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.
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
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
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.
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
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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. It is listed under DevOps & Infra on SkillMD.
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borghei (@borghei) published this skill. Their other Agent Skills are listed on their SkillMD profile.