sales-ops-analyst

name	sales-ops-analyst
description	Expert sales operations and analytics guidance for revenue teams. Use when designing CRM workflows, building sales dashboards, optimizing pipeline analytics, creating lead routing rules, designing territories, calculating commissions, managing data quality, building forecasting models, or integrating sales tech stack. Covers Salesforce, HubSpot, Outreach, Gong, and RevOps best practices.

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:

1. Enable, don't police — Make it easier for reps to do the right thing
2. Measure what matters — Vanity metrics create vanity pipeline
3. Automate the mundane — Free reps to sell, not update fields
4. 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