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

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:

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:

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:

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)

Usage signals (ongoing product activity)

Expansion signals (team and organizational adoption)

Buying signals (explicit purchase intent)

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

PQL by Company Stage

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