Startup Go-to-Market

When to Use

• Designing go-to-market strategy for new product
• Choosing between PLG and sales-led motion
• Planning product launches (soft, beta, ProductHunt, full)
• Defining ICP and channel strategy
• Implementing AI-powered GTM automation

When NOT to Use

• Positioning and messaging deep dive -> marketing-content-strategy (use startup-competitive-analysis for differentiation inputs)
• Competitive intelligence -> startup-competitive-analysis
• Fundraising strategy -> startup-fundraising
• Pricing and revenue models -> startup-business-models

Quick Start (Inputs)

• Stage: pre-PMF, early PMF, growth, scale
• Product and category: what it is, who uses it, and what "first value" looks like
• ICP and buyer: firmographics, pains, procurement constraints, economic buyer vs champion
• Pricing and economics: current/target ACV/ARPA, COGS drivers (include variable compute), payback constraints
• Motion constraints: self-serve possible, sales cycle expectations, implementation/onboarding complexity
• Channel constraints: budget, time, audience access (communities, lists, partnerships), geo, compliance limits
• Baseline metrics: traffic, signup/demo rate, activation, retention, win rate, sales cycle length, pipeline
• Team and tooling: who executes (founder/marketing/sales/CS), CRM + analytics stack

Workflow

1. Define ICP and the buying path

• Primary/secondary ICP, anti-ICP, trigger events, and an "activation" definition.
• Use

  assets/icp-definition.md

  to draft.

1. Align on positioning and proof

• If positioning is unclear, use startup-competitive-analysis to map alternatives + differentiation, then marketing-content-strategy to express it as messaging.

1. Choose the motion (PLG / sales-led / hybrid)

• Use the decision tree below for a fast cut.
• For details:

  references/plg-implementation.md

  and

  references/sales-motion-design.md

  .

1. Pick 1-2 channels to sequence (not parallelize)

• Use a bullseye-style test plan: quick tests, measure, double down.
• For execution details:

  references/channel-playbooks.md

  .

1. Define measurement and RevOps alignment

• Define shared lifecycle stages and the "one source of truth" for metrics (product + CRM).
• Ensure handoffs are measurable (e.g., PQL -> SQL routing rules and SLAs for hybrid).

1. Produce deliverables + operating cadence

• Draft GTM plan (

  assets/gtm-strategy.md

  ) and launch plan (

  assets/launch-playbook.md

  ).
• Run a weekly GTM review: 30 minutes on pipeline + funnel, 30 minutes on experiments, 30 minutes on decisions.

Decision Tree

GTM QUESTION
  |-- "How do I reach customers?" -> Channel Strategy
  |-- "PLG or Sales-led?" -> Motion Selection
  |-- "How do I launch?" -> Launch Planning
  |-- "Who is my ICP?" -> Segmentation
  `-- "How do I scale?" -> Growth Loops

GTM Motion Types

Motion Selection Framework

ACV < $5K and self-serve possible?
  - yes: PLG (add sales-assist for expansion)
  - no: is buyer technical?
      - yes: developer/community-led (bottom-up)
      - no: sales-led

ICP Components

ICP Scoring

Channel Strategy

Channel Sequencing by Stage

Measurement (Minimum Viable GTM Analytics)

• Prefer lifecycle + cohorts over vanity metrics. Always break down by ICP/segment + channel.
• Define a single funnel per motion (PLG vs sales-led) with clear stage definitions and owners.
• Track leading indicators (activation/retention, PQL, win rate) before "scale" decisions.

PQL = (Engagement * 0.4) + (Fit * 0.3) + (Intent * 0.3)

Product-Led Sales (Sales-Assist) Basics

• Define PQL triggers (events) and thresholds (e.g., 3 key actions in 7 days)
• Define disqualifiers (students, competitors, tiny companies, unsupported geo)
• Set an SLA for first touch (e.g., <24 hours for high-intent PQLs)
• Define handoff criteria to AE (PQL -> meeting booked, security/procurement requested)
• Instrument outcomes (PQL->meeting->pipeline->won) and review weekly

Launch Types

Growth Loops

Do / Avoid

Do

• Define activation as concrete "first value moment"
• Track leading indicators (activation, PQL, retention)
• Use AI for execution while humans own strategy
• Tier ICP based on fit + intent signals

Avoid

• Content spam without measurement
• "Do all channels" in parallel
• Vanity metrics without retention context
• Over-automating without human oversight
• Scaling paid before activation/retention is stable
• Treating benchmarks as targets without segmenting by ICP/channel

Resources

Templates

Data

Related Skills

What Good Looks Like

• One primary ICP with clear anti-ICP and measurable triggers (signals) for targeting.
• A motion decision with explicit economics (ACV, payback, touch model) and defined handoffs.
• One primary channel with a test plan, success metrics, and stop/pivot triggers.
• Instrumented funnel from source -> activation/value -> revenue/expansion (by segment + channel).
• A weekly operating cadence with a backlog of experiments and a written decision log.