Sales Ops Analyst Strategic sales operations expertise for revenue teams — from CRM architecture and pipeline analytics to territory design and commission automation. Philosophy Great sales ops isn't about more data. It's about actionable insights that accelerate revenue. The best sales operations teams: Enable, don't police — Make it easier for reps to do the right thing Measure what matters — Vanity metrics create vanity pipeline Automate the mundane — Free reps to sell, not update fields Build for scale — Today's workaround is tomorrow's technical debt How This Skill Works When invoked, apply the guidelines in rules/ organized by: crm- — CRM architecture, data models, hygiene practices pipeline- — Pipeline analytics, stage definitions, velocity metrics dashboard- — Sales reporting, metrics, visualizations process- — Automation, workflows, approval chains routing- — Lead routing, assignment rules, territory design commission- — Comp plans, calculation logic, tracking data- — Data quality, deduplication, enrichment forecast- — Forecasting methodologies, models, accuracy Core Frameworks The RevOps Data Hierarchy Level What It Measures Used By Update Frequency Activity Calls, emails, meetings Reps, managers Real-time Opportunity Deal progress, value Reps, managers Daily Pipeline Forecast, velocity Directors, execs Weekly Revenue Bookings, ARR, churn C-suite, board Monthly/Quarterly Pipeline Velocity Formula Pipeline Velocity = (# Opportunities × Win Rate × Avg Deal Size) / Sales Cycle Length Example: (100 opps × 25% × $50K) / 90 days = $13,889/day potential revenue The Sales Tech Stack ┌─────────────────────────────────────────────────────────────┐ │ ANALYTICS LAYER │ │ (BI Tools: Tableau, Looker, Power BI, Salesforce Reports) │ ├─────────────────────────────────────────────────────────────┤ │ CRM LAYER │ │ (Salesforce, HubSpot, Dynamics 365) │ ├──────────────────┬──────────────────┬───────────────────────┤ │ ENGAGEMENT │ INTELLIGENCE │ ENRICHMENT │ │ Outreach, Salesloft│ Gong, Chorus │ ZoomInfo, Clearbit │ ├──────────────────┴──────────────────┴───────────────────────┤ │ DATA LAYER │ │ (Integrations, ETL, Data Warehouse, CDP) │ └─────────────────────────────────────────────────────────────┘ Lead Scoring Matrix Signal Type Examples Weight Fit (firmographic) Industry, company size, tech stack 40% Engagement (behavioral) Website visits, content downloads, email opens 35% Intent (buying signals) Pricing page views, demo requests, competitor research 25% Territory Design Principles ┌─────────────────┐ │ BALANCED │ │ OPPORTUNITY │ └────────┬────────┘ │ ┌───────────────────┼───────────────────┐ │ │ │ ▼ ▼ ▼ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ Account │ │ Revenue │ │ Travel │ │ Volume │ │Potential│ │ Load │ └─────────┘ └─────────┘ └─────────┘ Key Metrics Overview Category Metric Target Range Red Flag Activity Meetings/week/rep 10-15 <5 Pipeline Coverage ratio 3-4x <2x Velocity Avg sales cycle Industry dependent Growing Quality Win rate 20-30% <15% or >50% Forecast Accuracy ±10%
25% variance Data Duplicate rate <5% 10% Anti-Patterns Field proliferation — Adding fields without removing unused ones Report graveyard — Dashboards no one looks at Process theater — Mandatory updates that don't drive action Excel dependency — Critical processes outside the CRM Garbage in, garbage out — No data quality governance Over-automation — Automating bad processes faster Single point of failure — Tribal knowledge in one person's head Metric gaming — Optimizing for the number, not the outcome