sales-ops-analyst

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