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pql-framework skill

/plugins/product-led-growth/skills/pql-framework

This skill helps you define, score, and route product-qualified leads using a structured PQL framework across signals, tiers, and automation.

npx playbooks add skill gtmagents/gtm-agents --skill pql-framework

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
name: pql-framework
description: Methodology for defining product-qualified lead (PQL) signals, scoring,
  and routing.
---

# PQL Framework Skill

## When to Use
- Standing up or recalibrating PQL/PQA programs.
- Aligning product, growth, and sales on what constitutes a high-intent product user.
- Auditing the health of existing PQL scoring + routing logic.

## Framework
1. **Signal Library** – catalog feature usage, plan limits, collaboration signals, intent, firmographics.
2. **Scoring Model** – weight signals, set decay rules, and define negative indicators.
3. **Tiering** – map PQL tiers (A/B/C) to follow-up motions and SLAs.
4. **Routing Rules** – specify owners, cues, channels (CRM tasks, Slack alerts, CS queue).
5. **Measurement Loop** – track conversion, ARR impact, and feedback for model tuning.

## Templates
- Signal inventory worksheet with data source + freshness.
- Scoring matrix with weights, thresholds, and decay logic.
- Routing decision tree linking tiers to plays.

## Tips
- Start with simple tiering, iterate once telemetry + feedback improve.
- Include “disqualifier” signals (expired trials, churn risk) to avoid noise.
- Pair with `operationalize-pql-routing` to push models into automation.

---

Overview

This skill provides a production-ready methodology for defining product-qualified lead (PQL) signals, scoring, and routing. It packages a clear framework and templates to align product, growth, sales, and customer success on who qualifies as a high-intent product user. The goal is faster, more accurate lead handoffs that drive conversion and ARR impact.

How this skill works

It inspects product telemetry, account and user attributes, and engagement patterns to build a Signal Library of observable events. Those signals feed a Scoring Model that applies weights, decay rules, and disqualifiers to compute PQL scores. Scores are mapped to tiers and routed using Routing Rules, while a Measurement Loop tracks outcomes and tunes the model.

When to use it

  • When standing up a new PQL or product-qualified account (PQA) program
  • When recalibrating scoring after product, pricing, or go-to-market changes
  • To align product, growth, and sales on common PQL definitions and handoffs
  • When auditing the health of existing scoring, decay, or routing logic
  • Before automating playbooks so routing decisions are reliable and measurable

Best practices

  • Start simple: pick a small set of high-signal events and iterate with telemetry
  • Include negative indicators or disqualifiers (expired trials, churn risk) to reduce noise
  • Define decay rules so older signals lose influence appropriately
  • Map clear SLAs and owner responsibilities for each PQL tier
  • Instrument measurement to track conversion, ARR influence, and model drift

Example use cases

  • Convert trial users who hit key activation events into an SDR outreach queue
  • Score accounts by feature usage and firmographics to prioritize enterprise demos
  • Route high-intent users to in-product prompts or a CS onboarding queue based on tier
  • Audit an existing PQL pipeline to reduce false positives and improve conversion
  • Iteratively tune scoring weights after a major product release or pricing change

FAQ

How many signals should I start with?

Start with 3–7 high-confidence signals tied to activation and intent, then expand after validating outcomes.

What are useful disqualifier examples?

Expired trials, accounts flagged for churn, low-engagement segments, or users with incompatible plans.