home / skills / onewave-ai / claude-skills / quota-setting-calculator

quota-setting-calculator skill

/quota-setting-calculator

This skill helps design fair quota models using top-down and bottom-up approaches with territory and ramp adjustments.

npx playbooks add skill onewave-ai/claude-skills --skill quota-setting-calculator

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
1.4 KB
---
name: quota-setting-calculator
description: Top-down vs bottom-up quota models. Historical attainment, market growth assumptions, ramp periods, territory complexity.
---

# Quota Setting Calculator
Top-down vs bottom-up quota models. Historical attainment, market growth assumptions, ramp periods, territory complexity.

## Instructions

You are an expert sales operations leader. Design fair, achievable quota models with clear methodology and territory adjustments.

### Output Format

```markdown
# Quota Setting Calculator Output

**Generated**: {timestamp}

---

## Results

[Your formatted output here]

---

## Recommendations

[Actionable next steps]

```

### Best Practices

1. **Be Specific**: Focus on concrete, actionable outputs
2. **Use Templates**: Provide copy-paste ready formats
3. **Include Examples**: Show real-world usage
4. **Add Context**: Explain why recommendations matter
5. **Stay Current**: Use latest best practices for sales-leadership

### Common Use Cases

**Trigger Phrases**:
- "Help me with [use case]"
- "Generate [output type]"
- "Create [deliverable]"

**Example Request**:
> "[Sample user request here]"

**Response Approach**:
1. Understand user's context and goals
2. Generate comprehensive output
3. Provide actionable recommendations
4. Include examples and templates
5. Suggest next steps

Remember: Focus on delivering value quickly and clearly!

Overview

This skill helps sales leaders design fair, achievable quota plans using top-down and bottom-up models. It balances historical attainment, market growth assumptions, ramp periods, and territory complexity to produce defensible quotas and clear recommendations. I provide copy-ready outputs, simple templates, and actionable next steps for quota rollout.

How this skill works

I ingest historical attainment by rep/territory, total addressable market (TAM) estimates, and sales cycle/ramp profiles. The tool computes top-down targets from company revenue goals and allocates them across segments, then runs a bottom-up build from individual capacity, territory potential, and complexity adjustments. It reconciles the two approaches, highlights gaps, and outputs recommended quotas, expected attainment, and sensitivity scenarios.

When to use it

  • Setting annual quotas during planning season
  • Recalibrating targets after territory realignment
  • Designing ramp profiles for new hires or new products
  • Assessing quota fairness after significant market changes
  • Validating quotas against historical attainment patterns

Best practices

  • Start with a clean dataset of 12–36 months of attainment by rep and territory
  • Run both top-down and bottom-up models and require alignment before finalizing
  • Apply transparent territory complexity multipliers (size, travel, vertical specialization)
  • Model ramp periods explicitly and separate quota from quota-carrying expectations
  • Document assumptions for TAM growth, win rates, and average deal size for auditability

Example use cases

  • Convert a $100M company target into balanced quotas by region using a top-down allocation, then validate against bottom-up capacity
  • Adjust quotas after expanding into a new vertical by adding market growth assumptions and territory complexity factors
  • Create ramped quota schedules for a 12-month hiring plan and quantify expected attainment by quarter
  • Recommend quota reductions or coverage hires when bottom-up potential falls short of corporate goals
  • Produce a one-page quota justification deck for leadership that shows reconciliation and sensitivity bands

FAQ

How do you reconcile top-down and bottom-up results?

I compare allocations, identify drivers of divergence (e.g., inflated TAM or unrealistic productivity), then adjust assumptions or apply smoothing to ensure fairness and feasibility.

What inputs are essential for reliable outputs?

Historical attainment, territory definitions, average deal size, win rate, ramp profile, and a defensible TAM or market growth assumption are required.