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

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

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 bottom-up models. Flag divergence exceeding 10%. Risk-adjust for ramp (ramping reps at 50-75% quota). Ensure 3-4x pipeline coverage at quarter start.
Comp plan driving wrong behaviors	Misaligned incentives; rewarding volume over quality; no accelerators	Audit comp plans against strategic objectives. Ensure accelerators kick in at 100% attainment. Weight new business vs. expansion per GTM strategy. Add SPIFs for strategic priorities.
Pipeline coverage drops mid-quarter	Insufficient lead flow; deals pushed or lost faster than replaced	Alert AEs when individual coverage drops below 2.5x. Coordinate with Marketing on lead generation campaigns. Implement minimum weekly prospecting activity requirements.
Stage conversion rates declining	Process bottleneck; missing enablement; competitive pressure	Identify the specific stage with the highest drop-off. Compare top performer conversion rates to team average. Deploy targeted training on the bottleneck stage. Review competitive win/loss data for that stage.

Success Criteria

Metric	Target	Measurement Method
Forecast accuracy	Within 10% of actual quarterly	Abs(Weighted Forecast - Actual) / Actual
Pipeline coverage ratio	3-4x quota at quarter start	Total pipeline value / Team quota
CRM data completeness	95%+ required fields populated	Weekly automated data quality audit
Territory balance	Less than 15% variance in potential-to-quota	Standard deviation of potential-to-quota ratio across territories
Quota attainment distribution	60%+ of reps at or above quota	Reps at 100%+ / Total ramped reps
Stage conversion rates	Improving or stable QoQ	Stage N+1 entries / Stage N entries per period
Sales cycle length	Trending downward or stable	Average days from opportunity creation to close
Ramp time to productivity	Under 6 months for new hires	Months until new rep reaches 75% of quota run rate
Process adoption	90%+ compliance with defined process	Audit score from monthly process compliance review

Scope & Limitations

In Scope:

• CRM administration, data quality management, and process enforcement
• Pipeline analytics: coverage ratios, stage conversion, velocity metrics, deal aging
• Territory design, account scoring, and balanced assignment optimization
• Quota modeling: top-down, bottom-up, and reconciliation approaches
• Compensation architecture: OTE splits, commission tiers, accelerators, SPIFs
• Forecast methodology: weighted pipeline, category-based, rolling forecasts
• Sales process audit: stage analysis, activity benchmarking, tool utilization
• Reporting infrastructure and dashboard design

Out of Scope:

• Individual deal strategy, qualification, and closing (see account-executive)
• Technical demos, RFP responses, and POC management (see sales-engineer)
• Post-sale customer management and retention (see customer-success-manager)
• Enterprise solution architecture and integration design (see solutions-architect)
• Marketing attribution modeling and campaign ROI (see marketing/campaign-analytics)
• Financial modeling beyond sales compensation (see finance)

Limitations:

• Territory optimization uses heuristic scoring, not mathematical optimization solvers; results are directional, not globally optimal
• Quota models require accurate historical data; garbage in, garbage out
• Forecast accuracy benchmarks assume consistent CRM hygiene; accuracy degrades with poor data quality
• Scripts process CSV/JSON exports only; no direct CRM API connectivity
• Compensation modeling does not account for tax implications or local labor law constraints

Integration Points

Integration	Direction	Purpose	Handoff Artifact
Account Executive	Ops -> AE	Territory assignments, quota targets, pipeline reports, forecast templates	Territory map, quota letter, pipeline dashboard, forecast submission form
Sales Engineer	Ops -> SE	Activity tracking, demo conversion metrics, technical win/loss data	SE activity reports, technical evaluation pipeline
Customer Success Manager	Ops -> CSM	Renewal pipeline tracking, expansion revenue attribution, churn reporting	Renewal forecast rollup, NRR reports, churn analysis
Marketing	Bidirectional	Lead attribution, MQL-to-SQL conversion, campaign ROI, pipeline sourcing	Attribution reports, lead routing rules, campaign pipeline reports
Finance	Ops -> Finance	Revenue forecasting, commission calculations, quota-to-capacity planning	Forecast submissions, commission statements, headcount models
Revenue Operations	Bidirectional	Cross-functional GTM metrics, funnel analytics, ARR reporting	Unified revenue dashboard, GTM efficiency metrics
HR	Ops -> HR	Headcount planning, ramp modeling, performance data for reviews	Ramp timelines, quota attainment reports, territory capacity models

Workflow Handoff Protocol:

1. Sales Ops publishes territory assignments and quota letters at least 2 weeks before quarter start
2. Sales Ops delivers weekly pipeline report to sales leadership every Monday by 10 AM
3. Sales Ops collects forecast submissions from AEs every Friday and publishes rolled-up forecast by Mon