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Afrexai Sales Compensation

Sales Compensation Plan Designer

1kalin0 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install 1kalin/afrexai-sales-compensation

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

Sales Compensation Plan Designer

Design, audit, and optimize sales compensation structures that actually drive the behavior you want. Covers quota setting, OTE splits, accelerators, clawbacks, SPIFs, and multi-role plan architectures.

When to Use

  • Designing comp plans for new sales roles (AE, SDR, CSM, SE, Channel)
  • Auditing existing plans for misaligned incentives
  • Modeling plan costs and quota coverage ratios
  • Building accelerator/decelerator curves
  • Comparing comp structures across industry benchmarks

Compensation Plan Framework

Step 1: Role Classification

Classify the role before designing comp:

Role Type Typical OTE Base/Variable Split Quota Multiple
SDR/BDR $65K-$90K 70/30 3-5x variable
AE (SMB) $100K-$140K 50/50 4-6x OTE
AE (Mid-Market) $150K-$200K 50/50 4-5x OTE
AE (Enterprise) $200K-$300K+ 60/40 3-4x OTE
CSM/AM $90K-$130K 65/35 4-6x variable
Sales Engineer $130K-$180K 70/30 Team-based
VP Sales $250K-$400K+ 55/45 2-3x OTE
Channel/Partner $120K-$160K 60/40 3-5x variable
Step 2: Quota Setting Methodology

Use bottom-up capacity model:

  1. TAM Analysis — addressable market in territory
  2. Historical Performance — trailing 4-quarter attainment distribution
  3. Ramp Adjustment — new hires at 25/50/75/100% quota months 1-4
  4. Coverage Ratio — pipeline-to-quota (3x minimum for new business, 2x for expansion)
  5. Quota:OTE Ratio — should be 4-6x. Below 3x = overpaying. Above 8x = nobody hits it.

Red flags in quota setting:

  • Top-down only (board target ÷ headcount)
  • Same quota for all territories regardless of TAM
  • No ramp period for new hires
  • Changing quotas mid-quarter
  • More than 60% of reps missing quota (plan problem, not people problem)
Step 3: Variable Compensation Design

Base Structure:

Monthly Variable = (Attainment % × Quota × Commission Ra...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/1kalin/afrexai-sales-compensation
Version
—
Commit
8305564665f1799778a4cff15139241f878ab56f
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

README

Sales Compensation Plan Designer

Design, audit, and optimize sales comp plans that drive the right behavior. Covers AE, SDR, CSM, SE, Channel, and VP-level plan architectures with 2026 benchmarks.

What's Inside

  • Role-specific OTE and split benchmarks across 10 industries
  • Quota setting methodology — bottom-up capacity model, not top-down guesswork
  • Accelerator/decelerator curve templates with rate multipliers
  • Plan cost modeling — bear/base/bull scenarios before you launch
  • 10-point annual audit checklist with scoring interpretation
  • SPIF design framework for short-term behavioral nudges
  • Clawback provisions — standard terms that protect without alienating
  • AI-era adjustments — how AI agents change SDR comp, ramp times, and quotas

Who It's For

  • Revenue leaders designing or overhauling comp plans
  • Sales ops teams modeling plan costs and coverage ratios
  • Founders setting up their first sales comp structure
  • CFOs auditing sales efficiency and CAC payback

Quick Start

Tell your agent: "Review our current sales comp plan using the sales compensation skill"

Or: "Design a comp plan for a mid-market AE role with $180K OTE"

2026 Industry Benchmarks Included

SaaS, Fintech, Healthcare IT, Cybersecurity, AI/ML, Legal Tech, Construction Tech, Manufacturing, Professional Services, Real Estate Tech.


Get your full industry AI strategy pack → afrexai-cto.github.io/context-packs ($47/pack)

Calculate your AI revenue leak → afrexai-cto.github.io/ai-revenue-calculator

Configure your AI agent workforce → afrexai-cto.github.io/agent-setup

Built by AfrexAI 🖤💛

Contenu extrait

SKILL.md
# Sales Compensation Plan Designer

Design, audit, and optimize sales compensation structures that actually drive the behavior you want. Covers quota setting, OTE splits, accelerators, clawbacks, SPIFs, and multi-role plan architectures.

## When to Use
- Designing comp plans for new sales roles (AE, SDR, CSM, SE, Channel)
- Auditing existing plans for misaligned incentives
- Modeling plan costs and quota coverage ratios
- Building accelerator/decelerator curves
- Comparing comp structures across industry benchmarks

## Compensation Plan Framework

### Step 1: Role Classification
Classify the role before designing comp:

| Role Type | Typical OTE | Base/Variable Split | Quota Multiple |
|-----------|-------------|--------------------:|----------------|
| SDR/BDR | $65K-$90K | 70/30 | 3-5x variable |
| AE (SMB) | $100K-$140K | 50/50 | 4-6x OTE |
| AE (Mid-Market) | $150K-$200K | 50/50 | 4-5x OTE |
| AE (Enterprise) | $200K-$300K+ | 60/40 | 3-4x OTE |
| CSM/AM | $90K-$130K | 65/35 | 4-6x variable |
| Sales Engineer | $130K-$180K | 70/30 | Team-based |
| VP Sales | $250K-$400K+ | 55/45 | 2-3x OTE |
| Channel/Partner | $120K-$160K | 60/40 | 3-5x variable |

### Step 2: Quota Setting Methodology
Use bottom-up capacity model:

1. **TAM Analysis** — addressable market in territory
2. **Historical Performance** — trailing 4-quarter attainment distribution
3. **Ramp Adjustment** — new hires at 25/50/75/100% quota months 1-4
4. **Coverage Ratio** — pipeline-to-quota (3x minimum for new business, 2x for expansion)
5. **Quota:OTE Ratio** — should be 4-6x. Below 3x = overpaying. Above 8x = nobody hits it.

Red flags in quota setting:
- Top-down only (board target ÷ headcount)
- Same quota for all territories regardless of TAM
- No ramp period for new hires
- Changing quotas mid-quarter
- More than 60% of reps missing quota (plan problem, not people problem)

### Step 3: Variable Compensation Design

**Base Structure:**
```
Monthly Variable = (Attainment % × Quota × Commission Ra...
README
# Sales Compensation Plan Designer

Design, audit, and optimize sales comp plans that drive the right behavior. Covers AE, SDR, CSM, SE, Channel, and VP-level plan architectures with 2026 benchmarks.

## What's Inside

- **Role-specific OTE and split benchmarks** across 10 industries
- **Quota setting methodology** — bottom-up capacity model, not top-down guesswork
- **Accelerator/decelerator curve templates** with rate multipliers
- **Plan cost modeling** — bear/base/bull scenarios before you launch
- **10-point annual audit checklist** with scoring interpretation
- **SPIF design framework** for short-term behavioral nudges
- **Clawback provisions** — standard terms that protect without alienating
- **AI-era adjustments** — how AI agents change SDR comp, ramp times, and quotas

## Who It's For

- Revenue leaders designing or overhauling comp plans
- Sales ops teams modeling plan costs and coverage ratios
- Founders setting up their first sales comp structure
- CFOs auditing sales efficiency and CAC payback

## Quick Start

Tell your agent: *"Review our current sales comp plan using the sales compensation skill"*

Or: *"Design a comp plan for a mid-market AE role with $180K OTE"*

## 2026 Industry Benchmarks Included

SaaS, Fintech, Healthcare IT, Cybersecurity, AI/ML, Legal Tech, Construction Tech, Manufacturing, Professional Services, Real Estate Tech.

---

**Get your full industry AI strategy pack →** [afrexai-cto.github.io/context-packs](https://afrexai-cto.github.io/context-packs/) ($47/pack)

**Calculate your AI revenue leak →** [afrexai-cto.github.io/ai-revenue-calculator](https://afrexai-cto.github.io/ai-revenue-calculator/)

**Configure your AI agent workforce →** [afrexai-cto.github.io/agent-setup](https://afrexai-cto.github.io/agent-setup/)

Built by [AfrexAI](https://afrexai-cto.github.io/context-packs/) 🖤💛

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