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How do we right-size rep capacity and assign quota without guessing?

📖 194 words⏱ 1 min read4/29/2024

Direct Answer

Use three inputs: historical productivity (ramp curve), territory size (accounts/pipeline), and geography/segment complexity. Assign quota at 85–95% of forecasted capacity to drive execution without burnout. Recalibrate quarterly.

Operator Approach

Capacity planning is math + behavior. Start with productivity curves:

Rep Ramp Data (typical SaaS):

Once ramped, measure quota-carrying capacity by historical close rate × average deal size × territory pipeline.

Example:

Red flags (over-quotaed reps):

Capacity table:

Territory TypeAccountsAvg ACVClose RateQuarterly QuotaBuffer
Enterprise12–15$500K+20%$1.2–1.5M10%
Mid-market25–35$150–250K25%$750K–$1M8%
SMB60–100$30–50K35%$600–800K5%

Mermaid: Capacity Assignment Decision Tree

graph TD A["Assess Historical Ramp Data"] --> B{"Rep is Ramped?"} B -->|No| C["Apply 70% Capacity Factor"] B -->|Yes| D["Calculate Quota = Close Rate × ACV × Territory Size"] C --> E["Assign with 15% Buffer"] D --> F{"Result > Historical Attainment?"} F -->|Yes| G["Risk: Over-quotaed. Reduce 5–10%"] F -->|No| H["Assign Quota at 85–95% Capacity"] G --> I["Monitor Attainment Weekly"] H --> I I --> J{"2+ Quarters < 60% Attainment?"} J -->|Yes| K["Revise Territory or Support"] J -->|No| L["Recalibrate Quarterly"]

Sources: Pavilion Compensation Study, Bridge Group Territory Benchmarks, SaaStr Quota Setting Guide

TAGS: capacity-planning,quota-assignment,territory-design,ramp-curve,attainment-analysis,productivity-metrics,forecast-accuracy

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PavilionPavilionBridge GroupBridge GroupSaaStrSaaStr
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