
startup-go-to-market
by vasilyu1983
SKILL.md
name: startup-go-to-market description: GTM strategy, channel selection, launch planning, AI-powered automation, and market entry execution metadata: version: "2.0"
Startup Go-to-Market
Systematic framework for designing and executing market entry strategies.
Modern Best Practices (Jan 2026):
- Start from ICP + positioning, then pick 1–2 channels to sequence (avoid "all channels").
- Instrument the funnel end-to-end (activation and retention defined, not assumed).
- Use tight feedback loops (weekly learning reviews) and write stop/pivot thresholds.
- Leverage AI-powered GTM tools for lead enrichment, personalized outreach, and pipeline intelligence.
- Align RevOps across sales, marketing, and CS—75% of fastest-growing companies have RevOps by 2026.
- Treat customer data with purpose limitation, retention, and access controls.
Decision Tree: What GTM Analysis?
GTM QUESTION
│
├─► "How do I reach customers?" ───► Channel Strategy
│ └─► Channel selection, sequencing
│
├─► "PLG or Sales-led?" ───────────► Motion Selection
│ └─► GTM motion design
│
├─► "How do I launch?" ────────────► Launch Planning
│ └─► Launch playbook
│
├─► "Who is my ICP?" ──────────────► Segmentation
│ └─► ICP definition, targeting
│
├─► "How do I scale?" ─────────────► Scaling Strategy
│ └─► Growth loops, expansion
│
└─► "Full GTM strategy" ───────────► COMPREHENSIVE ANALYSIS
└─► All dimensions
GTM Motion Types
Motion Taxonomy
| Motion | Description | Best For | Examples |
|---|---|---|---|
| Product-Led Growth (PLG) | Product drives acquisition, conversion, expansion | SMB, developers, horizontal | Slack, Figma, Notion |
| Sales-Led | Reps drive deals through outbound and inbound | Enterprise, complex sales | Salesforce, Workday |
| Community-Led | Community drives awareness and adoption | Developer tools, open source | Hashicorp, MongoDB |
| Channel/Partner-Led | Partners drive distribution | Enterprise, geographic expansion | Microsoft, Cisco |
| Marketing-Led | Marketing drives demand generation | B2C, SMB | HubSpot, Mailchimp |
Motion Selection Framework
ACV < $5K + Self-serve possible?
│
├─► YES ──► PLG (primary)
│ └─► Add Sales-assist for expansion
│
└─► NO ───► Is buyer technical?
│
├─► YES ──► Developer/Community-Led
│ └─► Bottom-up adoption
│
└─► NO ───► Sales-Led
└─► Inbound + Outbound
Hybrid Motions (2025-2026 Reality)
| Hybrid | Components | Examples |
|---|---|---|
| PLG + Sales | Self-serve → Sales-assist for enterprise | Slack, Zoom, Figma |
| Community + PLG | OSS → Hosted → Enterprise | MongoDB, Elastic |
| Marketing + Sales | Inbound MQLs → Sales conversion | HubSpot |
| Partner + Sales | Partner referrals → Direct sales | AWS Partners |
| AI-Augmented | AI SDRs + Human closers | Emerging 2026 |
Vertical vs. Horizontal Strategy (2026)
| Strategy | When to Use | Examples |
|---|---|---|
| Vertical | Deep industry workflows, compliance needs | Veeva (pharma), Toast (restaurants) |
| Horizontal | Broad applicability, platform play | Slack, Notion |
| Vertical-first | Start narrow, expand | Rippling (HR → IT → Finance) |
2026 trend: Horizontal platforms face increasing competition from specialized vertical solutions. Successful strategies either dominate specific verticals or create platform ecosystems.
Ideal Customer Profile (ICP)
ICP Components
| Component | Questions | Example |
|---|---|---|
| Firmographics | Size, industry, geography | 50-500 employees, B2B SaaS, US |
| Technographics | Tech stack, tools | Uses Salesforce, Modern data stack |
| Behavioral | Buying behavior, adoption patterns | Self-serve evaluation, fast decisions |
| Pain indicators | Symptoms of the problem | Growing support tickets, churn issues |
| Success indicators | Signs of good fit | Strong product-market alignment |
ICP Template
## Ideal Customer Profile: {{PRODUCT}}
### Company Profile
- **Industry**: {{INDUSTRY}}
- **Size**: {{EMPLOYEE_RANGE}} employees
- **Revenue**: ${{REVENUE_RANGE}}
- **Geography**: {{REGIONS}}
- **Growth Stage**: {{STAGE}}
### Technology Profile
- **Must have**: {{REQUIRED_TECH}}
- **Nice to have**: {{PREFERRED_TECH}}
- **Red flags**: {{AVOID_TECH}}
### Buyer Profile
- **Primary Buyer**: {{TITLE}}
- **Champions**: {{TITLES}}
- **Economic Buyer**: {{TITLE}}
- **Influencers**: {{TITLES}}
### Pain Indicators
- {{PAIN_1}}
- {{PAIN_2}}
- {{PAIN_3}}
### Success Indicators
- {{SUCCESS_1}}
- {{SUCCESS_2}}
ICP Scoring
| Factor | Weight | Score (1-10) |
|---|---|---|
| Budget available | 20% | |
| Problem severity | 25% | |
| Technical fit | 15% | |
| Decision timeline | 15% | |
| Champion identified | 15% | |
| Expansion potential | 10% | |
| ICP Score | 100% |
ICP Tiering (2026 Best Practice)
Don't treat ICP as a static persona—tier it based on fit and intent signals.
| Tier | Definition | Action | Resources |
|---|---|---|---|
| Tier 1 | Perfect fit + active buying signals | Priority outbound, personalized | High-touch, exec involvement |
| Tier 2 | Good fit, lower/no intent signals | Nurture sequences, monitor | Marketing-led, SDR follow-up |
| Tier 3 | Partial fit, no current signals | Marketing only, monitor | Automated, low-touch |
Intent Signals to Monitor:
- Hiring patterns (roles that use your product)
- Technology adoption (complementary tools)
- Funding events (Series A+ for growth stage)
- Buying committee activity (multiple visitors from same company)
- Content engagement (pricing page, case studies)
Channel Strategy
Channel Categories
| Category | Channels | Best For |
|---|---|---|
| Organic | SEO, content, social, community | Long-term, sustainable |
| Paid | SEM, paid social, display | Fast, scalable, expensive |
| Outbound | Email, cold calls, LinkedIn | Enterprise, high ACV |
| Partnerships | Referrals, integrations, resellers | Leverage, distribution |
| Product | Viral, freemium, PLG | Self-serve, network effects |
| Events | Conferences, webinars, meetups | Enterprise, brand |
Channel Selection Matrix
| Channel | CAC | Volume | Time to Impact | Control |
|---|---|---|---|---|
| SEO/Content | Low | High | 6-12 months | High |
| Paid Search | Medium | Medium | Immediate | High |
| Paid Social | Medium | High | Immediate | Medium |
| Outbound Email | Medium | Medium | 1-3 months | High |
| LinkedIn Outbound | High | Low | 1-3 months | High |
| Conferences | High | Low | 3-6 months | Medium |
| Partnerships | Medium | Medium | 6-12 months | Low |
| Product/Viral | Low | High | 3-6 months | Medium |
| Community | Low | Medium | 6-12 months | Medium |
Channel Sequencing by Stage
| Stage | Primary Channels | Why |
|---|---|---|
| Pre-PMF | Founder sales, communities, early users | Direct feedback |
| Early | Content, outbound, founder network | Capital efficient |
| Growth | Paid, SEO, partnerships | Scale |
| Scale | All channels optimized | Efficiency |
PLG Playbook
PLG Funnel
AWARENESS
│
▼
ACQUISITION (Sign up)
│
▼
ACTIVATION (First value moment)
│
▼
RETENTION (Continued usage)
│
▼
REVENUE (Convert to paid)
│
▼
REFERRAL (Viral spread)
Key PLG Metrics
| Stage | Metric | Benchmark |
|---|---|---|
| Acquisition | Visitor → Signup | 2-10% |
| Activation | Signup → Activated | 30-60% |
| Retention | Day 1 / Day 7 / Day 30 | 40% / 20% / 10% |
| Revenue | Activated → Paid | 10-30% |
| Referral | % users who invite | 20-30% |
Activation Definition
"Activation" = When user experiences core value
| Product Type | Activation Moment |
|---|---|
| Slack | Sent 2,000 messages |
| Dropbox | Installed + synced file |
| Zoom | Completed first meeting |
| Notion | Created and shared doc |
| Your product | {{ACTIVATION_MOMENT}} |
PLG Pricing Considerations
| Element | Recommendation |
|---|---|
| Free tier | Yes, with usage limits |
| Trial length | 14 days (card optional) |
| Upgrade triggers | Hit limits, need feature |
| Pricing page | Transparent, self-serve |
| Enterprise | "Contact sales" option |
PLG Evolution (2026)
Key Shift: "Aha Moment" → "Oh Wow Moment"
Getting users to value once isn't enough. The real metric is when they keep coming back: "Wait, it does this too?"
| Old PLG (2020-2024) | Modern PLG (2025-2026) |
|---|---|
| Single activation moment | Repeatable value discovery |
| MQL-driven qualification | PQL-driven (Engagement + Fit + Intent) |
| 14-30 day trials | Instant value, progressive commitment |
| Manual onboarding flows | AI-powered personalization |
| Time-based conversion | Value-based conversion |
PQL (Product Qualified Lead) Scoring:
PQL Score = (Engagement × 0.4) + (Fit × 0.3) + (Intent × 0.3)
Engagement: Feature usage depth, session frequency, collaboration
Fit: Company size, industry, tech stack match
Intent: Pricing page visits, integration setup, team invites
| Qualification | Conversion Rate | Action |
|---|---|---|
| MQL (Marketing) | 5-10% | Nurture |
| PQL (Product) | 25-30% | Sales-assist |
| PQL + Sales signal | 40-50% | Priority outbound |
2026 Buyer Expectations:
- Value within minutes, not days
- Try first, account later
- AI-personalized onboarding
- Self-serve to enterprise upgrade path
Sales-Led Playbook
Sales Motion Design
| Element | SMB | Mid-Market | Enterprise |
|---|---|---|---|
| ACV | $1-10K | $10-50K | $50K+ |
| Sales cycle | <30 days | 30-90 days | 90-270 days |
| Touch model | Low-touch/inside | Inside/field | Field/strategic |
| Demo | Self-serve or 15 min | 30-60 min | Custom POC |
| Stakeholders | 1-2 | 3-5 | 5-10+ |
| Procurement | Credit card | Simple | Complex |
Outbound Playbook
Sequence Structure:
Day 1: Email 1 (Pain-focused)
Day 3: LinkedIn connection
Day 5: Email 2 (Value-focused)
Day 8: LinkedIn message
Day 12: Email 3 (Social proof)
Day 16: Email 4 (Break-up)
Targeting:
| Element | Specification |
|---|---|
| Company size | {{RANGE}} |
| Titles | {{LIST}} |
| Industries | {{LIST}} |
| Signals | {{TRIGGERS}} |
Sales Stages
| Stage | Definition | Exit Criteria |
|---|---|---|
| Prospecting | Identifying targets | Meeting booked |
| Discovery | Understanding needs | Qualified (BANT/MEDDIC) |
| Demo | Showing solution | Interest confirmed |
| Evaluation | POC, trial, references | Success criteria met |
| Proposal | Pricing, terms | Proposal sent |
| Negotiation | Contract discussion | Agreement on terms |
| Closed Won | Signed | Revenue booked |
Launch Planning
Launch Types
| Type | Goal | Timeline |
|---|---|---|
| Soft launch | Test, iterate | 2-4 weeks |
| Beta launch | Build waitlist, get feedback | 4-8 weeks |
| ProductHunt launch | Awareness, early adopters | 1 day + prep |
| Full launch | Maximum awareness | 1-2 weeks |
| Feature launch | Existing customer expansion | Ongoing |
Launch Playbook Template
## Launch: {{PRODUCT/FEATURE}}
### Objectives
- Primary: {{GOAL}}
- Secondary: {{GOAL}}
- Metrics: {{TARGETS}}
### Timeline
| Week | Activities |
|------|------------|
| -4 | {{PREP}} |
| -2 | {{PREP}} |
| -1 | {{FINAL}} |
| Launch | {{ACTIVITIES}} |
| +1 | {{FOLLOW_UP}} |
### Channels
| Channel | Asset | Owner | Date |
|---------|-------|-------|------|
| ProductHunt | Listing | | |
| Blog | Announcement | | |
| Email | Customer comms | | |
| Social | Posts | | |
| PR | Press release | | |
| Community | Posts | | |
### Assets Needed
- [ ] Landing page
- [ ] Demo video
- [ ] Blog post
- [ ] Social graphics
- [ ] Email templates
- [ ] Press kit
- [ ] Customer quotes
### Success Metrics
| Metric | Target | Actual |
|--------|--------|--------|
| Signups | {{N}} | |
| Traffic | {{N}} | |
| Mentions | {{N}} | |
| Trials | {{N}} | |
ProductHunt Launch Playbook
Before (4 weeks):
- Build hunter network
- Create assets (logo, screenshots, video)
- Write compelling tagline and description
- Prepare maker comment
- Coordinate with team for day-of support
- Schedule for Tuesday-Thursday
Launch Day:
- Launch at 12:01 AM PT
- Post maker comment immediately
- Share on social, email, communities
- Respond to ALL comments within hours
- Coordinate team upvotes (ethically)
- Update throughout the day
After:
- Thank supporters
- Follow up with interested users
- Publish retrospective
- Update based on feedback
Growth Loops
Loop Types
| Loop | Mechanism | Example |
|---|---|---|
| Viral | User invites users | Dropbox referrals |
| Content | Content → SEO → Users → Content | HubSpot |
| UGC | Users create content | YouTube, TikTok |
| Paid | Revenue → Paid ads → Users | Performance marketing |
| Sales | Revenue → Sales team → Users | Enterprise sales |
| Partner | Partners drive users → Revenue share | App stores |
Viral Loop Design
USER → CREATES/SHARES → CONTENT/INVITE
│
▼
NEW USER → CREATES/SHARES → ...
Viral Coefficient (K):
K = Invites per user × Conversion rate
Example:
Average invites: 5
Conversion rate: 20%
K = 5 × 0.20 = 1.0 (viral threshold)
Content Loop Design
CONTENT → SEO TRAFFIC → SIGNUPS → PRODUCT USAGE
↑ │
│ │
└────── USER-GENERATED DATA ─────────┘
AI-Powered GTM (2026)
AI has fundamentally changed GTM execution. Teams using AI report 12+ hours saved per week, shorter deal cycles, and higher win rates.
AI GTM Capabilities
| Capability | Description | Tools |
|---|---|---|
| Intent detection | Real-time buyer intent signals | Demandbase, 6sense, Bombora |
| Lead enrichment | Automated data enrichment at scale | ZoomInfo, Clearbit, Apollo |
| Outreach automation | AI-personalized sequences | Reply.io, Outreach, Salesloft |
| Content generation | Automated GTM content | Copy.ai, Jasper, Writer |
| Pipeline intelligence | AI forecasting and deal insights | Clari, Gong, Chorus |
| Conversation intelligence | Call analysis and coaching | Gong, Chorus, Fireflies |
GTM Engineer Role (Emerging 2026)
New hybrid role combining RevOps + engineering capabilities:
| Responsibility | Output |
|---|---|
| Build AI-driven automations | Lead routing, scoring, enrichment pipelines |
| Integrate GTM tech stack | Unified data across CRM, marketing, product |
| Deploy AI SDRs | Automated qualification and initial outreach |
| Create custom dashboards | Real-time GTM intelligence |
Key Trend: Small teams generating enterprise-level outreach volume without hiring 20 SDRs—enabled by AI automation with human oversight.
AI GTM Metrics Impact
| Metric | Pre-AI Baseline | AI-Enhanced |
|---|---|---|
| Lead processing time | Hours | Minutes |
| Personalization level | Segment-level | Individual |
| Deal cycle length | Standard | 20-30% shorter |
| Win rate | Baseline | 10-20% higher |
| SDR productivity | 50 touches/day | 200+ touches/day |
AI GTM Implementation
Phase 1: Foundation
- Unified data layer (CRM + enrichment + intent)
- Basic automation (lead routing, task creation)
Phase 2: Intelligence
- AI-powered lead scoring
- Conversation intelligence
- Automated content personalization
Phase 3: Autonomy
- AI SDRs for initial qualification
- Predictive pipeline management
- Automated expansion signals
Caution: AI augments human judgment—don't automate strategy, only execution. Humans own positioning, messaging, and deal negotiation.
RevOps Alignment (2026)
By 2026, 75% of fastest-growing companies will have RevOps. RevOps creates unified operational language across sales, marketing, and CS.
RevOps Team Structure
| Function | Focus | Deliverables |
|---|---|---|
| Business Partners | Pipeline, forecasting | Revenue forecasts, deal support |
| Business Process | Workflow design | Process documentation, SLAs |
| Revenue Technology | GTM tech stack | System administration, integrations |
| Process Innovation | AI use cases | Automation, efficiency gains |
Key RevOps Metrics (Board-Level 2026)
| Metric | Target | Why It Matters |
|---|---|---|
| NRR (Net Revenue Retention) | 120%+ | Expansion > churn |
| CAC Payback | <12 months | Capital efficiency |
| Pipeline Velocity | Increasing QoQ | Sales efficiency |
| GTM Cost Ratio | Decreasing | Operational leverage |
| Win Rate | Stable or increasing | Sales effectiveness |
RevOps + GTM Alignment Checklist
- Single source of truth for customer data
- Unified definitions (MQL, SQL, PQL, opportunity stages)
- Shared dashboards across sales, marketing, CS
- Regular GTM reviews (weekly pipeline, monthly strategy)
- Clear handoff SLAs between teams
- Attribution model agreed across functions
Expansion Strategy
Land and Expand
LAND (Initial)
└─► Single team, single use case, low ACV
ADOPT (Prove)
└─► Usage growth, success metrics, champions
EXPAND (Grow)
└─► More teams, departments, use cases
STRATEGIC (Transform)
└─► Company-wide, multi-year, executive sponsor
Expansion Signals
| Signal | Action |
|---|---|
| High usage | Proactive expansion conversation |
| New use case request | Cross-sell motion |
| Team growth | Seat expansion |
| Hitting limits | Upgrade conversation |
| Success metrics achieved | Case study + referral ask |
Geographic Expansion
| Phase | Markets | Approach |
|---|---|---|
| 1 | Home market | Direct |
| 2 | Adjacent (language/culture) | Localization |
| 3 | New regions | Local presence or partners |
Resources
| Resource | Purpose |
|---|---|
| channel-playbooks.md | Detailed channel execution guides |
| sales-motion-design.md | Sales process design + RevOps alignment |
| plg-implementation.md | PLG execution guide + PQL frameworks |
Templates
| Template | Purpose |
|---|---|
| gtm-strategy.md | Full GTM strategy document |
| launch-playbook.md | Launch planning template |
| icp-definition.md | ICP documentation |
Data
| File | Purpose |
|---|---|
| sources.json | GTM resources and guides |
Do / Avoid (Jan 2026)
Do
- Define activation as a concrete "first value moment" (and track "Oh wow moments" for retention).
- Track leading indicators (activation, PQL conversion, retention) alongside revenue.
- Run structured experiments with decision thresholds.
- Use AI for execution (outreach, enrichment, personalization) while humans own strategy.
- Align RevOps across sales, marketing, and CS with unified data and definitions.
- Tier your ICP and prioritize based on fit + intent signals.
Avoid
- Content spam without measurement.
- "Do all channels" in parallel without learning loops.
- Vanity metrics without retention and payback context.
- Over-automating without human oversight (AI augments, doesn't replace judgment).
- Treating ICP as static—revisit quarterly based on win/loss data.
What Good Looks Like
- ICP + positioning: one primary segment, explicit alternatives, and proof points (quotes, numbers, cases).
- Channel focus: 1 primary channel with a 4-week experiment plan and decision thresholds.
- Instrumentation: activation (“first value moment”) defined, tracked, and reviewed weekly.
- Launch plan: messaging, assets, owners, and a post-launch learning review scheduled.
- Feedback loop: win/loss + retention cohorts drive the next backlog decisions.
Optional: AI / Automation
Use only when explicitly requested and policy-compliant.
- Draft assets and experiment variants; humans verify claims, brand voice, and compliance.
- Summarize call notes and objections; keep source links and spot-check.
Score
Total Score
Based on repository quality metrics
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