 25-40% PQL → Opportunity |
| Data source | Marketing automation, CRM, web analytics | Product analytics (Segment, Amplitude, Mixpanel, Heap) |
| Sales approach | Discovery: "What problem are you trying to solve?" | Value expansion: "I see you've set up X. Want to unlock Y?" |
| Applicable to | All B2B SaaS (inbound marketing model) | Only B2B SaaS with free trial or freemium |

**PQLs convert at 1.5-2x the rate of MQLs** because the prospect has already experienced the product. The sales conversation shifts from "why should I care" to "how do I get more of this."

---

## Building the PQL Definition

### Step 1: Identify the activation events

Activation events are product actions that correlate with conversion to paid. Start by analyzing what converted customers did differently from churned trial users.

**How to find activation events:**

1. Pull two cohorts: (a) trial/free users who converted to paid, (b) trial/free users who churned
2. For each cohort, list every product action in the first 14 days
3. Find the actions with the largest frequency difference between converters and churners

**Common activation events by product type:**

| Product type | Common activation events | Why they matter |
|-------------|------------------------|----------------|
| Sales engagement tool | Created a sequence, sent 10+ emails, connected CRM | User is actively running outbound through the product |
| CRM | Added 10+ contacts, created a deal, set up a pipeline | User is making the product their system of record |
| Analytics / BI | Connected a data source, created a dashboard, shared a report | User experienced the value (insight) and shared it (org buy-in) |
| Collaboration tool | Invited 3+ teammates, created a workspace, completed a project | Multi-user = stickiness. Hard to churn once the team is in |
| Marketing automation | Connected email, created a workflow, sent a campaign | User ran a real campaign. They've seen results |
| Developer tool | Made 100+ API calls, deployed to production, connected to CI | Production usage = real dependency. Hard to rip out |

### Step 2: Define the PQL threshold

The PQL threshold is the combination of activation events that predicts conversion with high confidence.

**Threshold template:**

```
A user becomes a PQL when:

1. ACCOUNT FIT (must pass):
   - Company size: ≥ [minimum] employees
   - Account is not: competitor, personal project, student, agency (if excluded)
   - Signup email is a work email (not gmail/yahoo)

AND

2. PRODUCT USAGE (all of these within the first [14/30] days):
   - Completed [core setup action] (e.g., connected data source)
   - Performed [value action] at least [N] times (e.g., sent 10+ emails)
   - Used [expansion signal] (e.g., invited a teammate)

OR

3. INSTANT PQL OVERRIDE:
   - Visited the upgrade/pricing page from within the product
   - Attempted to use a paid-only feature
   - Reached a usage limit on the free plan
```

### Step 3: Validate with historical data

Before deploying the PQL definition, backtest it.

**Validation process:**

1. Apply the proposed PQL criteria to the last 6 months of trial signups
2. Calculate: what % of users who met the PQL criteria actually converted?
3. Calculate: what % of users who converted were NOT flagged as PQLs (false negatives)?
4. Target: PQL → Paid conversion rate ≥ 25%. False negative rate ≤ 15%

**Validation rules:**
- If PQL → Paid conversion < 20%, the threshold is too loose. Tighten the product usage requirements
- If false negative rate > 20%, the threshold is too tight. Lower the activation event count or add new trigger events
- If both are bad, the wrong activation events are being tracked. Go back to Step 1 and re-analyze the converter vs churner data

### Step 4: Implement tracking

PQL scoring requires product usage data flowing to the sales team's CRM or a shared dashboard.

**Data pipeline:**

```
Product → Event tracking (Segment, Amplitude, Mixpanel)
  → PQL scoring engine (custom logic, or Correlated, Pocus, Endgame)
  → CRM (HubSpot, Salesforce)
  → Alert (Slack notification to assigned rep)
```

**Implementation options:**

| Approach | Complexity | Best for |
|----------|-----------|----------|
| Manual (export + spreadsheet) | Low | < 100 signups/month. Early stage validation |
| Zapier/Make + product analytics API | Medium | 100-500 signups/month. No engineering needed |
| Reverse ETL (Census, Hightouch) | Medium-high | 500+ signups/month. Syncs product data to CRM automatically |
| Purpose-built PQL tool (Pocus, Correlated) | Medium | Teams that want pre-built PQL scoring without custom engineering |
| Custom pipeline (product DB → scoring service → CRM API) | High | 1,000+ signups/month. Full control over scoring logic |

---

## PQL Scoring Models

### Model 1: Threshold-based (recommended starting point)

A user becomes a PQL when they complete a specific set of actions. Binary: you either hit the threshold or you didn't.

**Example:**

```
PQL = TRUE when:
  - Account has ≥ 2 active users
  - Primary user has completed onboarding (3/5 setup steps done)
  - At least 1 core value action performed (sent a campaign, created a report, ran a query)
  - Signup was within last 30 days
  - Work email (not personal)
```

**Pros:** Simple. Easy to understand. Easy to implement. Easy to debug.
**Cons:** Binary. Doesn't differentiate between a user barely past the threshold and a power user.

### Model 2: Points-based (weighted scoring)

Assign points to product actions. PQL when the score exceeds a threshold.

**Example:**

| Action | Points | Cap |
|--------|--------|-----|
| Completed onboarding | +20 | One-time |
| Connected integration | +15 per integration | 3 max (45 pts) |
| Invited teammate | +10 per invite | 5 max (50 pts) |
| Core feature used | +5 per use | 10 max (50 pts) |
| Daily active use (DAU) | +3 per day | 14 max (42 pts) |
| Visited pricing page in-app | +25 | One-time |
| Hit usage limit | +30 | One-time |
| **PQL threshold** | **≥ 60 points** | |

**Pros:** Nuanced. Differentiates between engagement levels. Enables prioritization (higher score = hotter PQL).
**Cons:** More complex. Harder to explain to sales. Requires calibration.

### Model 3: Milestone-based

Define 3-5 milestones on the path to value. PQL when the user passes a specific milestone.

**Example:**

```
Milestone 1: Signed up + completed onboarding     → "Activated"
Milestone 2: Performed core value action            → "Engaged"
Milestone 3: Invited teammate OR connected integration → "Expanding" ← PQL
Milestone 4: Hit usage limit OR visited pricing      → "Ready to buy"
Milestone 5: Initiated upgrade flow                  → "Hand-raiser"
```

**Pros:** Intuitive. Maps to the user journey. Easy for sales to understand where the user is.
**Cons:** Sequential model may not capture users who skip milestones.

**Recommendation:** Start with threshold-based. It's the simplest to build, test, and calibrate. Graduate to points-based when you have enough data to weight actions accurately (usually after 500+ trial signups).

---

## PQL Signals by Category

### Setup signals (completed onboarding steps)

| Signal | Weight | Why it matters |
|--------|--------|---------------|
| Completed account setup | Low-medium | Necessary but not sufficient. Most trial users complete setup |
| Connected integration (CRM, data source, API) | High | Integration = commitment. Hard to undo. Signals real use |
| Imported data (contacts, records, files) | High | Brought their real data into the product. Not tire-kicking |
| Configured settings (preferences, templates, workflows) | Medium | Customization = investment. Making the product theirs |

### Usage signals (ongoing product activity)

| Signal | Weight | Why it matters |
|--------|--------|---------------|
| Daily active use (3+ days in first week) | High | Regular usage correlates strongly with conversion |
| Core feature used repeatedly (not just once) | High | One-time use = exploration. Repeated use = workflow adoption |
| Volume of core actions (10+ emails, 5+ reports, 100+ API calls) | High | Volume = dependency. The product is becoming part of their process |
| Session duration > X minutes | Medium | Longer sessions suggest deep engagement, not just a quick look |

### Expansion signals (team and organizational adoption)

| Signal | Weight | Why it matters |
|--------|--------|---------------|
| Invited teammates | Very high | Multi-user = organizational buy-in. Hardest signal to fake |
| Teammates accepted invite and were active | Very high | Not just invited. Actually using. Strongest PQL signal |
| Created shared workspace / team project | High | Collaborative use = team dependency |
| Shared a report or asset externally | Medium | User is showing the product's output to stakeholders |

### Buying signals (explicit purchase intent)

| Signal | Weight | Why it matters |
|--------|--------|---------------|
| Visited in-app pricing or upgrade page | Very high | Explicit intent to evaluate purchasing |
| Attempted to use a paid feature (hit paywall) | Very high | Needs functionality beyond the free tier |
| Hit a usage limit (contacts, API calls, seats) | High | Outgrowing the free plan |
| Clicked "Contact Sales" from within the product | Instant PQL | Direct request for sales conversation |
| Started but didn't complete an upgrade flow | Very high | Cart abandonment in SaaS. Follow up immediately |

---

## Sales Motion for PQLs

PQL outreach is fundamentally different from MQL outreach. The user already knows the product. The conversation is about expanding value, not explaining value.

### PQL outreach templates

**Template 1: Usage acknowledgment (for PQLs who hit a usage milestone)**

```
Subject: nice progress on [product]

{first_name}, saw you've [specific action: sent 50+ emails /
created 3 dashboards / connected your CRM] in the last week.

Most teams at your stage find that [paid feature or higher limit]
helps with [specific next step]. Worth a quick walkthrough of how
that works for a team like {company}?

{rep_first_name}
```

**Template 2: Expansion signal (for PQLs who invited teammates)**

```
Subject: {company} is growing on [product]

{first_name}, noticed you've added {N} teammates to your
[product] workspace. That's usually when teams start looking at
[paid capability: advanced permissions, team reporting, SSO].

Happy to show you how {similar_company} set this up when they
scaled past the free plan. 15 minutes?

{rep_first_name}
```

**Template 3: Paywall hit (for PQLs who tried a paid feature)**

```
Subject: unlocking {feature_name}

{first_name}, looks like you tried to [specific paid action] on
[product]. That's a [paid plan] feature.

Want me to set up a trial of the full plan so you can test it
with your real data? Takes 2 minutes on my end.

{rep_first_name}
```

### PQL outreach rules

- Reference specific product actions. "I saw you sent 50 emails" not "I noticed you've been using our platform." Specificity proves you're paying attention, not blasting
- Lead with the next step, not a pitch. The user already uses the product. Don't explain what it does. Help them do more
- Offer to unlock, not to sell. "Want me to extend your trial?" or "Want me to set up a pilot of the paid plan?" is lower friction than "Want to talk about pricing?"
- Respond within 2 hours of PQL trigger for buying signals (pricing page, paywall hit, upgrade attempt). These are the hottest signals in PLG
- Don't cold-call a PQL without email first. They're using a product, not expecting a phone call. Email first, call if they engage

---

## Measurement

| Metric | Definition | Target | Frequency |
|--------|-----------|--------|-----------|
| PQL volume | PQLs generated per month | Tracking trend with signup growth | Weekly |
| PQL conversion rate | Paid conversions / PQLs | 25-40% | Monthly |
| Time to PQL | Days from signup to PQL status | < 14 days (for 14-day trials) | Monthly |
| PQL response time | Time from PQL alert to first rep outreach | < 2 hours for buying signals | Weekly |
| False positive rate | PQLs that never convert / total PQLs | < 60% | Monthly |
| False negative rate | Paid conversions that were never PQLs / total conversions | < 15% | Quarterly |
| PQL-to-Opportunity rate | Opportunities created from PQLs | 30-50% | Monthly |
| Self-serve vs sales-assisted conversion | What % of PQLs convert without sales touch vs with | Track split | Monthly |
| Activation rate | % of signups that reach PQL status | 15-30% | Monthly |

---

## PQL by Company Stage

| Stage | PQL approach | Typical criteria |
|-------|-------------|-----------------|
| Pre-PMF (< $1M ARR) | Manual. Watch product usage dashboards. Reach out personally when you see engagement | "Someone is using it a lot" (unstructured) |
| Post-PMF ($1-5M ARR) | Threshold-based. Simple PQL definition. Manual or Zapier pipeline to CRM | 3 activation events + work email |
| Growth ($5-20M ARR) | Points-based or milestone. Automated pipeline. PQL tool or Reverse ETL | Weighted scoring model. Automated routing |
| Scale ($20M+ ARR) | Predictive. ML-assisted PQL scoring. Multiple PQL definitions per segment | Predictive model trained on conversion data |

---

## Anti-Pattern Check

- PQL definition based on signup only. Creating an account is not product usage. A signup with no activation is a free user, not a PQL. Require meaningful product actions
- No fit gate on PQLs. A student building a side project who uses every feature is not a PQL. Include account-level fit criteria (company size, work email) alongside product usage
- Treating all PQLs the same. A user who hit a paywall (explicit buying signal) and a user who completed onboarding (early activation) need different outreach. Tier PQLs by signal strength
- No product data flowing to CRM. Sales can't act on PQL signals they can't see. Product usage data must sync to CRM where reps work. A dashboard nobody checks is not a pipeline
- Cold-calling PQLs without context. "Hi, I see you signed up for a free trial" is generic. "I noticed you connected Salesforce and sent 50 emails this week" shows you know their usage. Reference specific actions
- PQL threshold set from intuition. "Completing onboarding seems like a good threshold" is a guess. Analyze converter vs churner data. Find the actions that actually predict conversion
- No false negative tracking. If 30% of paid conversions were never flagged as PQLs, the definition is too narrow and you're missing real buyers. Track and fix quarterly
- Same PQL definition for SMB and enterprise signups. An enterprise user's activation path is different (longer, involves more stakeholders, different features). Consider segment-specific PQL criteria at scale