Every resource on this page is a downloadable guide, framework, or scorecard we built from real work with B2B SaaS teams. We use these internally at PQ Intel to shape our product strategy, and we've shared them here so anyone can benefit — no email gate, no demo request, no sales follow-up required.
Why free? Because our core mission is helping product-led teams make smarter GTM and product decisions. When your team has better frameworks for evaluating market signals, prioritizing features, and measuring customer health, the entire ecosystem improves. We'd rather earn your trust through useful content than lock it behind a form.
Each resource helps with a specific stage of the product lifecycle — from GTM motion selection and ML feature validation to customer retention analysis and feature prioritization. Together they form a complete toolkit for any B2B SaaS operator, PM, or growth lead who wants structured frameworks instead of guesswork.
Scrolling down? Use the guide below to find the resource that matches your current challenge.
GTM Guide
The Complete GTM Strategy Guide for B2B SaaS (2026)
A 7-step GTM framework with 2026 benchmarks by ARR stage, growth motion, and ACV segment — from motion selection to measurement cadence. Includes a decision tree for choosing between PLG, sales-led, product-assisted sales, and hybrid motions. Also covers channel mix recommendations and a template for building your own GTM playbook from scratch.
Inside: GTM motion selection matrix (PLG vs sales-led vs hybrid), channel mix calculator with budget allocation templates, ACV-by-segment benchmarks across 50+ SaaS companies, 7-step GTM sprint template, motion transition playbook for companies scaling from $1M to $10M ARR, KPI dashboard with leading and lagging metric definitions for each motion type, and a quarterly GTM audit checklist.
ML Readiness Audit: A 5-Dimension Framework for SaaS AI Features
Score your organization across Data Quality, Governance, Architecture, Infrastructure, and Culture — then get a remediation roadmap. Each dimension includes diagnostic questions, scoring rubrics, and tiered recommendations based on your maturity level. Use this before committing engineering resources to any AI feature initiative.
Inside: 25 diagnostic questions across 5 maturity dimensions, 1-5 scoring rubric with tiered recommendations (Beginner/Intermediate/Advanced), 5 actionable remediation roadmaps grouped by effort (quick wins vs strategic investments), worked example with real SaaS AI feature assessment, data quality scorecard template, and governance checklist aligned with emerging AI regulations.
SaaS Customer Health Score Dashboard: The 5-Pillar Framework
Measure, predict, and act on customer health. Product telemetry, sentiment, financial health, engagement, and support signals — weighted and scored. Comes with suggested weightings by business model (usage-based vs. seat-based) and a template for setting your own health thresholds and trigger-based alert rules.
Inside: 5-pillar health scoring framework with 18 individual metrics, usage-based vs seat-based model weightings (validated across 30+ SaaS companies), health threshold templates (Green/Yellow/Red with automated trigger rules), rolling health score tracker spreadsheet template, early churn prediction model with 6 leading indicators, and a quarterly health review meeting agenda with stakeholder-specific data packages.
A structured scorecard to determine whether your next AI feature is worth building. Score each question using a 1-5 rubric and decide with confidence. Covers user pain alignment, data availability, technical feasibility, business impact, and differentiation potential — the five factors that separate value-adding AI from expensive toys.
Inside: 10 scored questions with detailed 1-5 scoring rubric for each, decision matrix mapping total scores to build/delay/deprecate recommendations, five-factor breakdown (User Pain, Data Availability, Technical Feasibility, Business Impact, Differentiation), worked example scoring two real AI feature proposals, scorecard template for running the exercise with your team, and guidance on re-scoring cadence as market conditions change.
Feature Prioritization Playbook: 4 Frameworks to Kill the Feature Factory
Four prioritization frameworks (RICE, ICE, WSJF, Opportunity Scoring) with scoring templates and a growth sprint methodology. Each framework includes a worked example with real SaaS metrics, plus guidance on which framework fits your team's stage and data maturity. The playbook also shows how to combine frameworks for quarterly planning.
Inside: Four full framework walkthroughs (RICE, ICE, WSJF, Opportunity Scoring) with reusable scoring templates, worked examples using real SaaS feature data (pricing page redesign, onboarding flow optimization, API integration suite), framework selection guide mapped to team stage and data maturity, quarterly planning calendar integrating all four frameworks, feature factory prevention checklist with 12 red-flag behaviors, and a facilitation guide for running prioritization workshops with cross-functional teams.
Match your current situation to the resource that will help most. Each pick includes a suggested reading order inside the resource.
You are defining or refining your GTM motion for the first time
The GTM Strategy Guide is your starting point. It covers the full decision tree — which motion (PLG, sales-led, hybrid), which channels, which metrics — and connects everything else on this page back to a unified GTM strategy. Start here if you are pre-$5M ARR or going through a motion transition. Work through it over a week, completing each step's exercise before moving to the next.
Pick: The Complete GTM Strategy Guide
Your team wants to add AI features but you are not sure where to start
Run the ML Readiness Audit and AI Fit Scorecard together. The audit tells you whether your org is ready to build an AI feature; the scorecard tells you whether the specific feature you have in mind is worth building. Use both before writing a single line of code. A low readiness score does not mean "don't build" — it means "fix these gaps first." If you can only download one, start with the AI Fit Scorecard to validate the idea, then use the ML Readiness Audit to assess execution risk.
Pick: ML Readiness Audit + AI Feature Fit Scorecard
You are in a growth or retention phase and drowning in feature requests
Pair the Customer Health Score dashboard with the Feature Prioritization Playbook. The health score surfaces which customer segments are at risk and why. The prioritization playbook helps you decide which of the resulting feature requests to build first. Together they break the "we need everything" decision paralysis that kills growth teams. Use the health score to identify the 20% of segments driving 80% of churn risk, then use WSJF from the playbook to sequence fixes.
Pick: Customer Health Score + Feature Prioritization Playbook
You need to convince stakeholders that a specific feature investment is worth it
The Feature Prioritization Playbook gives you the scoring frameworks and worked examples you need to build a data-backed business case. Use the RICE or WSJF scoring template with your own metrics, present the comparison table showing your proposed feature against other candidates, and let the scores do the convincing. The playbook also includes a stakeholder communication template for presenting prioritization results to leadership.
Pick: Feature Prioritization Playbook
You want to understand how all five resources work together over a full product cycle
Start with the GTM Strategy Guide to set your motion and channel strategy. Use the ML Readiness Audit and AI Fit Scorecard as you evaluate new AI features. Track customer health with the Health Score dashboard as you onboard and retain customers. And use the Feature Prioritization Playbook every quarter to decide what to build next. Each resource includes cross-references to the others — they are designed as a system, not a collection. Download all five and work through them in this order over the course of a quarter.
Pick: All five resources (download the full set)
How to use these resources
The five resources above aren't meant to be consumed in a single sitting. Here's a practical approach to getting the most out of them:
Start with the GTM Strategy Guide if you're in the early stages of defining or refining your go-to-market motion. It gives you the highest-level picture — market selection, motion choice, channel strategy — and everything else connects back to it. Work through it over a week, completing each step's exercise before moving to the next.
Run the ML Readiness Audit and AI Fit Scorecard together if you're evaluating AI features. The audit tells you whether your org is ready to build; the scorecard tells you whether the feature is worth building. Use both before writing a single line of code. A low readiness score doesn't mean "don't build" — it means "fix these gaps first."
Pair the Customer Health Score dashboard with the Feature Prioritization Playbook when you're in a growth or retention phase. The health score surfaces which customer segments are at risk; the prioritization playbook helps you decide which of the resulting feature requests to build first. Together they break the "we need all of it" decision paralysis.
Each resource includes scoring templates and worked examples. We recommend running the exercise with your whole team — the discussion is often more valuable than the score itself. Revisit the frameworks quarterly as your data improves and your product matures.
What's coming next
We're working on several new resources. Here's a preview of what's in the pipeline — sign up below to be notified when they launch.
Pricing Page Teardown Playbook
A structured framework for auditing competitor and aspirant pricing pages. Includes a 20-point scoring rubric covering packaging, anchoring, tier differentiation, and pricing psychology tactics. Coming Q3 2026.
B2B Signal-to-Intent Translation Matrix
How to map raw buying signals (job changes, funding events, tech stack shifts, hiring patterns) to product intent scores. Includes validated weightings and a scoring template. Coming Q4 2026.
Product-Led Growth Maturity Model
A 5-stage maturity model for PLG organizations, from ad-hoc acquisition to fully automated growth loops. Each stage includes diagnostic criteria, capability benchmarks, and a transition plan. Coming Q1 2027.
Get notified about new resources
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