# GTM Strategist Playbook

This is the reference playbook for the gtm-strategist skill. Read the relevant section before producing any GTM strategy artifact.

## Table of Contents

1. ICP Framework - Deep Methodology
2. Segmentation Models - Templates and Scoring
3. Funnel Architecture - Stage Definitions and Benchmarks
4. Pipeline Math - Formulas and Models
5. Capacity Planning - Methodology and Templates
6. GTM Model Decision Trees
7. Diagnostic Frameworks
8. Benchmarks by Company Stage and ACV

---

## 1. ICP Framework - Deep Methodology

### The ICP Development Process

ICP is not a brainstorming exercise. It's a pattern recognition exercise that starts with existing customer data and validates against market opportunity.

**Step 1: Mine existing wins**

Pull the last 12-24 months of closed-won deals. For each, capture:
- Firmographics (industry, size, geography, tech stack)
- Deal characteristics (ACV, cycle length, discount, expansion)
- Entry path (inbound, outbound, PLG, community, referral, partner)
- Buying committee (who was involved, who championed, who blocked)
- Trigger event (what initiated the buying process)
- Competitive situation (who else was evaluated, why did we win)

**Step 2: Identify clusters**

Look for patterns. The goal is to find 2-4 clusters that share:
- Similar firmographic profiles
- Similar trigger events
- Similar buying processes
- Similar outcomes (ACV, cycle length, satisfaction, expansion)

These clusters become your candidate segments. Name them concretely (e.g., "Mid-market SaaS companies outgrowing Jamf" not "Tech companies").

**Step 3: Score segments**

For each candidate segment, score on five dimensions:

| Dimension | Weight | How to Evaluate |
|-----------|--------|-----------------|
| Win Rate | 25% | Historical close rate in this segment vs. overall |
| Deal Size | 20% | Average ACV in this segment vs. target ACV |
| Velocity | 15% | Average cycle length vs. overall |
| Expansion | 20% | Net revenue retention in this segment |
| Addressability | 20% | How many accounts fit this profile that you can reach |

Composite score determines priority: Invest (top segment), Grow (promising), Maintain (profitable but limited), Deprioritize (poor fit).

**Step 4: Build the detailed ICP for top segments**

For your top 1-2 segments, build the full ICP profile (firmographic filters, situational triggers, buying committee map, negative filters, validation signals - as outlined in SKILL.md).

**Step 5: Validate with the field**

Present the ICP to the 3-5 best-performing reps. Ask:
- "Does this match your best customers?"
- "What's missing?"
- "Who would you add or remove?"
- "What trigger events do you see that aren't captured?"

Reps see patterns that data misses, especially around trigger events and buying committee dynamics.

**Step 6: Operationalize**

An ICP that lives in a document is useless. Operationalize by:
- Encoding firmographic filters into account scoring in CRM
- Training SDRs on trigger event identification
- Building target account lists against the ICP
- Creating messaging maps for each buying committee persona
- Establishing quarterly ICP review cadence (markets shift, ICPs should too)

### ICP Anti-Patterns to Flag

- **"Our ICP is any company with 500+ employees"** - This is a firmographic filter, not an ICP. It has no trigger, no buying committee, no behavioral component.
- **"We sell to IT"** - IT is not a persona. "Director of IT Operations at companies migrating from legacy MDM during contract renewal" is a persona.
- **"We target Fortune 500"** - This is a vanity target. Win rate in Fortune 500 is often lower than in mid-market because of incumbent entrenchment and procurement complexity. Validate with data.
- **"Everyone is our customer"** - If everyone is your customer, no one is. Focus creates pipeline; breadth dilutes it.

### Buying Committee Mapping Template

For each persona in the buying committee:

```
PERSONA: [Title pattern, e.g., "Director of IT / IT Operations Manager"]

ROLE IN DEAL: [Economic Buyer / Technical Evaluator / Champion / Influencer / Blocker]

GOALS:
  - What are they measured on?
  - What does success look like for them?
  - What would get them promoted?

FEARS:
  - What keeps them up at night?
  - What would get them fired?
  - What risks do they want to avoid?

EVALUATION CRITERIA:
  - What do they care about when assessing solutions?
  - What questions will they ask?
  - What proof do they need?

TRIGGER EVENTS:
  - What changes in their world make them open to a conversation?
  - What events create urgency?

CONTENT PREFERENCES:
  - What formats do they consume? (Technical docs, case studies, ROI analysis, peer validation)
  - Where do they go for information? (Analyst reports, community forums, peer networks, conferences)

OBJECTIONS:
  - What will they push back on?
  - What concerns will they raise?
  - How do you address each?
```

---

## 2. Segmentation Models

### Needs-Based Segmentation Framework

Firmographic segmentation (by size, industry, geography) is the starting point but not the destination. Needs-based segmentation groups accounts by what they're trying to accomplish, which better predicts buying behavior.

**Step 1: Identify the primary needs dimensions**

Common dimensions for B2B SaaS:
- **Complexity of problem** - Simple (one use case) vs. complex (platform need)
- **Urgency of need** - Planned purchase vs. crisis-driven
- **Decision authority** - Centralized vs. distributed buying
- **Technical sophistication** - Self-serve capable vs. needs hand-holding
- **Regulatory pressure** - Compliance-driven vs. discretionary

**Step 2: Map segments on a 2x2**

Pick the two dimensions that most differentiate buying behavior in your market. Map your addressable market into four quadrants. Each quadrant becomes a segment with distinct GTM implications.

Example for a device management company:

```
                    High Compliance Pressure
                           |
    SEGMENT A:             |           SEGMENT B:
    "Regulated Enterprise" |           "Compliance-Driven Mid-Market"
    - Large, complex       |           - Smaller but heavily regulated
    - Long cycle, high ACV |           - Faster cycle, moderate ACV
    - Need self-hosted     |           - Need compliance features
    - Buy through RFP      |           - Champion-driven purchase
                           |
  Complex ─────────────────┼──────────────────── Simple
                           |
    SEGMENT C:             |           SEGMENT D:
    "Tech-Forward Enter."  |           "Agile Mid-Market"
    - Large, DevOps-mature |           - Smaller, modern stack
    - Value OSS, GitOps    |           - Speed of deployment matters
    - Technical evaluation |           - Self-serve or light-touch
    - Competitive displace.|           - PLG entry, expand to enterprise
                           |
                    Low Compliance Pressure
```

**Step 3: Size each segment**

For each segment, estimate:
- Total accounts that fit the profile
- Average deal size (based on historical wins or informed estimate)
- Segment TAM = accounts × average deal size
- Segment accessibility (how easily you can identify and reach these accounts)
- Competitive intensity (who else is pursuing this segment)

**Step 4: Prioritize**

Use a weighted scoring model:

| Factor | Weight | Description |
|--------|--------|-------------|
| Segment size (revenue potential) | 20% | Total addressable revenue |
| Win rate expectation | 25% | Based on historical data or competitive analysis |
| Strategic value | 15% | Brand logos, reference potential, market signaling |
| Cost to serve | 15% | Sales cycle complexity, support requirements |
| Expansion potential | 15% | NRR expectation, cross-sell opportunity |
| Competitive defensibility | 10% | How hard it is for competitors to displace you |

### Segment Activation Checklist

Before investing in a segment, ensure you have:
- [ ] 3+ reference customers in the segment
- [ ] Validated messaging for the segment's primary persona
- [ ] A list of 50+ accounts that fit the segment profile
- [ ] A clear entry point (which product/feature/use case opens the door)
- [ ] At least 1 case study or proof point relevant to the segment
- [ ] SDR team briefed on segment triggers and qualification criteria

---

## 3. Funnel Architecture

### Enterprise B2B Funnel Stage Definitions

The following stage model works for enterprise B2B SaaS with $50K+ ACV and 3-6 month sales cycles. Adjust stage names to match company terminology, but preserve the logic.

```
STAGE 0: TARGET ACCOUNT
  Definition: Account matches ICP criteria and is on the target list
  Entry: Appears on target account list based on ICP scoring
  Exit: Meaningful engagement established (meeting booked, inbound request, PQL trigger)
  Owner: Marketing + SDR
  Key metric: % of target accounts engaged

STAGE 1: DISCOVERY / QUALIFICATION
  Definition: Initial conversation(s) to validate fit and identify need
  Entry: First meeting held with a relevant contact
  Exit: BANT or MEDDIC qualification criteria met; opportunity created in CRM
  Owner: SDR → AE handoff occurs here
  Key metric: Meeting-to-opportunity conversion rate
  Benchmark: 30-50% of meetings should convert to qualified opportunities
  Time in stage: 1-3 weeks
  Red flags: >3 weeks without advancing; single-threaded; no pain articulated

STAGE 2: SCOPING / TECHNICAL EVALUATION
  Definition: Active evaluation underway; technical fit being validated
  Entry: Qualified opportunity with defined requirements
  Exit: Technical validation complete; champion confirmed; buying process understood
  Owner: AE + SE (Solutions Engineer)
  Key metric: Technical win rate
  Benchmark: 60-75% should advance
  Time in stage: 2-6 weeks
  Red flags: No access to technical evaluator; competitor POC running in parallel with no differentiation strategy; evaluation criteria unclear

STAGE 3: BUSINESS CASE / PROPOSAL
  Definition: Technical fit confirmed; building economic justification
  Entry: Technical validation passed; economic buyer identified
  Exit: Proposal delivered; pricing and terms under discussion
  Owner: AE
  Key metric: Proposal-to-negotiation conversion rate
  Benchmark: 70-85% should advance
  Time in stage: 1-3 weeks
  Red flags: Economic buyer not engaged; no compelling event; ROI case not clear

STAGE 4: NEGOTIATION / PROCUREMENT
  Definition: Commercial terms under discussion; legal/procurement involved
  Entry: Proposal accepted in principle; terms negotiation begun
  Exit: Contract signed or deal lost
  Owner: AE + Deal Desk / Legal
  Key metric: Close rate (from negotiation)
  Benchmark: 75-90% should close
  Time in stage: 2-6 weeks (highly variable by company size)
  Red flags: New stakeholders appearing; legal review with no timeline; budget re-allocation requests

STAGE 5: CLOSED-WON
  Definition: Contract signed; revenue recognized
  Handoff: AE → CS/Onboarding
```

### Multi-Entry Funnel Model

Enterprise companies typically have 3-5 entry paths. Each enters the funnel at a different stage:

```
OUTBOUND (SDR-generated)    → Enters at Stage 0/1
INBOUND (marketing-generated) → Enters at Stage 1
PLG (product-qualified)      → Enters at Stage 1 or 2 (already using product)
COMMUNITY (OSS-qualified)    → Enters at Stage 1 or 2 (already familiar)
PARTNER (channel-generated)  → Enters at Stage 1 or 2
```

Track conversion rates by entry path separately - they will differ significantly. PLG and community entries typically have higher conversion rates but may have lower ACV. Outbound typically has lower conversion but can target higher-value accounts.

### Funnel Health Metrics

Monitor these weekly:

| Metric | What It Tells You | Alarm Threshold |
|--------|-------------------|-----------------|
| Stage 0→1 conversion | Are we engaging target accounts? | <5% (outbound), <15% (inbound) |
| Stage 1→2 conversion | Are we qualifying well? | <30% |
| Stage 2→3 conversion | Is the product winning evaluations? | <50% |
| Stage 3→4 conversion | Is the business case landing? | <65% |
| Stage 4→5 conversion | Are we closing what we should? | <70% |
| Average days per stage | Are deals moving? | >2x benchmark for any stage |
| Pipeline coverage ratio | Do we have enough? | <3x for the quarter |
| Pipeline creation rate | Are we building fast enough? | Below monthly target for 2 consecutive months |

---

## 4. Pipeline Math

### The Pipeline Math Model

Work backward from revenue target. Every number must trace to an assumption.

```
ANNUAL REVENUE TARGET:                    $__________

÷ Average ACV:                            $__________
= DEALS NEEDED:                           __________

÷ Stage 4→5 close rate:                   __________% 
= DEALS ENTERING NEGOTIATION:             __________

÷ Stage 3→4 conversion rate:              __________%
= PROPOSALS NEEDED:                       __________

÷ Stage 2→3 conversion rate:              __________%
= TECHNICAL EVALUATIONS NEEDED:           __________

÷ Stage 1→2 conversion rate:              __________%
= QUALIFIED OPPORTUNITIES NEEDED:         __________

÷ Meeting-to-opportunity rate:             __________%
= MEETINGS NEEDED:                        __________

÷ Outreach-to-meeting rate:               __________%
= OUTREACH VOLUME NEEDED:                 __________
```

**Critical assumption: Pipeline coverage ratio**

Pipeline coverage = total pipeline value ÷ revenue target for the period. Standard benchmarks:

| ACV Range | Recommended Coverage | Why |
|-----------|---------------------|-----|
| $10-50K | 3x quarterly target | Shorter cycles, more predictable conversion |
| $50-150K | 3.5-4x quarterly target | Longer cycles, more variability |
| $150K+ | 4-5x quarterly target | Fewer deals, each one matters more |

**Pipeline creation rate**

Monthly pipeline creation must exceed monthly pipeline consumption (closed-won + closed-lost + slipped). If consumption exceeds creation for 2+ consecutive months, you will miss the quarter.

```
MONTHLY PIPELINE CREATION TARGET = 
  (Quarterly revenue target × coverage ratio) ÷ average months pipeline is open

Example:
  $5M quarterly target × 4x coverage = $20M pipeline needed
  Average deal stays in pipeline 4 months
  Monthly creation target = $20M ÷ 4 = $5M new pipeline per month
```

### Pipeline Math by Source

Break pipeline creation targets by source to hold each function accountable:

```
TOTAL MONTHLY PIPELINE CREATION TARGET: $__________

  Outbound (SDR-generated):    ___% = $__________
  Inbound (marketing-generated): ___% = $__________
  PLG (product-qualified):     ___% = $__________
  Community (OSS-qualified):   ___% = $__________
  Partner (channel-generated): ___% = $__________
  AE-sourced (expansion/ref):  ___% = $__________
                               ─────
                               100%
```

The mix will vary by company stage and GTM model. Typical for scale-stage enterprise:
- Outbound: 30-40%
- Inbound: 20-30%
- PLG/Community: 10-20% (higher for OSS companies)
- Partner: 5-15%
- AE-sourced: 10-20%

---

## 5. Capacity Planning

### Sales Capacity Model

**Step 1: Determine quota per rep**

```
Annual quota per AE = Target OTE × quota-to-OTE ratio

Typical ratios:
  - $50-100K ACV: 4-6x OTE
  - $100-250K ACV: 5-8x OTE
  - $250K+ ACV: 6-10x OTE

Example: AE with $200K OTE, 6x ratio → $1.2M annual quota
```

**Step 2: Account for ramp and attrition**

Not every rep is at full capacity. Adjust:

```
EFFECTIVE CAPACITY per rep = Annual quota × attainment adjustment

Attainment adjustment factors:
  - Fully ramped rep (12+ months): 85-100% of quota (average attainment is ~85%)
  - Ramping rep (6-12 months): 50-75% of quota
  - New rep (0-6 months): 0-25% of quota

  - Attrition assumption: plan for 15-25% annual sales attrition
  - Each departure costs ~6 months of productive capacity (backfill + ramp)
```

**Step 3: Calculate headcount needed**

```
TOTAL QUOTA CAPACITY NEEDED = Annual revenue target ÷ average attainment %

REPS NEEDED = Total quota capacity ÷ quota per rep

  + Ramp buffer (reps in ramp at any time)
  + Attrition buffer (expected departures)
  = TOTAL HEADCOUNT PLAN

Example:
  $15M target ÷ 85% avg attainment = $17.6M quota capacity needed
  $17.6M ÷ $1.2M per rep = ~15 fully productive reps
  + 3 reps in ramp
  + 2 buffer for attrition
  = 20 total AE headcount plan
```

**Step 4: SDR capacity**

```
SDRs NEEDED = Outbound pipeline target ÷ pipeline per SDR per month ÷ 12

Pipeline per SDR per month depends on:
  - ACV (higher ACV = fewer but larger opportunities)
  - Market maturity (established category = higher response rates)
  - Signal quality (better signals = higher conversion)

Benchmarks:
  - $50-100K ACV: $150-300K pipeline created per SDR per month
  - $100-250K ACV: $200-500K pipeline created per SDR per month
  - $250K+ ACV: $300-750K pipeline created per SDR per month

SDR-to-AE ratio: typically 2:1 to 3:1 for enterprise, 1:1 to 2:1 for mid-market
```

**Step 5: Build the hiring timeline**

Work backward from when capacity is needed:
- AE: 4-6 month ramp → hire 6 months before capacity is needed
- SDR: 2-3 month ramp → hire 4 months before pipeline generation is needed
- Always hire in cohorts (minimum 2) for training efficiency and peer learning

### Capacity Planning Anti-Patterns

- **Planning at 100% attainment** - Average attainment is 60-85%. Planning at 100% guarantees a miss.
- **Ignoring ramp time** - A Q1 hire doesn't produce meaningful Q1 pipeline. They produce Q3 pipeline.
- **Single-point forecasting** - Build three scenarios (base, upside, downside) with different assumption sets.
- **Static annual plans** - Revisit capacity quarterly. Markets change, attrition happens, segments shift.

---

## 6. GTM Model Decision Trees

### PLG vs. Sales-Led vs. Hybrid

**Pure PLG works when:**
- ACV is <$25K
- Product is self-serve evaluable
- Buyer can implement without IT/procurement involvement
- Time to value is <1 day
- Category is understood (buyer knows they need this type of tool)

**Pure Sales-Led works when:**
- ACV is >$100K
- Product requires technical integration and configuration
- Multiple stakeholders must align
- Compliance/security review is required
- Competitive displacement is the primary motion

**Hybrid is right when (most common at scale):**
- ACV range spans from self-serve ($5-25K) to enterprise ($100K+)
- Product has both simple and complex use cases
- Community or PLG creates awareness that enterprise sales converts
- Different segments prefer different buying experiences

**For hybrid models, define the boundary:**
```
SELF-SERVE LANE:
  - ACV: <$___K
  - User count: <___ seats/devices
  - Features: Standard tier only
  - Support: Community + docs
  - Conversion: Automated nurture → upgrade prompt → self-serve purchase

SALES-ASSISTED LANE:
  - ACV: $___K - $___K
  - Trigger: Usage threshold, enterprise feature request, or inbound request
  - Motion: SDR qualification → AE discovery → standard sales process
  - Handoff: PQL or community signal → SDR outreach within 24 hours

ENTERPRISE LANE:
  - ACV: >$___K
  - Trigger: Named account engagement, executive referral, or RFP
  - Motion: Account-based engagement → multi-threaded sales process
  - Handoff: ABM signal or executive engagement → AE-led process
```

### Community-Led + Enterprise (OSS Model)

This is the model for companies like Fleet DM, GitLab, HashiCorp, Elastic, etc.

```
COMMUNITY FUNNEL:
  Awareness → Download/Star → Deploy → Use → Hit Limit → Evaluate Enterprise → Buy

KEY METRICS:
  - Community size (GitHub stars, Slack members, downloads)
  - Deployment signals (active instances, device count thresholds)
  - Enterprise feature interest (config attempts, docs page views, support requests)
  - Conversion rate: community user → enterprise evaluation
  - Time from first deployment to enterprise purchase

CRITICAL HANDOFF POINTS:
  1. DEPLOYMENT THRESHOLD: When usage exceeds free tier limits
  2. ENTERPRISE FEATURE TRIGGER: When user attempts enterprise-only features
  3. SUPPORT ESCALATION: When community support isn't sufficient
  4. COMPLIANCE NEED: When audit/compliance requirements emerge
  5. EXECUTIVE ENGAGEMENT: When management asks "are we paying for this?"

Each trigger should route to a specific response:
  - Automated nurture for low-urgency signals
  - SDR outreach for medium-urgency signals  
  - AE engagement for high-urgency signals (budget, timeline, authority confirmed)
```

---

## 7. Diagnostic Frameworks

### Pipeline Gap Diagnosis

When pipeline is below target, systematically diagnose the root cause:

**Level 1: Coverage or Conversion?**
```
IF total pipeline value < coverage ratio × target → COVERAGE PROBLEM
  (Not enough opportunities entering the funnel)

IF total pipeline value ≥ coverage ratio × target BUT revenue is behind → CONVERSION PROBLEM
  (Enough pipeline exists but deals aren't closing)
```

**Level 2 (Coverage): Where is creation failing?**
```
Check each source against its creation target:
  - Outbound below target? → SDR capacity, signal quality, or targeting issue
  - Inbound below target? → Marketing demand gen, content, or web conversion issue  
  - PLG/Community below target? → Product engagement, conversion triggers, or handoff issue
  - Partner below target? → Partner enablement, co-sell execution, or program maturity issue
```

**Level 2 (Conversion): Where are deals stalling?**
```
Check conversion rates stage by stage:
  - Stage 1→2 low? → Qualification criteria too loose or discovery process weak
  - Stage 2→3 low? → Technical evaluation failing or competitive loss
  - Stage 3→4 low? → Business case not compelling or champion not strong enough
  - Stage 4→5 low? → Procurement/legal issues or deal terms misaligned
  
Check velocity:
  - Average days in any stage >2x benchmark? → Deals are stalling, not dying
  - Stalling usually means: missing stakeholder, no compelling event, or champion lost momentum
```

**Level 3: Structural or Execution?**
```
STRUCTURAL issues (fix the system):
  - ICP is wrong (winning in a different segment than targeted)
  - Funnel stages are poorly defined (teams disagree on what "qualified" means)
  - Handoffs are broken (leads fall between marketing and sales)
  - Metrics aren't tracked (can't diagnose because data doesn't exist)

EXECUTION issues (fix the process):
  - SDRs aren't following the playbook
  - AEs aren't multi-threading deals
  - Discovery is shallow (reps jump to demo too fast)
  - Follow-up cadence is inconsistent
```

### GTM Alignment Audit

Quick diagnostic for cross-functional alignment:

```
Ask each team lead independently:
  1. "Describe your ICP in one sentence"
  2. "What are the top 3 trigger events that create buying urgency?"
  3. "What are the stages of our funnel?"
  4. "What's our pipeline coverage target?"
  5. "What % of pipeline should come from each source?"

Score: How consistent are the answers?
  - All aligned: Good foundation, focus on execution
  - Mostly aligned with gaps: Fix the gaps, then execute
  - Significantly different answers: STOP. Align on fundamentals before optimizing anything
```

---

## 8. Benchmarks by Company Stage and ACV

### Conversion Rate Benchmarks

| Metric | $25-50K ACV | $50-150K ACV | $150K+ ACV |
|--------|-------------|--------------|------------|
| Meeting → Opportunity | 35-45% | 30-40% | 25-35% |
| Opportunity → Technical Eval | 60-70% | 55-65% | 50-60% |
| Technical Eval → Proposal | 65-75% | 60-70% | 55-65% |
| Proposal → Close | 70-80% | 65-75% | 60-70% |
| Overall meeting → close | 10-18% | 7-14% | 5-10% |

### Sales Cycle Benchmarks

| ACV Range | Average Cycle | 25th Percentile | 75th Percentile |
|-----------|--------------|-----------------|-----------------|
| $25-50K | 45-60 days | 30 days | 90 days |
| $50-100K | 60-90 days | 45 days | 120 days |
| $100-250K | 90-120 days | 60 days | 180 days |
| $250K+ | 120-180 days | 90 days | 270 days |

### Pipeline Coverage Benchmarks

| ACV Range | Minimum Coverage | Recommended | Stretch |
|-----------|-----------------|-------------|---------|
| $25-50K | 2.5x | 3x | 4x |
| $50-150K | 3x | 3.5-4x | 5x |
| $150K+ | 3.5x | 4-5x | 6x |

### SDR Productivity Benchmarks

| Metric | Inbound SDR | Outbound SDR |
|--------|------------|--------------|
| Meetings booked / month | 15-25 | 8-15 |
| Pipeline created / month | $200-500K | $150-400K |
| Activities / day | 30-50 | 50-80 |
| Connect rate (calls) | 5-10% | 3-7% |
| Email reply rate | 8-15% | 3-8% |
| LinkedIn connect rate | 20-35% | 15-25% |

Note: These benchmarks assume enterprise B2B SaaS with reasonable brand awareness. Early-stage companies or new market entrants will typically perform 30-50% below these benchmarks until brand recognition builds.
