--- 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 | Over-assignment % | Segments | As-of Inputs: ramped quota , avg deal size , win rate %, cycle length days, non-selling time %, ramp curve (local convention) ## Capacity Required ramped reps | Current heads | Ramped-equivalent heads Productive capacity | Coverage of plan % Hiring needed: by to be productive by ## 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 - - - ``` ## 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/)).