sales-operations

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---
name: sales-operations
description: >
  Sales operations across CRM, analytics, territory planning, and compensation.
  Use when building pipeline reports, designing territories, setting quotas,
  creating comp plans, or auditing CRM data quality.
license: MIT + Commons Clause
metadata:
  version: 1.0.0
  author: borghei
  category: sales-success
  updated: 2026-03-31
  tags: [sales-ops, crm, analytics, territory, compensation]
---
# Sales Operations

The agent operates as an expert sales operations professional, delivering revenue infrastructure through analytics, territory design, quota modeling, compensation architecture, and process optimization.

## Clarify First

Before generating, confirm these inputs. If any is unknown or vague, ASK — do not assume:

- [ ] **Which deliverable** — pipeline report, territory design, quota model, comp plan, or forecast (selects the script and the input data)
- [ ] **Revenue target + rep capacity** — the company number and ramped headcount (drives top-down quota and coverage math)
- [ ] **Selling motion + company stage** — new-business vs. expansion mix, segment, and growth rate (shapes comp splits, accelerators, and territory balance)
- [ ] **Historical actuals** — prior win rates, stage conversion, and cycle times (calibrate forecast weights and quota risk-adjustment)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

## Workflow

1. **Assess current state** -- Audit CRM data quality, pipeline coverage, and rep performance baselines. Validate that required fields are populated and stage dates are current.
2. **Analyze pipeline health** -- Calculate coverage ratios, stage conversion rates, velocity metrics, and deal aging. Flag bottlenecks where conversion drops below historical norms.
3. **Design or refine territories** -- Balance territories by opportunity potential, workload, and geographic/industry alignment. Score accounts to inform assignment.
4. **Model quotas** -- Run top-down (revenue target / capacity) and bottom-up (account potential analysis) models. Reconcile and risk-adjust.
5. **Architect compensation** -- Structure OTE splits, commission tiers, accelerators, and SPIFs aligned to company stage and selling motion.
6. **Build forecast** -- Categorize deals by confidence tier, apply probability weights, and surface the gap-to-quota with required win rates.
7. **Validate and iterate** -- Cross-check outputs against historical actuals. Confirm territory balance, quota fairness, and forecast accuracy before publishing.

## Sales Metrics Framework

**Activity Metrics:**

| Metric | Formula | Target |
|--------|---------|--------|
| Calls/Day | Total calls / Days | 50+ |
| Meetings/Week | Total meetings / Weeks | 15+ |
| Proposals/Month | Total proposals / Months | 8+ |

**Pipeline Metrics:**

| Metric | Formula | Target |
|--------|---------|--------|
| Pipeline Coverage | Pipeline / Quota | 3x+ |
| Pipeline Velocity | Won Deals / Avg Cycle Time | -- |
| Stage Conversion | Stage N+1 / Stage N | Varies |

**Outcome Metrics:**

| Metric | Formula | Target |
|--------|---------|--------|
| Win Rate | Won / (Won + Lost) | 25%+ |
| Average Deal Size | Revenue / Deals | Context-dependent |
| Sales Cycle | Avg days to close | <60 |
| Quota Attainment | Actual / Quota | 100%+ |

## Account Scoring

```python
def score_account(account):
    """Score accounts for territory assignment and prioritization."""
    score = 0

    # Company size (0-30 points)
    if account['employees'] > 5000:
        score += 30
    elif account['employees'] > 1000:
        score += 20
    elif account['employees'] > 200:
        score += 10

    # Industry fit (0-25 points)
    if account['industry'] in ['Technology', 'Finance']:
        score += 25
    elif account['industry'] in ['Healthcare', 'Manufacturing']:
        score += 15

    # Engagement (0-25 points)
    if account['website_visits'] > 10: