sales-operations

What this skill does

# 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.

## 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:
        score += 15
    if account['content_downloads'] > 0:
        score += 10

    # Intent signals (0-20 points)
    if account['intent_score'] > 80:
        score += 20
    elif account['intent_score'] > 50:
        score += 10

    return score  # Max 100; 70+ = Tier 1, 40-69 = Tier 2, <40 = Tier 3
```

## Territory Design

The agent balances territories across three dimensions:

- **Balance** -- Similar opportunity potential, comparable workload, fair distribution across reps.
- **Coverage** -- Geographic proximity, industry alignment, existing account relationships.
- **Growth** -- Room for expansion, career progression paths, untapped market potential.

### Example: Territory Allocation Table

| Territory | Rep | Accounts | ARR Potential | Quota | Coverage |
|-----------|-----|----------|---------------|-------|----------|
| West Enterprise | Rep A | 45 | $3.0M | $2.7M | 111% |
| East Mid-Market | Rep B | 62 | $2.8M | $2.4M | 117% |
| Central (Ramping) | Rep C | 38 | $2.5M | $1.2M | 208% |

## Quota Setting

### Top-Down Model

```
Company Revenue Target: $50M
  Growth Rate: 30%
  Team Capacity: 20 reps
  Average Quota: $2.5M
  Adjustments: +/-20% based on territory potential
```

### Bottom-Up Model

```
Account Potential Analysis:
  Existing accounts: $30M
  Pipeline value: $15M
  New logo potential: $10M
  Total: $55M
  Risk adjustment: -10%
  Final: $49.5M
```

The agent reconciles both models and flags divergence exceeding 10%.

## Compensation Architecture

```
TOTAL ON-TARGET EARNINGS (OTE)
  Base Salary: 50-60%
  Variable: 40-50%
    Commission: 80% of variable
      New Business: 60%
      Expansion: 40%
    Bonus: 20% of variable
      Quarterly accelerators
      SPIFs

COMMISSION RATE TIERS
  0-50% quota:   0.5x rate
  50-100% quota:  1.0x rate
  100-150% quota: 1.5x rate
  150%+ quota:    2.0x rate
```

## Forecasting

### Forecast Categories

| Category | Definition | Weighting |
|----------|------------|-----------|
| Closed | Signed contract | 100% |
| Commit | Verbal commit, high confidence | 90% |
| Best Case | Strong opportunity, likely to close | 50% |
| Pipeline | Active opportunity | 20% |
| Upside | Early stage | 5% |

### Example: Weighted Forecast Output

```
Q4 Forecast - Week 8
  Quota: $10M

  Category       Deals    Amount     Weighted
  Closed         12       $2.4M      $2.4M
  Commit         8        $1.8M      $1.6M
  Best Case      15       $3.2M      $1.6M
  Pipeline       22       $4.5M      $0.9M

  Forecast (Closed + Commit): $4.0M
  Upside (with Best Case): $5.6M
  Gap to Quota: $6.0M
  Required Win Rate on Pipeline: 35%
```

## CRM Data Quality Checklist

The agent validates these fields during every pipeline review:

- [ ] Required fields populated on all open opportunities
- [ ] Stage dates updated within the last 7 days
- [ ] Close dates set to realistic future dates (no past-due)
- [ ] Deal amounts reflect current pricing discussions
- [ ] Contact roles assigned with at least one economic buyer
- [ ] Next steps documented with specific actions and dates

## Process Optimization

### Sales Process Audit Framework

```
STAGE ANALYSIS
  Average time in stage -> identify stalls
  Conversion rate per stage -> find drop-off points
  Drop-off reasons -> categorize and address

ACTIVITY ANALYSIS
  Activities per stage -> benchmark against top performers
  Activity-to-outcome ratio -> measure efficiency
  Time allocation -> optimize selling vs. admin time

TOOL UTILIZATION
  CRM adoption rate -> target 95%+ daily login
  Feature usage -> identify underused capabilities
  Data quality score -> track completeness over time
  Automation opportunities -> reduce manual entry
```

## Scripts

```bash
# Pipeline analyzer
python scripts/pipeline_analyzer.py --data opportunities.csv

# Territory optimizer
python scripts/territory_optimizer.py --accounts accounts.csv --reps 10

# Quota calculator
python scripts/quota_calculator.py --target 50000000 --reps team.csv

# Forecast reporter
python scripts/forecast_report.py --quarter Q4 --output report.html
```

## Troubleshooting

| Problem | Root Cause | Resolution |
|---------|-----------|------------|
| Forecast accuracy below 70% | Inconsistent stage definitions; reps over-committing; lack of weighted methodology | Enforce strict stage entry/exit criteria. Apply probability weights by category (Commit 90%, Best Case 50%, Pipeline 20%). Review commit deals individually in weekly forecast calls. Compare rolling 4-quarter actuals to calibrate weights. |
| Territory imbalance causing rep attrition | Uneven account distribution; potential-to-quota mismatch exceeding 20% | Re-score accounts quarterly using the scoring model. Target less than 15% variance in potential-to-quota ratio across territories. Review territory balance monthly in high-growth periods. |
| CRM data quality below 80% completeness | Insufficient enforcement; no automated validation; rep adoption gaps | Implement required field validation at stage transitions. Run weekly data quality reports. Tie CRM hygiene to variable compensation (5-10% of bonus). Target 95%+ daily login rate. |
| Quota attainment below 60% team-wide | Quotas set too aggressively; insufficient pipeline; ramp time underestimated | Reconcile top-down and