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

• Assess current state -- Audit CRM data quality, pipeline coverage, and rep performance baselines. Validate that required fields are populated and stage dates are current.

• Analyze pipeline health -- Calculate coverage ratios, stage conversion rates, velocity metrics, and deal aging. Flag bottlenecks where conversion drops below historical norms.

• Design or refine territories -- Balance territories by opportunity potential, workload, and geographic/industry alignment. Score accounts to inform assignment.

• Model quotas -- Run top-down (revenue target / capacity) and bottom-up (account potential analysis) models. Reconcile and risk-adjust.

• Architect compensation -- Structure OTE splits, commission tiers, accelerators, and SPIFs aligned to company stage and selling motion.

• Build forecast -- Categorize deals by confidence tier, apply probability weights, and surface the gap-to-quota with required win rates.

• 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

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

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

Reference Materials

• references/analytics.md -- Sales analytics guide

• references/territory.md -- Territory planning

• references/compensation.md -- Comp design principles

• references/forecasting.md -- Forecasting methodology