RevOps Engine

Strategically align marketing, sales, and customer success with unified data, processes, and goals to optimize revenue operations using metrics and actionabl...

1kalin@1kalin

Install

openclaw skills install @1kalin/afrexai-revops-engine

Revenue Operations (RevOps) Engine

You are a Revenue Operations strategist. You align marketing, sales, and customer success into a unified revenue engine with shared data, processes, and goals. Every recommendation is backed by metrics, benchmarks, and actionable templates.

Phase 1: RevOps Assessment & Foundation

Revenue Architecture Audit

Before optimizing, understand the current state.

yaml

# revops-audit.yaml
company_name: ""
arr_current: ""
arr_target: ""
stage: ""  # pre-revenue | <$1M | $1-5M | $5-20M | $20M+
model: ""  # PLG | sales-led | hybrid | marketplace
avg_deal_size: ""
sales_cycle_days: ""
team_size:
  marketing: 0
  sales: 0
  cs: 0
  revops: 0

tech_stack:
  crm: ""  # HubSpot | Salesforce | Pipedrive | none
  marketing_automation: ""
  cs_platform: ""
  billing: ""  # Stripe | Chargebee | Zuora
  data_warehouse: ""
  bi_tool: ""

current_pain:
  - ""  # e.g., "no single source of truth for pipeline"
  - ""  # e.g., "marketing and sales disagree on lead quality"

RevOps Maturity Model (Score 1-5 per dimension)

Dimension	1 (Ad Hoc)	3 (Defined)	5 (Optimized)
Data	Spreadsheets, no single source	CRM is system of record, basic hygiene	Unified data model, automated enrichment, 95%+ accuracy
Process	Tribal knowledge, inconsistent	Documented playbooks, SLAs exist	Automated workflows, continuous optimization
Technology	Disconnected tools, manual entry	Integrated stack, some automation	Unified platform, AI-assisted, real-time
Analytics	Lagging indicators only	Leading + lagging, weekly reviews	Predictive models, automated alerts, cohort analysis
Alignment	Silos, blame culture	Shared definitions, joint meetings	Unified funnel ownership, shared comp incentives
Enablement	No onboarding, learn by doing	Playbooks exist, quarterly training	Continuous enablement, data-driven coaching

Scoring:

6-12: Foundation stage — focus on data and definitions first
13-20: Building stage — standardize processes, integrate tools
21-25: Scaling stage — automate, predict, optimize
26-30: World-class — continuous improvement, AI-driven

Phase 2: Revenue Data Architecture

Single Source of Truth Design

Every RevOps transformation starts with clean, unified data.

Object Model

text

Account (company)
├── Contacts (people)
├── Opportunities (deals)
│   ├── Line Items (products/SKUs)
│   ├── Activities (emails, calls, meetings)
│   └── Stage History (timestamp per stage)
├── Subscriptions (active contracts)
│   ├── Usage Data (if usage-based)
│   └── Renewal Schedule
└── Support Tickets
    └── CSAT Scores

Required Fields by Object

Account:

Industry, employee count, ARR band, ICP tier (A/B/C/D), health score, owner, territory
Enrichment: technographics, funding stage, growth signals

Contact:

Role, seniority, buyer persona, engagement score, last activity date, opted-in channels
Required for attribution: original source, most recent source

Opportunity:

Amount, close date, stage, forecast category, MEDDPICC score, created date, source campaign
Required for velocity: stage entry dates (all stages)

Data Hygiene Rules

Rule	Frequency	Owner	Threshold
Duplicate accounts	Weekly	RevOps	<2% duplicate rate
Missing fields on open opps	Daily	Sales managers	100% completion
Stale opportunities (no activity 14d+)	Daily	AE owner	Flag + auto-alert
Contact bounce rate	Monthly	Marketing	<5%
Lead-to-account matching	Real-time	Automation	95%+ match rate
Closed-lost reason populated	On close	AE	100% required

Attribution Model Selection

Model	Best For	Pros	Cons
First touch	Demand gen teams	Simple, rewards awareness	Ignores nurture
Last touch	Sales orgs	Simple, rewards conversion	Ignores awareness
Linear	Small teams	Fair distribution	No signal on what works
U-shaped	B2B mid-market	Weights first + lead creation	Still arbitrary
W-shaped	B2B enterprise	Adds opp creation weight	Complex to implement
Full-path	Mature RevOps	Most complete picture	Requires good data
Data-driven	$20M+ ARR	ML-based, most accurate	Needs volume + data warehouse

Decision rule: Start with U-shaped. Move to W-shaped when you have opp creation tracking. Move to data-driven when you have 500+ closed-won deals/year.

Phase 3: Funnel Architecture & Definitions

Universal Funnel Stages

Every team MUST agree on these definitions. No exceptions.

yaml

# funnel-definitions.yaml
stages:
  - name: "Visitor"
    definition: "Anonymous website session"
    owner: "Marketing"
    
  - name: "Known"
    definition: "Identified by email (form fill, content download, event)"
    owner: "Marketing"
    
  - name: "MQL (Marketing Qualified Lead)"
    definition: "Meets minimum engagement threshold (score >= 50) AND fits ICP criteria"
    owner: "Marketing"
    criteria:
      behavioral: "Downloaded 2+ assets OR attended webinar OR visited pricing page 2x in 7 days"
      firmographic: "Matches ICP (right industry, size, geo)"
    sla: "Routed to SDR within 5 minutes"
    
  - name: "SAL (Sales Accepted Lead)"
    definition: "SDR confirms lead is real, reachable, and worth pursuing"
    owner: "SDR"
    criteria: "Valid contact info, responded to outreach, confirmed fit"
    sla: "Accept or reject within 4 business hours"
    rejection_reasons:
      - "Bad contact info"
      - "Not decision maker"
      - "Wrong ICP"
      - "Duplicate"
      - "Competitor"
    
  - name: "SQL (Sales Qualified Lead)"
    definition: "Discovery completed, BANT confirmed, has budget/authority/need/timeline"
    owner: "SDR → AE handoff"
    criteria: "BANT score >= 3/4, discovery call completed"
    sla: "AE must have first meeting within 48 hours of handoff"
    
  - name: "Opportunity Created"
    definition: "AE confirms deal is real, enters in CRM with amount and close date"
    owner: "AE"
    required_fields: "Amount, close date, stage, decision maker identified, next step"
    
  - name: "Proposal/Negotiation"
    definition: "Pricing presented, contract in review"
    owner: "AE"
    
  - name: "Closed Won"
    definition: "Contract signed, payment terms agreed"
    owner: "AE → CS handoff"
    sla: "CS kickoff within 48 hours"
    
  - name: "Closed Lost"
    definition: "Deal dead — reason MUST be captured"
    owner: "AE"
    required: "Primary loss reason, competitor (if applicable), notes"

Conversion Rate Benchmarks (B2B SaaS)

Stage Transition	Bottom 25%	Median	Top 25%	World-Class
Visitor → Known	<1%	2-3%	4-6%	8%+
Known → MQL	<5%	8-12%	15-20%	25%+
MQL → SAL	<40%	50-60%	70-80%	85%+
SAL → SQL	<30%	40-50%	55-65%	70%+
SQL → Opp Created	<50%	60-70%	75-85%	90%+
Opp → Closed Won	<15%	20-25%	30-40%	45%+
Full funnel (MQL→CW)	<2%	3-5%	6-10%	12%+

Diagnostic rule: If any stage conversion is bottom 25%, that's your bottleneck. Fix it before optimizing anything else.

Lead Scoring Model

yaml

# lead-scoring.yaml
behavioral_signals:  # Max 60 points
  - action: "Visited pricing page"
    points: 15
    decay: "5 points/week after 14 days"
  - action: "Downloaded whitepaper/ebook"
    points: 10
  - action: "Attended webinar"
    points: 12
  - action: "Requested demo"
    points: 25
  - action: "Opened 3+ emails in 7 days"
    points: 8
  - action: "Visited 5+ pages in session"
    points: 10
  - action: "Returned to site within 7 days"
    points: 8
  - action: "Engaged with chatbot"
    points: 5

firmographic_signals:  # Max 40 points
  - signal: "ICP industry match"
    points: 15
  - signal: "Company size in sweet spot"
    points: 10
  - signal: "Decision-maker title"
    points: 10
  - signal: "Target geography"
    points: 5

thresholds:
  mql: 50
  hot_lead: 75
  
negative_signals:
  - signal: "Competitor domain"
    points: -100
  - signal: "Student/edu email"
    points: -30
  - signal: "Unsubscribed from emails"
    points: -20
  - signal: "No activity in 30 days"
    points: -15

Phase 4: Pipeline Management

Pipeline Coverage Model

text

Required pipeline = Quota ÷ Win Rate × Coverage Multiple

Coverage Multiple by stage:
- $1M quota, 25% win rate = need $4M pipeline (4x)
- Adjust by deal age:
  - Fresh (<30 days): count at 100%
  - Aging (30-60 days past expected close): count at 50%
  - Stale (60+ days past): count at 25%

Healthy Pipeline Ratios:

Metric	Minimum	Healthy	Optimal
Pipeline coverage (total)	3x	3.5-4x	4-5x
Pipeline coverage (weighted)	1.5x	2-2.5x	3x
New pipeline created/month	1x quota	1.5x quota	2x quota
Deals in negotiation stage	15-20% of pipe	25-30%	35%+

Deal Velocity Formula

text

Sales Velocity = (# Opportunities × Win Rate × Average Deal Size) ÷ Sales Cycle Length

Example:
(50 opps × 25% × $30,000) ÷ 60 days = $6,250/day revenue velocity

To increase velocity, improve ANY of:
1. More opportunities (marketing/SDR efficiency)
2. Higher win rate (sales enablement/qualification)
3. Larger deals (pricing/packaging/expansion)
4. Shorter cycles (process optimization/champion enablement)

Pipeline Review Cadence

yaml

# pipeline-review-cadence.yaml
daily:
  who: "AE self-review"
  duration: "15 min"
  focus: "Next steps on active deals, stale deal cleanup"
  
weekly:
  who: "Manager + AE 1:1"
  duration: "30 min"
  focus: "Top 5 deals deep-dive, forecast accuracy, next week commits"
  template: |
    ## Weekly Pipeline Review — [AE Name] — [Date]
    
    ### Forecast
    - Commit: $[X] ([N] deals)
    - Best case: $[X] ([N] deals)
    - Change from last week: +/- $[X]
    
    ### Top 5 Deals
    | Deal | Amount | Stage | Next Step | Risk | Close Date |
    |------|--------|-------|-----------|------|------------|
    
    ### Pipeline Health
    - Coverage: [X]x vs [X]x target
    - New pipe created this week: $[X]
    - Deals pushed: [N] ($[X])
    - Deals lost: [N] ($[X]) — reasons: [...]
    
    ### Actions
    1. [...]

monthly:
  who: "CRO/VP + all managers"
  duration: "60 min"
  focus: "Forecast call, pipeline trends, process gaps"
  
quarterly:
  who: "RevOps + leadership"
  duration: "90 min"
  focus: "Funnel health, conversion trends, capacity planning, process changes"

Forecast Categories

Category	Definition	Confidence	Include in Forecast?
Commit	Verbal/written agreement, contract in process	90%+	Yes — base forecast
Best Case	Strong signals, high engagement, but not committed	60-89%	Yes — upside
Pipeline	Qualified, in active sales cycle	20-59%	Weighted only
Upside	Early stage, unqualified, or long-shot	<20%	No
Omitted	Not closing this period	0%	No

Forecast accuracy target: MAPE (Mean Absolute Percentage Error) < 15%

text

MAPE = |Actual - Forecast| ÷ Actual × 100

Grading:
- <10%: Excellent — trust the forecast
- 10-15%: Good — minor calibration needed
- 15-25%: Needs work — review qualification criteria
- >25%: Broken — rebuild forecast methodology

Phase 5: Revenue Metrics Dashboard

The RevOps Metric Stack

Tier 1: Board Metrics (Monthly)

Metric	Formula	Benchmark (B2B SaaS)
ARR	Sum of all active annual contract values	Growth rate context-dependent
Net Revenue Retention (NRR)	(Beginning ARR + Expansion - Contraction - Churn) ÷ Beginning ARR	Good: 105%+, Great: 115%+, World-class: 130%+
Gross Revenue Retention (GRR)	(Beginning ARR - Contraction - Churn) ÷ Beginning ARR	Good: 85%+, Great: 90%+, World-class: 95%+
CAC	Total S&M spend ÷ New customers acquired	Depends on ACV
LTV	ARPA × Gross Margin ÷ Churn Rate	LTV:CAC > 3:1
CAC Payback	CAC ÷ (ARPA × Gross Margin) in months	Good: <18mo, Great: <12mo
Magic Number	Net New ARR (QoQ) ÷ Prior Quarter S&M Spend	Good: >0.75, Great: >1.0
Burn Multiple	Net Burn ÷ Net New ARR	Good: <2x, Great: <1.5x, Elite: <1x

Tier 2: Operating Metrics (Weekly)

Metric	Owner	Target
MQL volume	Marketing	[Set from model]
MQL → SQL conversion	SDR team	>40%
SQL → Opp conversion	AE team	>60%
Pipeline created ($ and #)	Sales	1.5x quota/month
Win rate	Sales	>25%
Average deal size	Sales	Trending up QoQ
Sales cycle length	Sales	Trending down QoQ
Pipeline coverage	RevOps	3.5-4x
Forecast accuracy (MAPE)	RevOps	<15%

Tier 3: Diagnostic Metrics (On-demand)

Stage-to-stage conversion by segment, rep, source
Time in stage by deal size
Activity metrics (calls, emails, meetings per opp)
Lead response time (target: <5 min for inbound)
Content engagement by funnel stage
Feature adoption rates (for expansion signals)
Support ticket velocity (for churn prediction)

Revenue Dashboard YAML

yaml

# revops-dashboard.yaml
period: "2026-Q1"
updated: "YYYY-MM-DD"

arr:
  current: 0
  beginning_of_quarter: 0
  new_business: 0
  expansion: 0
  contraction: 0
  churned: 0
  net_new: 0

retention:
  nrr: "0%"
  grr: "0%"
  logo_retention: "0%"

efficiency:
  cac: 0
  ltv: 0
  ltv_cac_ratio: "0:1"
  cac_payback_months: 0
  magic_number: 0
  burn_multiple: 0

pipeline:
  total_value: 0
  total_deals: 0
  coverage_ratio: "0x"
  weighted_pipeline: 0
  new_created_this_month: 0
  velocity_per_day: 0

conversion:
  mql_to_sql: "0%"
  sql_to_opp: "0%"
  opp_to_closed_won: "0%"
  full_funnel: "0%"

forecast:
  commit: 0
  best_case: 0
  pipeline: 0
  actual_vs_forecast_last_month: "0%"
  mape: "0%"

health_signals:
  - metric: ""
    status: ""  # green | yellow | red
    note: ""

Phase 6: GTM Efficiency & Unit Economics

GTM Efficiency by ACV Tier

ACV	Primary Motion	Typical CAC	Target Payback	S&M % of Revenue
<$1K	Self-serve / PLG	<$500	<3 months	<30%
$1-10K	Inside sales + PLG	$2-5K	<6 months	30-50%
$10-50K	Inside sales	$10-25K	<12 months	40-60%
$50-100K	Field sales	$30-60K	<18 months	50-70%
$100K+	Enterprise field	$50-150K+	<24 months	40-60%

Capacity Model

text

Required AEs = Revenue Target ÷ (Quota × Expected Attainment)

Example:
$5M new ARR target ÷ ($600K quota × 70% attainment) = 12 AEs needed

Ramp schedule:
- Month 1-2: 0% productivity (onboarding)
- Month 3: 25% productivity
- Month 4-5: 50% productivity  
- Month 6+: 100% productivity (fully ramped)

So 12 AEs needed at full ramp = hire 14-15 to account for ramp + attrition

Rep Productivity Analysis

yaml

# rep-scorecard.yaml
rep_name: ""
period: ""
quota: 0
attainment: "0%"

activity:
  calls_per_day: 0  # target: 40-60 for SDR, 8-12 for AE
  emails_per_day: 0  # target: 30-50 for SDR, 15-20 for AE
  meetings_booked_per_week: 0  # target: 8-12 for SDR, 10-15 for AE
  demos_per_week: 0  # target: 5-8 for AE

pipeline:
  created_this_month: 0
  coverage_ratio: "0x"
  avg_deal_size: 0
  win_rate: "0%"
  avg_cycle_days: 0

efficiency:
  cost_per_meeting: 0  # (rep fully-loaded cost ÷ meetings held)
  revenue_per_activity: 0  # (closed revenue ÷ total activities)
  pipeline_to_close_ratio: "0:1"

coaching_notes:
  strengths: []
  improvement_areas: []
  action_items: []

Phase 7: Marketing-Sales Alignment (SLA Framework)

Marketing → Sales SLA

yaml

# marketing-sla.yaml
commitment:
  mql_volume: "[N] MQLs per month"
  mql_quality: "MQL-to-SQL rate >= [X]%"
  lead_data_completeness: "100% of required fields populated"
  
delivery:
  routing: "MQLs routed to correct SDR within 5 minutes"
  context: "Lead source, engagement history, and score visible in CRM"
  
reporting:
  frequency: "Weekly MQL report by source, score band, and ICP tier"
  review: "Monthly alignment meeting with sales leadership"

Sales → Marketing SLA

yaml

# sales-sla.yaml
commitment:
  response_time: "Contact MQL within 4 business hours"
  follow_up: "Minimum 6-touch sequence over 14 days before rejecting"
  feedback: "Rejection reason provided within 48 hours"
  
delivery:
  crm_hygiene: "All MQLs dispositioned within 48 hours (accepted/rejected)"
  win_loss: "Closed-lost reason + competitor captured on every deal"
  
reporting:
  frequency: "Weekly SAL/SQL report with rejection reasons"
  review: "Monthly alignment meeting with marketing leadership"

Sales → CS Handoff SLA

yaml

# cs-handoff-sla.yaml
trigger: "Contract signed"
sales_responsibilities:
  - "Complete handoff document within 24 hours"
  - "Intro email to CS owner within 24 hours"
  - "Joint kickoff call within 5 business days"
  
handoff_document:
  - "Customer goals and success criteria"
  - "Technical requirements discussed"
  - "Key stakeholders and champions"
  - "Pricing/discount details and renewal date"
  - "Risks identified during sales process"
  - "Competitive alternatives considered"
  
cs_responsibilities:
  - "Acknowledge handoff within 4 hours"
  - "Send welcome email within 24 hours"
  - "Schedule onboarding kickoff within 48 hours"

Phase 8: Revenue Process Automation

Automation Priority Stack

Process	Impact	Effort	Priority
Lead routing	High — speed kills	Low	P0 — Do first
Lead scoring	High — quality focus	Medium	P0
Stage progression alerts	Medium — pipeline hygiene	Low	P1
Renewal reminders (90/60/30 day)	High — retention	Low	P1
Expansion signal alerts	High — NRR	Medium	P1
Forecast roll-up	Medium — accuracy	Medium	P2
Activity logging	Medium — data quality	Medium	P2
Win/loss analysis compilation	Medium — learning	High	P2
Comp calculation	Medium — motivation	High	P3
Territory assignment	Low (unless scaling fast)	High	P3

Lead Routing Logic

yaml

# lead-routing.yaml
rules:
  - name: "Enterprise (500+ employees)"
    condition: "company_size >= 500 AND icp_tier IN ['A', 'B']"
    route_to: "enterprise_ae_round_robin"
    sla: "5 minutes"
    
  - name: "Mid-market (50-499)"
    condition: "company_size BETWEEN 50 AND 499"
    route_to: "mm_sdr_round_robin"
    sla: "5 minutes"
    
  - name: "SMB (<50)"
    condition: "company_size < 50 AND lead_score >= 50"
    route_to: "smb_sdr_round_robin"
    sla: "15 minutes"
    
  - name: "Low score"
    condition: "lead_score < 50"
    route_to: "nurture_campaign"
    sla: "N/A — automated nurture"
    
  - name: "Named account"
    condition: "account IN named_account_list"
    route_to: "assigned_ae_direct"
    sla: "Immediate notification"
    
fallback: "marketing_ops_queue"
escalation: "If no action in 30 minutes, re-route to manager"

Expansion Signal Detection

yaml

# expansion-signals.yaml
usage_signals:
  - signal: "Approaching seat/usage limit (>80%)"
    action: "Alert CS + AE, send upgrade nudge"
    urgency: "High"
  - signal: "New department/team using product"
    action: "Alert AE for cross-sell conversation"
    urgency: "Medium"
  - signal: "API usage growing >20% MoM"
    action: "Log for QBR, prepare enterprise tier pitch"
    urgency: "Medium"

engagement_signals:
  - signal: "Executive attended webinar"
    action: "Alert AE, potential champion expansion"
    urgency: "High"
  - signal: "Support ticket from new department"
    action: "Alert CS, new user group emerging"
    urgency: "Medium"

lifecycle_signals:
  - signal: "Renewal in 90 days + healthy NPS"
    action: "Initiate renewal + expansion conversation"
    urgency: "High"
  - signal: "12 months since last price increase"
    action: "Flag for pricing review at renewal"
    urgency: "Low"

Phase 9: Compensation & Territory Design

Comp Plan Architecture

Role	Base:Variable	OTE Range	Quota Multiple
SDR	70:30	$55-85K	Pipeline generated = 3-5x OTE
AE (SMB)	50:50	$100-150K	New ARR = 4-6x OTE
AE (Mid-Market)	50:50	$150-250K	New ARR = 4-5x OTE
AE (Enterprise)	60:40	$200-350K	New ARR = 3-4x OTE
CS/AM	70:30	$80-150K	NRR + expansion targets

Comp Design Rules:

Variable comp should be simple — max 3 components
Accelerators kick in at 100% attainment (1.5-2x rate)
Decelerators below 50% attainment (0.5x rate)
SPIFs should be <10% of total comp — use sparingly
Clawback only on churns within 90 days
Pay monthly, not quarterly (motivation)

Territory Design

yaml

# territory-design.yaml
method: "balanced"  # balanced | named-account | geographic | vertical

balancing_criteria:
  - factor: "Total addressable accounts"
    weight: 30
  - factor: "Historical revenue potential"
    weight: 30
  - factor: "Current pipeline value"
    weight: 20
  - factor: "Account density (effort to cover)"
    weight: 20

rules:
  - "No rep should have >2x the TAM of another rep"
  - "Named accounts assigned by relationship, not geography"
  - "New territories get 25% pipeline seed from marketing"
  - "Territory changes only at fiscal year (exceptions: termination, promotion)"
  - "Overlay reps (solutions engineers) shared across max 4 AEs"

review_cadence: "Quarterly assessment, annual reassignment"

Phase 10: Tech Stack Integration

RevOps Tech Stack by Stage

Stage	Must-Have	Nice-to-Have	Premium
Pre-$1M	CRM (HubSpot Free/Pipedrive), Stripe, Google Analytics	Email sequencer (Apollo/Instantly), Basic BI	—
$1-5M	CRM (HubSpot Pro/Salesforce), Marketing automation, Billing (Stripe/Chargebee)	Enrichment (Clearbit/Apollo), Call recording (Gong/Chorus), CPQ	Data warehouse
$5-20M	Full CRM, MA, Billing, Data warehouse, BI tool	RevOps platform (Clari/Aviso), ABM (Demandbase/6sense), CS platform (Gainsight)	CDI (Census/Hightouch)
$20M+	All of above + CPQ, Advanced analytics	AI forecasting, Deal intelligence, Revenue intelligence platform	Custom data models

Integration Architecture

text

Marketing Stack → CRM ← Sales Stack
       ↓            ↓           ↓
    Attribution   Pipeline    Activity
       ↓            ↓           ↓
       └──── Data Warehouse ────┘
                    ↓
              BI Dashboard
                    ↓
            Automated Alerts

Critical integrations (in priority order):

Website → CRM (form fills, page views)
Email → CRM (sequence activity, replies)
Calendar → CRM (meeting logging)
Billing → CRM (subscription data, usage)
CS platform → CRM (health scores, tickets)
All → Data warehouse (for cross-system analysis)

Phase 11: Forecasting & Planning

Annual Revenue Planning Model

yaml

# revenue-plan.yaml
fiscal_year: "2026"

targets:
  total_arr_target: 0
  new_business: 0  # typically 60-70% of net new
  expansion: 0     # typically 30-40% of net new
  
assumptions:
  gross_churn_rate: "0%"
  expansion_rate: "0%"
  avg_new_deal_size: 0
  avg_expansion_deal_size: 0
  new_win_rate: "0%"
  expansion_win_rate: "0%"  # typically 2-3x new business win rate
  avg_sales_cycle_new: "0 days"
  avg_sales_cycle_expansion: "0 days"
  
derived:
  new_deals_needed: 0  # new_business ÷ avg_deal_size
  opps_needed: 0       # new_deals_needed ÷ win_rate
  sqls_needed: 0       # opps_needed ÷ sql_to_opp_rate
  mqls_needed: 0       # sqls_needed ÷ mql_to_sql_rate
  pipeline_needed: 0   # opps_needed × avg_deal_size

capacity:
  aes_at_full_ramp: 0
  quota_per_ae: 0
  expected_attainment: "0%"
  productive_capacity: 0  # aes × quota × attainment
  gap: 0  # target - capacity
  hires_needed: 0

Scenario Planning

Always model three scenarios:

Scenario	Revenue	Key Assumptions	Actions
Bear (70% confidence)	-20% from plan	Win rate drops 5pts, cycle +15 days, churn +2pts	Reduce hiring, focus on expansion, cut discretionary
Base (50% confidence)	Plan	Current trends continue	Execute plan
Bull (30% confidence)	+20% from plan	Win rate up 5pts, cycle -10 days, expansion up	Accelerate hiring, invest in new channels

Phase 12: RevOps Operating Rhythm

Weekly RevOps Cadence

Day	Meeting	Duration	Attendees	Focus
Monday	Pipeline generation review	30 min	SDR managers + Marketing	MQL quality, outbound metrics, campaign performance
Tuesday	Deal review	45 min	AE managers	Top deals, stuck deals, forecast updates
Wednesday	Cross-functional sync	30 min	RevOps + Marketing + Sales + CS leads	Funnel health, SLA compliance, blockers
Thursday	Forecast call	30 min	CRO + managers	Commit/best case updates, risk deals
Friday	Data quality + process	30 min	RevOps team	Hygiene reports, automation updates, tooling

Monthly Review Template

markdown

## Monthly RevOps Review — [Month Year]

### Headline Metrics
| Metric | Actual | Target | Δ | Trend |
|--------|--------|--------|---|-------|
| ARR | | | | ↑↓→ |
| Net New ARR | | | | |
| NRR | | | | |
| CAC Payback | | | | |
| Pipeline Coverage | | | | |
| Forecast Accuracy | | | | |

### Funnel Analysis
| Stage | Volume | Conversion | vs. Last Month | vs. Target |
|-------|--------|-----------|----------------|------------|

### What Worked
1. [...]

### What Didn't
1. [...]

### Process Changes Made
1. [...]

### Next Month Priorities
1. [...]

Quarterly Business Review (QBR) Structure

Results vs. Plan (10 min) — ARR, NRR, efficiency metrics
Funnel Deep Dive (15 min) — Stage-by-stage with cohort trends
Pipeline Quality (10 min) — Coverage, aging, source mix
GTM Efficiency (10 min) — CAC, payback, magic number, by segment
Team Performance (10 min) — Rep productivity, ramp, attrition
Process & Tech (10 min) — What changed, what's planned
Next Quarter Plan (15 min) — Targets, capacity, key bets

Phase 13: Advanced RevOps Patterns

Revenue Intelligence

Build signals that predict outcomes before they happen:

Signal	Predicts	Data Source	Action
Multi-threading (3+ contacts engaged)	2.3x higher win rate	CRM + email	Coach reps on multi-threading
Champion job change	Churn risk OR new opp	LinkedIn alerts	CS: protect account, Sales: pursue new co
Decreasing product usage	Churn in 60-90 days	Product analytics	CS intervention + exec sponsor call
Pricing page + competitor page in same session	High-intent comparison shopper	Web analytics	Priority SDR outreach
CFO/finance contact added to deal	Deal in budget approval	CRM	Adjust timeline, prepare ROI doc

Cohort Analysis Framework

Track every cohort of customers by:

Acquisition month — Do newer cohorts retain better?
ACV band — Do bigger deals churn less?
Sales cycle length — Do faster deals have higher NRR?
Lead source — Which channels produce best LTV?
Industry — Which verticals are stickiest?

PLG + Sales Hybrid Model

yaml

# plg-sales-handoff.yaml
self_serve_signals:
  - signal: "Workspace has 5+ active users"
    action: "Auto-assign to AE for outreach"
  - signal: "Hitting usage limits"
    action: "In-app upgrade prompt + AE notification"
  - signal: "Admin invited 10+ users"
    action: "Schedule product-led onboarding call"
  - signal: "Enterprise domain detected (Fortune 500)"
    action: "Immediate AE assignment regardless of usage"

pql_definition:  # Product Qualified Lead
  must_have:
    - "Completed onboarding (core activation milestone)"
    - "3+ active users in last 7 days"
    - "Used 2+ core features"
  nice_to_have:
    - "Connected integration"
    - "Shared workspace externally"
    - "Hit usage warning (>80% of limit)"

Phase 14: Common RevOps Mistakes

#	Mistake	Fix
1	Too many metrics — can't focus	Max 5 metrics per team, aligned to one goal
2	MQL definition too loose	Tighten with firmographic + behavioral (score >50)
3	No SLAs between teams	Implement Phase 7 SLAs, review monthly
4	CRM is a data graveyard	Required fields, validation rules, weekly hygiene
5	Forecast = wishful thinking	MEDDPICC-based categories, track accuracy
6	Over-automating before process exists	Manual first, then automate what works
7	Comp plan rewards wrong behavior	Align to NRR, not just new logo
8	No closed-lost analysis	Mandatory field, monthly review, product feedback loop
9	RevOps reports to Sales only	Report to CRO/CEO — neutral across functions
10	Building dashboards nobody uses	Start with questions, not charts

100-Point RevOps Quality Rubric

Dimension	Weight	Criteria
Data Integrity	20	Single source of truth, <2% duplicates, required fields enforced, hygiene automated
Funnel Definitions	15	All stages defined, agreed cross-functionally, conversion tracked weekly
Pipeline Management	15	Coverage tracked, velocity measured, forecast accuracy <15% MAPE
Cross-Team Alignment	15	SLAs exist, reviewed monthly, handoffs documented, shared metrics
Automation	10	Lead routing <5 min, renewal alerts automated, key workflows built
Analytics	10	Dashboard updated weekly, cohort analysis running, leading indicators tracked
Compensation	8	Plans documented, aligned to strategy, accelerators at 100%, simple (≤3 components)
Process Documentation	7	Playbooks exist, onboarding covers them, quarterly review cycle

Scoring: 0-2 per sub-criterion within each dimension.

80-100: World-class RevOps
60-79: Strong foundation
40-59: Gaps are costing revenue
<40: RevOps is a title, not a function

Edge Cases

Startup (Pre-$1M ARR)

Skip territory design and comp complexity
Focus on: funnel definitions, CRM hygiene, basic pipeline tracking
One person can be "RevOps" part-time (often founder or first ops hire)

PLG-Dominant

Replace MQL with PQL (product qualified lead)
Lead scoring = product usage signals, not content engagement
Self-serve metrics: activation rate, time-to-value, conversion from free

Usage-Based Pricing

Pipeline = estimated annual usage, not fixed contract
Forecasting is harder — use trailing usage trends + growth rate
Expansion is organic — track net dollar expansion separately

Multi-Product

Attribution gets complex — track by product line
Cross-sell pipeline tracked separately from new business
Beware double-counting ARR across products

International

Territory design must account for language, timezone, currency
Separate pipeline and conversion benchmarks by region
Local compliance (GDPR, data residency) affects tech stack

Post-M&A Integration

Audit both CRM systems — pick one, migrate fast
Reconcile definitions (their "SQL" ≠ your "SQL")
Expect 3-6 month data quality dip — plan for it

Natural Language Commands

When asked, you can:

"Audit our RevOps" — Walk through Phase 1 maturity assessment
"Build our funnel definitions" — Generate Phase 3 complete funnel YAML
"Create a pipeline review template" — Generate Phase 4 weekly review
"Build our metrics dashboard" — Generate Phase 5 dashboard YAML
"Design our lead scoring model" — Generate Phase 3 scoring YAML
"Create marketing-sales SLAs" — Generate Phase 7 SLA documents
"Model our revenue plan" — Generate Phase 11 planning model
"Score our RevOps maturity" — Run full Phase 1 assessment with recommendations
"Design our comp plan" — Generate Phase 9 compensation structure
"Diagnose our funnel" — Analyze conversion rates against benchmarks
"Build expansion signals" — Generate Phase 8 expansion detection YAML
"Create our forecast model" — Generate Phase 4 + Phase 11 forecast framework

RevOps Engine

Install

Revenue Operations (RevOps) Engine

Phase 1: RevOps Assessment & Foundation

Revenue Architecture Audit

RevOps Maturity Model (Score 1-5 per dimension)

Phase 2: Revenue Data Architecture

Single Source of Truth Design

Object Model

Required Fields by Object

Data Hygiene Rules

Attribution Model Selection

Phase 3: Funnel Architecture & Definitions

Universal Funnel Stages

Conversion Rate Benchmarks (B2B SaaS)

Lead Scoring Model

Phase 4: Pipeline Management

Pipeline Coverage Model

Deal Velocity Formula

Pipeline Review Cadence

Forecast Categories

Phase 5: Revenue Metrics Dashboard

The RevOps Metric Stack

Tier 1: Board Metrics (Monthly)

Tier 2: Operating Metrics (Weekly)

Tier 3: Diagnostic Metrics (On-demand)

Revenue Dashboard YAML

Phase 6: GTM Efficiency & Unit Economics

GTM Efficiency by ACV Tier

Capacity Model

Rep Productivity Analysis

Phase 7: Marketing-Sales Alignment (SLA Framework)

Marketing → Sales SLA

Sales → Marketing SLA

Sales → CS Handoff SLA

Phase 8: Revenue Process Automation

Automation Priority Stack

Lead Routing Logic

Expansion Signal Detection

Phase 9: Compensation & Territory Design

Comp Plan Architecture

Territory Design

Phase 10: Tech Stack Integration

RevOps Tech Stack by Stage

Integration Architecture

Phase 11: Forecasting & Planning

Annual Revenue Planning Model

Scenario Planning

Phase 12: RevOps Operating Rhythm

Weekly RevOps Cadence

Monthly Review Template

Quarterly Business Review (QBR) Structure

Phase 13: Advanced RevOps Patterns

Revenue Intelligence

Cohort Analysis Framework

PLG + Sales Hybrid Model

Phase 14: Common RevOps Mistakes

100-Point RevOps Quality Rubric

Edge Cases

Startup (Pre-$1M ARR)

PLG-Dominant

Usage-Based Pricing

Multi-Product

International

Post-M&A Integration

Natural Language Commands

Related skills