Your VP Sales assigns territories randomly. Rep 1 gets the West Coast. Rep 2 gets the East Coast.
Six months later: Rep 1 is crushing quota (30 SaaS companies in their territory). Rep 2 is struggling (mostly manufacturing companies that don't fit ICP).
This happens because most territory planning ignores customer segmentation data and treats all territories equally.
Good territory planning isn't geography or random assignment. It's data-driven design based on customer concentration, ICP fit, and revenue potential.
Here's how PMM supports sales territory planning that drives revenue.
The Territory Planning Framework
PMM's role in territory planning:
Provide data:
- ICP analysis (where ideal customers are)
- Customer concentration maps (geographic and firmographic)
- Win/loss by segment (where we win best)
- TAM analysis by territory (revenue potential)
Inform strategy:
- Segment-based territories (vs. random geography)
- Specialized vs. generalist reps
- Territory potential scoring
Goal: Maximize revenue by matching reps to territories where they can win
Territory Planning Data PMM Provides
Data 1: Customer Concentration Analysis
Question: Where are our ideal customers located?
Analysis:
Geographic concentration:
- Map current customers by location
- Identify clusters (cities, regions with high concentration)
- Calculate customers per territory
Example:
| Territory | Total Companies | Current Customers | ICP Match % | Win Rate |
|---|---|---|---|---|
| SF Bay Area | 5,000 | 50 | 80% | 40% |
| NYC/NJ | 3,500 | 35 | 75% | 38% |
| Austin | 1,200 | 8 | 85% | 45% |
| Midwest | 2,000 | 5 | 30% | 15% |
Insights:
- High concentration in SF, NYC, Austin
- Midwest has low ICP match (mostly manufacturing vs. SaaS)
- Austin punches above weight (highest win rate)
Implication: Assign more reps to SF/NYC, specialized rep to Austin, don't invest heavily in Midwest yet
Data 2: ICP Fit by Region
Question: Which territories have highest concentration of ICP customers?
Analysis:
Criteria: B2B SaaS, 200-1,000 employees, $10M-$100M revenue
ICP concentration by territory:
| Territory | Total Prospects | ICP Prospects | ICP % | Avg ACV |
|---|---|---|---|---|
| SF Bay Area | 10,000 | 4,500 | 45% | $45K |
| NYC/NJ | 8,000 | 3,200 | 40% | $42K |
| Austin | 2,500 | 1,500 | 60% | $48K |
| Seattle | 3,000 | 1,800 | 60% | $46K |
| Midwest | 5,000 | 500 | 10% | $25K |
Insights:
- Austin and Seattle highest ICP density (60%)
- Midwest only 10% ICP match (wrong companies)
- SF Bay Area largest absolute number of ICPs
Implication: Prioritize Austin/Seattle (efficient territories), expand SF/NYC (large TAM), avoid Midwest for now
Data 3: TAM (Total Addressable Market) by Territory
Question: What's revenue potential in each territory?
Calculation:
TAM = (# of ICP prospects) × (Avg ACV) × (Market penetration assumption)
Example:
| Territory | ICP Prospects | Avg ACV | Penetration (10%) | TAM |
|---|---|---|---|---|
| SF Bay Area | 4,500 | $45K | 450 | $20.3M |
| NYC/NJ | 3,200 | $42K | 320 | $13.4M |
| Austin | 1,500 | $48K | 150 | $7.2M |
| Seattle | 1,800 | $46K | 180 | $8.3M |
Insights:
- SF Bay Area highest TAM ($20.3M)
- NYC second ($13.4M)
- Austin/Seattle smaller but efficient
Implication: Assign 2-3 reps to SF Bay Area, 2 reps to NYC, 1 rep to Austin/Seattle each
Data 4: Win/Loss by Segment
Question: Where do we win best?
Analysis:
| Segment | Opportunities | Wins | Win Rate | Avg Sales Cycle |
|---|---|---|---|---|
| Mid-Market SaaS | 100 | 40 | 40% | 60 days |
| Enterprise SaaS | 50 | 15 | 30% | 120 days |
| SMB Tech | 200 | 30 | 15% | 30 days |
| Healthcare | 30 | 3 | 10% | 150 days |
Insights:
- Win best with Mid-Market SaaS (40%)
- Enterprise longer cycle, lower win rate
- SMB/Healthcare poor fit
Implication: Focus territories on Mid-Market SaaS concentration, assign enterprise specialist for Enterprise segment
Territory Design Models
Model 1: Geographic Territories
How it works:
- Assign reps based on geography (West, East, Central)
- Reps cover all accounts in region
Pros:
- Simple to administer
- Clear ownership (no overlap)
- Good for evenly distributed customers
Cons:
- Ignores ICP concentration
- Some territories much better than others
- Reps cover accounts outside their expertise
When to use: Early stage (<10 reps), evenly distributed customer base
Model 2: Segment-Based Territories
How it works:
- Assign reps based on customer segment (SMB, Mid-Market, Enterprise)
- Reps specialize in serving one segment
Pros:
- Reps develop segment expertise
- Better product-market fit conversations
- Higher win rates (specialization)
Cons:
- Potential overlap (two reps in same geography)
- Requires coordination
- More complex to manage
When to use: Growth stage (10-50 reps), distinct customer segments with different needs
Example:
- SMB team: Covers all SMB accounts (nationwide)
- Mid-Market team: 50-500 employees (nationwide)
- Enterprise team: 500+ employees (nationwide)
Model 3: Industry/Vertical Territories
How it works:
- Assign reps based on industry vertical (Healthcare, FinTech, Retail)
- Reps become vertical experts
Pros:
- Deep industry expertise
- Speak customer language
- Better competitive positioning
Cons:
- Requires vertical specialization
- Limited territory size (fewer prospects)
- Harder to scale
When to use: Scale stage (50+ reps), when product has strong vertical use cases
Example:
- Healthcare rep: Covers all healthcare companies
- FinTech rep: Covers all financial services
- SaaS rep: Covers all B2B SaaS
Model 4: Hybrid (Segment + Geography)
How it works:
- Combine segment specialization with geographic coverage
- Example: "Mid-Market SaaS, West Coast"
Pros:
- Specialization + geographic efficiency
- Clear territories, less overlap
- Balanced workload
Cons:
- Most complex to design
- Requires more reps
When to use: Scale stage (50+ reps), geographically concentrated customer segments
Example:
- Rep 1: Mid-Market SaaS, SF Bay Area
- Rep 2: Mid-Market SaaS, NYC/NJ
- Rep 3: Enterprise SaaS, Nationwide
- Rep 4: SMB, West Coast
This is most common at scale.
How PMM Supports Territory Planning
Support 1: Provide Territory Scorecard
PMM creates territory analysis:
TERRITORY SCORECARD: SF Bay Area
Market Characteristics:
- Total companies in territory: 10,000
- ICP-match companies: 4,500 (45%)
- Current customers: 50 (1.1% penetration)
Revenue Potential:
- TAM (10% penetration): $20.3M
- Current ARR from territory: $2M
- Whitespace: $18.3M
Sales Performance:
- Current rep: Sarah (2 years)
- Win rate: 40% (vs. 35% company avg)
- Avg sales cycle: 55 days (vs. 65 avg)
- Pipeline: $5M
Customer Profile:
- Segment: 80% Mid-Market SaaS, 15% Enterprise, 5% Other
- Avg ACV: $45K
- Top use cases: Product launches, GTM coordination
Competitive Landscape:
- Main competitor: Competitor A (strong presence)
- Competitive win rate: 45%
Recommendation:
- Add 1-2 more reps (TAM supports 3-4 reps total)
- Focus on Mid-Market SaaS (80% of wins)
- Differentiate vs. Competitor A
Provide for each territory to inform planning.
Support 2: Model Territory Scenarios
PMM models:
Scenario A: Geographic Split (East/West)
- Rep 1: West (5,000 prospects, $25M TAM)
- Rep 2: East (5,000 prospects, $25M TAM)
- Pros: Simple, balanced
- Cons: Ignores ICP concentration
Scenario B: Segment Split (SMB/Mid/Enterprise)
- Team 1: SMB (nationwide, 2,000 prospects, $10M TAM)
- Team 2: Mid-Market (nationwide, 6,000 prospects, $35M TAM)
- Team 3: Enterprise (nationwide, 2,000 prospects, $30M TAM)
- Pros: Specialization, higher win rates
- Cons: Overlap, coordination needed
Scenario C: Hybrid (Segment + Geography)
- Rep 1: Mid-Market, West ($18M TAM)
- Rep 2: Mid-Market, East ($17M TAM)
- Rep 3: Enterprise, Nationwide ($30M TAM)
- Rep 4: SMB, Nationwide ($10M TAM)
- Pros: Specialization + geographic efficiency
- Cons: More complex
Recommendation: Scenario C (best balance)
Support 3: Create Territory Assignments
Based on ICP analysis, PMM recommends:
Rep 1: Sarah - Mid-Market SaaS, SF Bay Area
- Territory size: 2,000 ICP prospects
- TAM: $9M
- Focus: Product-led SaaS companies
- Quota: $2M
Rep 2: Tom - Mid-Market SaaS, NYC/NJ
- Territory size: 1,500 ICP prospects
- TAM: $6.5M
- Focus: Sales-led SaaS companies
- Quota: $1.8M
Rep 3: Maria - Enterprise SaaS, Nationwide
- Territory size: 500 ICP prospects
- TAM: $15M
- Focus: 500+ employee SaaS companies
- Quota: $3M
Clear ownership based on data.
Territory Balance and Quota Setting
Ensure fairness:
Territory quality score:
| Territory | Prospects | TAM | ICP % | Quota | TAM/Quota |
|---|---|---|---|---|---|
| Sarah (SF) | 2,000 | $9M | 55% | $2M | 4.5x |
| Tom (NYC) | 1,500 | $6.5M | 50% | $1.8M | 3.6x |
| Maria (Ent) | 500 | $15M | 70% | $3M | 5.0x |
Balance check:
- TAM/Quota ratio should be similar (3-5x)
- If imbalanced, adjust quotas or territories
Example: Tom's territory is weaker (3.6x vs. Sarah's 4.5x)
Options:
- Lower Tom's quota slightly ($1.6M vs. $1.8M)
- Add higher-value prospects to Tom's territory
- Accept imbalance (market reality)
Common Territory Planning Mistakes
Mistake 1: Random assignment
You assign territories without data
Problem: Imbalanced territories, poor results
Fix: Use ICP concentration and TAM data
Mistake 2: Geography-only
You only consider geography, ignore segments
Problem: Reps cover accounts they can't close
Fix: Segment-based or hybrid territories
Mistake 3: Set-and-forget
You design territories once, never adjust
Problem: Market changes, territories become stale
Fix: Review territories annually, rebalance
Mistake 4: Ignoring PMM data
Sales plans territories without PMM input
Problem: Miss ICP concentration insights
Fix: PMM provides territory scorecards
Mistake 5: No specialization
All reps are generalists covering everything
Problem: No expertise, lower win rates
Fix: Segment or vertical specialization (as you scale)
Quick Start: Inform Territory Planning in 2 Weeks
Week 1:
- Map current customers (geography, firmographics)
- Identify ICP concentration (where ICPs cluster)
- Calculate TAM by territory
Week 2:
- Analyze win/loss by segment and territory
- Create territory scorecards
- Model scenarios (geographic vs. segment vs. hybrid)
Deliverable: Territory analysis and recommendations for sales leadership
Impact: Data-driven territories vs. random assignment
The Uncomfortable Truth
Here's what actually happens: Most territory planning is done without data. Sales VPs assign territories randomly based on gut feel or who joined when. They use only geography—Northeast, Southwest, West—ignoring that some segments or verticals cluster in specific areas. They don't consider ICP concentration, so some reps have territories rich with ideal customers while others are assigned areas where your ICP barely exists. And they never rebalance once territories are set, even as markets shift and rep performance diverges.
The result is entirely predictable. Imbalanced territories where some reps have $5M TAM and others have $500K but equal quotas. Reps covering the wrong accounts because territory assignment doesn't match where they have expertise or where customers actually cluster. And wildly different win rates by territory—not because some reps are better, but because some territories are fundamentally easier.
What actually works is PMM providing ICP concentration data so sales leadership knows where your best-fit customers actually are. TAM analysis by territory that shows revenue potential and helps set fair quotas. Win/loss analysis by segment that reveals where you win at higher rates. Segment-based or hybrid territory models that let reps specialize. And annual rebalancing that adjusts territories based on actual performance data.
The best territory planning I've seen is data-driven from day one. ICP concentration mapping, TAM calculations, historical win rate analysis—all inform the territory model. Territories are balanced with similar TAM-to-quota ratios so reps compete on fair footing. They're specialized by segment or vertical so reps build deep expertise. They're reviewed annually and rebalanced based on performance data. And they're designed collaboratively by Sales, PMM, and RevOps so everyone's insights are incorporated.
If your reps have wildly different win rates by territory—some at 45%, others at 15%—you have a territory planning problem, not a rep performance problem. The playing field isn't level, and no amount of coaching will fix structural imbalance.
Provide the data. Model different scenarios. Push for balanced, specialized territories. That's how PMM influences territory planning in ways that actually improve win rates across the board.