# Revenue Operations > Implement revenue operations (RevOps) workflows including pipeline management, forecasting, sales-marketing alignment, lead scoring, and quota tracking. Covers automation patterns for modern go-to-market teams. - Skill: `sandeeprdy1729/revenue-operations` (Agent Skill) - Install (CLI): `npx skillmds add sandeeprdy1729/revenue-operations` - Raw SKILL.md: https://api.skillmd.com/api/skills/sandeeprdy1729/revenue-operations/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - License: Apache 2.0 - Author: Sandeeprdy1729 (https://skillmd.com/u/sandeeprdy1729) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/sandeeprdy1729/revenue-operations --- # Revenue Operations ## Overview Revenue Operations (RevOps) unifies sales, marketing, and customer success operations under a single data and process framework. As AI agents gain access to CRM and analytics tools, RevOps workflows become automatable — from lead scoring to pipeline forecasting to churn prediction. This skill covers the operational patterns, data models, and automation strategies for modern RevOps. ## When to Use This Skill - Building AI-powered pipeline forecasting and deal scoring - Automating lead routing and qualification workflows - Implementing sales-marketing alignment with SLA tracking - Creating revenue dashboards from CRM, billing, and usage data - Designing quota management and commission calculation systems ## Core Concepts ### The RevOps Data Flow ``` Marketing Sales Customer Success ───────── ───── ──────────────── Lead Gen ──► MQL ──► SQL ──► Opportunity ──► Closed Won ──► Onboarding ──► Renewal │ │ │ │ │ │ │ ▼ ▼ ▼ ▼ ▼ ▼ ▼ CAC Conversion Win Rate ACV Time-to- NPS/CSAT Net Revenue Metrics Rates Pipeline Value Retention ``` ### Key Metrics | Metric | Formula | Healthy Range | |--------|---------|---------------| | MQL-to-SQL Rate | SQLs / MQLs | 15-30% | | SQL-to-Opportunity | Opps / SQLs | 40-60% | | Win Rate | Closed Won / Total Opps | 20-35% | | Sales Cycle Length | Avg days from SQL to Close | Industry-dependent | | Pipeline Coverage | Pipeline Value / Quota | 3-4x | | CAC Payback Period | CAC / Monthly Gross Margin | < 18 months | | Net Revenue Retention | (Revenue + Expansion - Churn) / Starting Revenue | > 110% | | ARR per Rep | Total ARR / Quota-carrying Reps | $500K-$1.2M | ### Lead Scoring Model ```python class LeadScorer: """Multi-factor lead scoring combining firmographic and behavioral signals.""" FIRMOGRAPHIC_WEIGHTS = { "employee_count": {"1-50": 10, "51-200": 20, "201-1000": 30, "1000+": 25}, "industry": {"technology": 30, "finance": 25, "healthcare": 20, "other": 10}, "title_level": {"c-level": 40, "vp": 35, "director": 25, "manager": 15, "individual": 5}, } BEHAVIORAL_WEIGHTS = { "pricing_page_visit": 20, "demo_request": 40, "whitepaper_download": 10, "webinar_attendance": 15, "email_open": 2, "email_click": 5, "return_visit": 8, } def score(self, lead): firm_score = sum( self.FIRMOGRAPHIC_WEIGHTS.get(attr, {}).get(getattr(lead, attr, "other"), 0) for attr in self.FIRMOGRAPHIC_WEIGHTS ) behavior_score = sum( self.BEHAVIORAL_WEIGHTS.get(action, 0) * count for action, count in lead.activities.items() ) total = min(firm_score + behavior_score, 100) if total >= 80: return {"score": total, "grade": "A", "action": "route_to_sales"} if total >= 60: return {"score": total, "grade": "B", "action": "nurture_high"} if total >= 40: return {"score": total, "grade": "C", "action": "nurture_standard"} return {"score": total, "grade": "D", "action": "continue_marketing"} ``` ## Implementation Guide ### Pipeline Forecasting ```python def forecast_pipeline(deals, method="weighted"): """Generate revenue forecast from pipeline deals.""" stage_weights = { "discovery": 0.10, "qualification": 0.25, "proposal": 0.50, "negotiation": 0.75, "verbal_commit": 0.90, "closed_won": 1.00, "closed_lost": 0.00, } if method == "weighted": return sum( deal.amount * stage_weights.get(deal.stage, 0) for deal in deals ) elif method == "category": return { "best_case": sum(d.amount for d in deals if d.stage in ["verbal_commit", "negotiation", "proposal"]), "commit": sum(d.amount for d in deals if d.stage in ["verbal_commit"]), "closed": sum(d.amount for d in deals if d.stage == "closed_won"), } ``` ### Lead Routing Automation ```python class LeadRouter: """Route leads to sales reps based on territory, capacity, and specialization.""" def route(self, lead): # 1. Territory match territory_reps = self.get_reps_for_territory(lead.region) # 2. Specialization match specialized = [r for r in territory_reps if lead.industry in r.specializations] # 3. Capacity check (round-robin among available) candidates = specialized or territory_reps available = [r for r in candidates if r.active_deals < r.capacity_limit] if not available: return self.escalate_to_manager(lead) # Round-robin by least recent assignment selected = min(available, key=lambda r: r.last_assigned_at) self.assign(lead, selected) return selected ``` ### Marketing-Sales SLA Tracking ```python class SLATracker: SLAS = { "mql_followup": timedelta(hours=4), # Sales must contact MQL within 4 hours "sql_qualification": timedelta(days=2), # Must qualify/disqualify within 2 days "proposal_delivery": timedelta(days=5), # Proposal within 5 days of request } def check_violations(self): violations = [] for lead in self.get_open_leads(): for sla_name, sla_limit in self.SLAS.items(): if lead.get_stage_duration(sla_name) > sla_limit: violations.append({ "lead": lead.id, "sla": sla_name, "overdue_by": lead.get_stage_duration(sla_name) - sla_limit, "owner": lead.owner, }) return violations ``` ## Best Practices 1. **Single source of truth** — CRM is the system of record; sync everything back to it 2. **Score leads on both fit and intent** — firmographic + behavioral signals 3. **Pipeline coverage of 3-4x** — if your quota is $1M, you need $3-4M in pipeline 4. **Inspect deals weekly** — pipeline reviews catch stalled deals early 5. **Automate handoffs** — lead routing, stage progression notifications, and SLA alerts 6. **Track leading indicators** — activity metrics predict revenue better than lagging metrics ## Resources - [RevOps Framework by Clari](https://www.clari.com/resources/) - [HubSpot RevOps Playbook](https://www.hubspot.com/revenue-operations) - [SaaS Metrics 2.0 by David Skok](https://www.forentrepreneurs.com/saas-metrics-2/) ## Changelog | Version | Date | Changes | |---------|------|---------| | 1.0.0 | 2026-03-31 | Initial documentation |