Designing Territories and Quotas

• Source: Report research library
• Frameworks applied: Fullcast territory-based capacity modeling (hours-per-account formula, territory scoring, 85-115 balance band, scenario modeling), ramped-equivalent headcount, quota over-assignment, Clari required coverage = 1 / win rate feasibility test, Bessemer growth endurance and CAC payback guardrails

Explicitly refuses to present unpublished ramp curves and quota-to-OTE multiples as benchmarks and labels them local convention, which is the correct epistemic move.

Known weakness, published as found: Add one worked numeric example through the capacity and quota formulas; five fenced formula blocks with no sample values leave the agent guessing at units and rounding. Deconflict quota ownership with designing-sales-compensation so the two skills do not both fire on 'set quotas'.

1. 1.Copy the SKILL.md text below, or download the raw file.
2. 2.Create a folder named exactly designing-territories-and-quotas in your agent's skills directory.
3. 3.Save the file inside that folder as SKILL.md.
4. 4.Ask the agent one of the trigger requests below.
5. 5.Check the output against what you already know before it leaves your desk.

• We are hiring 12 AEs next year against a $30M new-ARR plan - build the capacity model and tell me if the plan is coverable
• Carve these accounts into eight balanced territories and flag any patch that is overloaded
• Set FY quotas including ramp for the five reps starting in Q2, with 15% over-assignment

• What is our pipeline coverage this quarter and are we going to hit the number?
• Compute NRR by ACV band and compare us to the SaaS Capital medians

---
name: designing-territories-and-quotas
description: >-
  Carves balanced sales territories from account scoring, sizes selling capacity
  from productivity and ramp assumptions, and sets quotas that roll up to plan
  with an explicit over-assignment and pipeline-coverage check. Use when the user
  says territory design, territory carve, territory balance, account assignment,
  book of business, patch, capacity model, capacity plan, how many reps do we
  need, quota setting, quota allocation, quota coverage, over-assignment, ramp
  schedule, ramped rep productivity, headcount plan, or hands over an account
  list or headcount roster to split and load with numbers. Do NOT use for
  computing NRR, CAC payback, or Rule of 40 (see modeling-saas-revenue-metrics),
  for building an in-period forecast or gap-to-plan call (see
  forecasting-pipeline-coverage), or for cleaning account records and duplicates
  (see auditing-crm-data-hygiene).
metadata:
  version: "1.0"
---

# Designing territories and quotas

Produce a territory map with balance scores, a capacity model that states how
many ramped selling months the plan actually has, and a quota sheet that rolls up
to plan with over-assignment. One job: annual or mid-year territory and quota
design. In-period forecasting, comp-plan mechanics such as accelerator rates, and
CRM record cleanup are out of scope.

## Workflow

Copy this checklist into your reply and tick items as you finish:

```
- [ ] 1. Collect and freeze capacity inputs
- [ ] 2. Score and segment the account universe
- [ ] 3. Compute per-rep capacity and derive required headcount
- [ ] 4. Carve territories and score balance
- [ ] 5. Apply ramp to convert headcount into productive capacity
- [ ] 6. Set quotas, over-assign, and check pipeline coverage feasibility
- [ ] 7. Validate against the checks; rebalance and re-run until clean
- [ ] 8. Publish the plan and deploy to CRM
```

**1. Collect and freeze capacity inputs.** Non-negotiable, because a mid-model
input change invalidates every territory score computed before it. Collect the
three input classes named by the territory-based capacity model
([Fullcast](https://www.fullcast.com/content/territory-based-capacity-modeling/)):

- Rep-level: annual quota for a fully ramped rep, target account count per rep,
  average activities per deal (calls, meetings, demos), ramp time for new hires.
- Account-level: ICP definition, firmographics, market segment, potential TAM
  per account.
- Sales-cycle: average deal size, historical win rates by segment, average cycle
  length.

Record each input's source and as-of date in the plan, because territory disputes
are almost always disputes about an input, not about the arithmetic.

**2. Score and segment the account universe.** Score and prioritize accounts by
geography and vertical using TAM and ICP data, then allocate rep time to the
highest-potential targets rather than merely assigning accounts
([Fullcast](https://www.fullcast.com/content/territory-based-capacity-modeling/)).
Judgment step: choose the scoring dimensions that actually predict win rate in
this business (spend, employee count, tech-stack fit, existing footprint, prior
engagement) and state the weight and rationale for each. Do not carry more than
five dimensions, because past that the score stops being defensible to the field.

**3. Compute per-rep capacity and required headcount.** Use both the time-based
formula and the quota-based cross-check, and reconcile them:

```
Max accounts per rep = Total working hours per year
                       / Hours required per account per year

Deals needed per rep = Ramped annual quota / Average deal size
Opportunities needed = Deals needed per rep / Win rate
Selling capacity per rep = Working hours per year
                           x (1 - non-selling time %)
Required ramped reps = Plan number / Ramped annual quota per rep
```

The hours formula is the core mechanic
([Fullcast](https://www.fullcast.com/content/territory-based-capacity-modeling/)).
Derive hours per account from average activities per deal times duration, times
the opportunities needed per deal won, because deriving it from win rate keeps the
model consistent with the coverage math in step 6. Subtract non-selling time
(internal meetings, training, admin, PTO) explicitly as a stated percentage rather
than embedding it in the hours figure, so the assumption can be challenged.

**4. Carve territories and score balance.** Build a territory score per patch
from account count, weighted account potential, and required hours, then index
each patch against the mean. Use the published balance thresholds
([Fullcast](https://www.fullcast.com/content/territory-based-capacity-modeling/)):

| Territory score | Reading | Action |
|---|---|---|
| < 85 | Wasted capacity; the rep lacks the opportunity to hit quota | Add accounts or merge patches |
| 85-115 | Balanced | Accept |
| > 115 | Rep overload, burnout risk, high-potential accounts neglected | Split the patch or add heads |

Many teams target a territory-score range of 800-1,200 points on the underlying
point system (same source). A dense metro may need three reps where a traditional
plan assigns one, so carve on potential and required hours, not on map area or
account count (same source). Also model scenarios before committing: run what-ifs
such as hiring ten more reps, entering a new market, or a product launch, and use
performance-to-plan tracking before changes go live (same source).

**5. Apply ramp.** Convert headcount into productive capacity before quotas are
set, because a plan built on head count rather than ramped selling months
overstates capacity by roughly the ramp period:

```
Ramped-equivalent months for a hire =
  Sum over plan months of (productivity % in that month)

Productive capacity = Sum over reps of
  (Ramped annual quota x Ramped-equivalent months / 12)

Coverage of plan = Productive capacity / Plan number
```

Ramp time for new hires is a required rep-level input
([Fullcast](https://www.fullcast.com/content/territory-based-capacity-modeling/)),
but no numerical ramp curve or quota-to-OTE multiple is published there. Set the
ramp curve and any quota-to-OTE multiple from this company's own cohort history
and label them in the plan as local convention, not benchmark, because presenting
an unsourced multiple as an industry standard is how a plan loses credibility in
the first field review. If no internal history exists, state the assumed curve
explicitly and mark it as an assumption to be back-tested after two hire cohorts.

**6. Set quotas and test feasibility.** Compute in this order:

```
Sum of individual quotas = Plan number x (1 + Over-assignment %)
Individual quota = Ramped annual quota x Ramped-equivalent months / 12
                   x Territory score / 100
Quota per rep as a check = Deals needed x Average deal size

Required pipeline coverage = 1 / Win rate
Pipeline required per rep  = Individual quota x Required coverage
Feasibility test = Addressable pipeline potential in the patch
                   >= Pipeline required per rep
```

Required coverage of 1 divided by win rate replaces "3x" as a universal rule, and
most B2B teams target 3x-5x while enterprise motions with 15-25% win rates need
4x-7x ([Clari](https://www.clari.com/blog/pipeline-coverage-best-practices/)). Any
territory that fails the feasibility test is over-quota'd regardless of its
balance score, because the pipeline to support the number does not exist in the
patch. Fix by moving accounts, lowering the quota, or funding demand generation,
and name which.

Sanity-check aggregate growth implied by the plan against private B2B SaaS median
growth of 22% in 2025 versus 25% in 2024
([SaaS Capital](https://www.saas-capital.com/research/private-saas-company-growth-rate-benchmarks/))
and against growth endurance, where ARR growth decays at a fairly predictable 30%
so next year's growth runs near 70% of this year's
([Bessemer](https://www.bvp.com/atlas/scaling-to-100-million)). A plan implying
growth acceleration needs a named mechanism.

**7. Validate, fix, re-validate.** Run all seven checks. If any fails, rebalance
or re-quota and then re-run all seven, because moving one account changes both the
donor and recipient scores. Only publish once every check passes.

```
- [ ] Every account assigned exactly once; no unassigned ICP accounts
- [ ] Every territory score between 85 and 115
- [ ] Sum of quotas = plan x (1 + over-assignment %), within rounding
- [ ] Every quota reduced for ramp where the rep is not fully ramped
- [ ] Every territory passes the pipeline feasibility test at 1 / win rate
- [ ] Named accounts and existing relationships preserved or explicitly traded
- [ ] Implied growth reconciles to prior-year actuals with a named mechanism
```

**8. Publish and deploy.** Push finalized territories into CRM so reps see the
same accounts and targets, and update when markets shift, reps move, or new
accounts appear
([Fullcast](https://www.fullcast.com/content/territory-based-capacity-modeling/)).
Deploy assignments and quotas in the same release, because a territory live
without its quota lets reps work accounts they will not be credited for.

## Output format

Use this exact section order, because plan reviews compare versions section by
section. Wording inside sections is yours to adapt.

```
## Plan basis
Plan number <value> | Over-assignment <x>% | Segments <list> | As-of <date>
Inputs: ramped quota <value>, avg deal size <value>, win rate <x>%,
cycle length <n> days, non-selling time <x>%, ramp curve <months: %> (local convention)

## Capacity
Required ramped reps <n> | Current heads <n> | Ramped-equivalent heads <n>
Productive capacity <value> | Coverage of plan <x>%
Hiring needed: <n> by <date> to be productive by <date>

## Territories
| Territory | Rep | Accounts | Weighted potential | Required hours | Score | Verdict |

## Quotas
| Rep | Ramp status | Quota | Deals needed | Pipeline required (1/win rate) | Feasible? |

## Risks and open decisions
- <risk> - <owner> - <decision needed by>
```

## Gotchas

- Territory scores must be recomputed after every account move, not once at the
  end. Moving one account changes the donor and recipient scores together, and
  sequential single-account fixes routinely push a balanced patch above 115.
- Set quotas after ramp is applied, never before. Assigning a full-year quota to
  a rep starting in month four builds a gap into the plan that no execution can
  close.
- A patch can score 85-115 on balance and still be unwinnable if its addressable
  pipeline is below quota times 1 divided by win rate, because balance measures
  fairness between reps and says nothing about absolute sufficiency.
- Territory optimization is associated with 10-20% productivity gains and 7%
  higher sales, and roughly 5% of revenue is lost to leakage from undisciplined
  growth, but these are vendor claims with no stated source or date, so cite them
  as directional only
  ([Fullcast](https://www.fullcast.com/content/territory-based-capacity-modeling/)).
- Quota-to-OTE multiples and ramp productivity curves are not published in the
  capacity-model source, so never present a figure such as a 5x quota-to-OTE ratio
  as an industry benchmark; derive it from internal history and label it local
  convention.
- Carving on account count produces the most balanced-looking and least workable
  map, since equal counts in a dense metro and a rural region differ by an order of
  magnitude in required hours.
- Check CAC payback implications before raising quotas to close a plan gap:
  targets are under 12 months for SMB, under 18 for mid-market, and under 24 for
  enterprise, and a quota raise funded by added headcount can break them
  ([Bessemer](https://www.bvp.com/atlas/scaling-to-100-million)).
- Highly qualified deals are 1.9x less likely to slip more than 90 days, so a
  patch loaded with weakly qualified pipeline needs more coverage than the win-rate
  formula alone implies; the sample behind that figure is not stated, so treat it as
  directional
  ([Fullcast](https://www.fullcast.com/content/territory-based-capacity-modeling/)).

What does the Designing Territories and Quotas skill do?

Carves balanced territories, sizes capacity from productivity and ramp, and sets quotas that roll up to plan with coverage math

Where does the Designing Territories and Quotas skill come from?

Report research library. It was written by The Revenue AI Report against a 12 criterion quality rubric and graded in an independent scoring pass.

Why was the Designing Territories and Quotas skill chosen for this library?

Explicitly refuses to present unpublished ramp curves and quota-to-OTE multiples as benchmarks and labels them local convention, which is the correct epistemic move.

When should the Designing Territories and Quotas skill not be used?

Do not use it for: What is our pipeline coverage this quarter and are we going to hit the number? Or: Compute NRR by ACV band and compare us to the SaaS Capital medians

How do I install the Designing Territories and Quotas SKILL.md file?

Download the file, create a folder named exactly designing-territories-and-quotas inside your agent's skills directory, and save the file inside it as SKILL.md. The agent loads it when a request matches the description.