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 practicespipeline-* — Pipeline analytics, stage definitions, velocity metricsdashboard-* — Sales reporting, metrics, visualizationsprocess-* — Automation, workflows, approval chainsrouting-* — Lead routing, assignment rules, territory designcommission-* — Comp plans, calculation logic, trackingdata-* — Data quality, deduplication, enrichmentforecast-* — 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
1---2name: sales-ops-analyst3description: 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.4---5
6# Sales Ops Analyst
7
8Strategic sales operations expertise for revenue teams — from CRM architecture and pipeline analytics to territory design and commission automation.
9
10## Philosophy
11
12Great sales ops isn't about more data. It's about **actionable insights** that accelerate revenue.
13
14The best sales operations teams:
151. **Enable, don't police** — Make it easier for reps to do the right thing
162. **Measure what matters** — Vanity metrics create vanity pipeline
173. **Automate the mundane** — Free reps to sell, not update fields
184. **Build for scale** — Today's workaround is tomorrow's technical debt
19
20## How This Skill Works
21
22When invoked, apply the guidelines in `rules/` organized by:
23
24- `crm-*` — CRM architecture, data models, hygiene practices
25- `pipeline-*` — Pipeline analytics, stage definitions, velocity metrics
26- `dashboard-*` — Sales reporting, metrics, visualizations
27- `process-*` — Automation, workflows, approval chains
28- `routing-*` — Lead routing, assignment rules, territory design
29- `commission-*` — Comp plans, calculation logic, tracking
30- `data-*` — Data quality, deduplication, enrichment
31- `forecast-*` — Forecasting methodologies, models, accuracy
32
33## Core Frameworks
34
35### The RevOps Data Hierarchy
36
37| Level | What It Measures | Used By | Update Frequency |
38|-------|------------------|---------|------------------|
39| **Activity** | Calls, emails, meetings | Reps, managers | Real-time |
40| **Opportunity** | Deal progress, value | Reps, managers | Daily |
41| **Pipeline** | Forecast, velocity | Directors, execs | Weekly |
42| **Revenue** | Bookings, ARR, churn | C-suite, board | Monthly/Quarterly |
43
44### Pipeline Velocity Formula
45
46```
47Pipeline Velocity = (# Opportunities × Win Rate × Avg Deal Size) / Sales Cycle Length
48
49Example:
50(100 opps × 25% × $50K) / 90 days = $13,889/day potential revenue
51```
52
53### The Sales Tech Stack
54
55```
56┌─────────────────────────────────────────────────────────────┐
57│ ANALYTICS LAYER │
58│ (BI Tools: Tableau, Looker, Power BI, Salesforce Reports) │
59├─────────────────────────────────────────────────────────────┤
60│ CRM LAYER │
61│ (Salesforce, HubSpot, Dynamics 365) │
62├──────────────────┬──────────────────┬───────────────────────┤
63│ ENGAGEMENT │ INTELLIGENCE │ ENRICHMENT │
64│ Outreach, Salesloft│ Gong, Chorus │ ZoomInfo, Clearbit │
65├──────────────────┴──────────────────┴───────────────────────┤
66│ DATA LAYER │
67│ (Integrations, ETL, Data Warehouse, CDP) │
68└─────────────────────────────────────────────────────────────┘
69```
70
71### Lead Scoring Matrix
72
73| Signal Type | Examples | Weight |
74|-------------|----------|--------|
75| **Fit** (firmographic) | Industry, company size, tech stack | 40% |
76| **Engagement** (behavioral) | Website visits, content downloads, email opens | 35% |
77| **Intent** (buying signals) | Pricing page views, demo requests, competitor research | 25% |
78
79### Territory Design Principles
80
81```
82 ┌─────────────────┐
83 │ BALANCED │
84 │ OPPORTUNITY │
85 └────────┬────────┘
86 │
87 ┌───────────────────┼───────────────────┐
88 │ │ │
89 ▼ ▼ ▼
90 ┌─────────┐ ┌─────────┐ ┌─────────┐
91 │ Account │ │ Revenue │ │ Travel │
92 │ Volume │ │Potential│ │ Load │
93 └─────────┘ └─────────┘ └─────────┘
94```
95
96## Key Metrics Overview
97
98| Category | Metric | Target Range | Red Flag |
99|----------|--------|--------------|----------|
100| **Activity** | Meetings/week/rep | 10-15 | <5 |
101| **Pipeline** | Coverage ratio | 3-4x | <2x |
102| **Velocity** | Avg sales cycle | Industry dependent | Growing |
103| **Quality** | Win rate | 20-30% | <15% or >50% |
104| **Forecast** | Accuracy | ±10% | >25% variance |
105| **Data** | Duplicate rate | <5% | >10% |
106
107## Anti-Patterns
108
109- **Field proliferation** — Adding fields without removing unused ones
110- **Report graveyard** — Dashboards no one looks at
111- **Process theater** — Mandatory updates that don't drive action
112- **Excel dependency** — Critical processes outside the CRM
113- **Garbage in, garbage out** — No data quality governance
114- **Over-automation** — Automating bad processes faster
115- **Single point of failure** — Tribal knowledge in one person's head
116- **Metric gaming** — Optimizing for the number, not the outcome
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