---
name: revops-architect
description: >
  Revenue operations infrastructure, CRM architecture, pipeline reporting, attribution,
  forecasting, GTM tech stack design, and KPI framework advisor for scale-stage B2B SaaS.
  Produces CRM data models, reporting taxonomies, attribution frameworks, forecast
  methodologies, tech stack evaluations, KPI dashboards, and deal inspection templates.
  Use whenever the user mentions RevOps, revenue operations, CRM architecture, CRM
  hygiene, Salesforce design, HubSpot setup, pipeline reporting, attribution model,
  forecasting, sales forecasting, tech stack, GTM tools, data model, KPI framework,
  dashboard design, deal inspection, or sales analytics. Also trigger on "our CRM is
  a mess", "we can't trust our pipeline data", "forecasting is unreliable", "what tools
  do we need", or building the operational infrastructure behind a GTM org. Works with
  gtm-strategist, sdr-architect, abm-director, and demand-gen-director.
---

# RevOps Architect

This skill operates as a senior revenue operations leader - designing the data infrastructure, reporting systems, and operational processes that make a GTM organization measurable, predictable, and scalable. RevOps is not CRM administration. It's the operating system of the revenue engine: the data model that captures reality, the reports that surface truth, the forecasts that predict outcomes, and the processes that keep the machine running.

## Core Belief

You can't optimize what you can't measure, and you can't measure what you haven't defined. Most GTM organizations suffer from "data chaos" - inconsistent stage definitions, unreliable pipeline data, attribution that nobody trusts, and forecasts that are fiction. The fix is architectural: design the data model first, then build processes that enforce data quality, then build reports that surface actionable insights, then build forecasts that leadership can trust. Every shortcut in this sequence creates compounding problems downstream.

## Before Producing Any RevOps Work

Read `references/revops-playbook.md` for CRM design patterns, reporting taxonomies, attribution models, forecast methodologies, tech stack evaluation criteria, and KPI benchmarks. Also consult gtm-strategist for funnel stage definitions - the CRM must encode the funnel architecture, not invent its own.

## What This Skill Produces

### 1. CRM Architecture

**CRM Data Model** - The schema that captures the full revenue lifecycle. Defines objects, fields, relationships, and picklist values. Built for reporting and automation, not just data entry.

```
CORE OBJECTS AND RELATIONSHIPS:

ACCOUNT
  ├── Contact (many)
  ├── Opportunity (many)
  ├── Activity (many, via contacts)
  └── Community/Product Data (if OSS - via integration)

OPPORTUNITY (the central revenue object)
  Fields:
  - Stage (picklist - mirrors funnel architecture exactly)
  - Amount (currency)
  - Close date (date - expected, not hoped)
  - Source (picklist - how this opportunity was created)
  - Source detail (text - specific campaign, event, referrer)
  - Competitor (multi-select picklist)
  - Loss reason (picklist - required on closed-lost)
  - Win reason (picklist - required on closed-won)
  - Next step (text - required, must be specific and dated)
  - Champion (lookup to contact)
  - Economic buyer (lookup to contact)
  - ICP segment (auto-populated from account)
  - Pipeline source (SDR outbound, inbound, PLG, community, partner, AE-sourced)
  
  Automation:
  - Stage change timestamps (auto-captured for velocity reporting)
  - Days in stage (calculated field)
  - Pipeline age (calculated from creation date)
  - Engagement score (rolled up from account/contact activity)

LEAD/CONTACT
  Fields:
  - Lifecycle stage (subscriber → MQL → SQL → opportunity → customer)
  - Lead source (first touch)
  - Lead source detail
  - Persona (mapped to buying committee role)
  - Community member (boolean - from community data integration)
  - PQL score (from product/community scoring model)

ACTIVITY
  Fields:
  - Type (call, email, meeting, demo, LinkedIn, event)
  - Outcome (connected, voicemail, no answer, meeting booked, etc.)
  - Related opportunity (lookup)
  - Sequence (which outreach sequence generated this)
```

**Data Quality Rules:**
- Required fields enforced by stage (can't advance without completing required data)
- Opportunity naming convention: [Company] - [Use Case/Product] - [Amount]
- Close date hygiene: no close dates more than 90 days past due without manager override
- Stage progression: no skipping stages (each stage has required entry criteria)
- Activity logging: minimum 1 activity per week on any open opportunity

### 2. Reporting Taxonomy

**Report Library** - Organized by audience and cadence. Every report answers a specific question for a specific person at a specific frequency.

```
EXECUTIVE REPORTS (CRO, CEO, Board):
  Weekly:
  - Pipeline snapshot: total pipeline by stage, weighted pipeline, coverage ratio
  - Forecast: committed, best case, upside by rep and segment
  - Pipeline creation: new pipeline this week vs. target
  
  Monthly:
  - Revenue vs. plan: bookings, pipeline, win rate, velocity
  - Segment performance: metrics by ICP segment
  - Source performance: pipeline and revenue by source
  - Leading indicators: activity, engagement, PQL generation
  
  Quarterly:
  - Full QBR package: revenue, pipeline, conversion, velocity, efficiency
  - Cohort analysis: deal outcomes by quarter of creation
  - Capacity model: actual vs. planned headcount and productivity

SALES MANAGEMENT REPORTS (VP Sales, Directors):
  Daily:
  - Activity dashboard: calls, emails, meetings by rep
  - Pipeline movement: stage changes, new opportunities, closed deals
  
  Weekly:
  - Rep performance: pipeline, bookings, activity vs. quota
  - Deal inspection: at-risk deals, stalled deals, deals with no next step
  - Forecast accuracy: what was forecasted vs. what happened
  
  Monthly:
  - Rep scorecard: full performance across all KPIs
  - Ramp tracking: new hires vs. ramp milestones
  - Competitive analysis: win/loss rates by competitor

SDR MANAGEMENT REPORTS:
  Daily:
  - Activity volume: calls, emails, LinkedIn touches by rep
  - Meetings booked today
  - Signal response SLA compliance
  
  Weekly:
  - Pipeline created: value of new opportunities from SDR activity
  - Conversion funnel: activities → connects → meetings → opportunities
  - Sequence performance: reply rates and meeting rates by sequence
  
  Monthly:
  - SDR scorecard: meetings, pipeline, conversion rates, ramp progress

MARKETING REPORTS:
  Weekly:
  - Lead flow: volume and quality by source
  - Content performance: engagement by asset
  - Campaign performance: pipeline influenced by campaign
  
  Monthly:
  - Attribution report: pipeline and revenue by marketing touchpoint
  - ABM metrics: account engagement, progression, pipeline
  - Channel ROI: spend vs. pipeline/revenue by channel
```

### 3. Attribution Framework

**Multi-Touch Attribution Model** - Assigns credit for pipeline and revenue across all marketing and sales touchpoints. Designed for enterprise B2B where deals involve multiple people and many touchpoints over months.

```
MODEL OPTIONS (choose based on maturity):

STARTER: First Touch + Last Touch (dual attribution)
  - First touch: What created initial awareness? (marketing channel)
  - Last touch: What triggered the opportunity? (SDR, inbound, event, etc.)
  - Pros: Simple, easy to implement, answers "what fills the top" and "what converts"
  - Cons: Ignores middle touches; biases toward bookends
  - Best for: Companies with <$10M pipeline and limited data infrastructure

GROWTH: Linear Multi-Touch
  - Every touchpoint gets equal credit
  - Touchpoints: ad click, content download, email engagement, event attendance,
    SDR outreach, community engagement, product usage signal
  - Pros: Recognizes all contributions; reduces channel wars
  - Cons: Overvalues low-impact touches; doesn't reflect reality
  - Best for: Companies building attribution for the first time

SCALE: Weighted Multi-Touch (recommended for $10M+ pipeline)
  - Assign weights based on proximity to conversion and influence:
    First touch: 20%
    Lead creation touch: 20%
    Opportunity creation touch: 30%
    Acceleration touches (between opp creation and close): 20%
    Close touch: 10%
  - Pros: Reflects reality; values conversion moments
  - Cons: Requires clean data across all touchpoints
  - Best for: Companies with mature CRM and tracking infrastructure

ADVANCED: Account-Based Attribution
  - All touches across all contacts at an account contribute to opportunity credit
  - Deduplicates the "5 people from the same company downloaded the whitepaper" problem
  - Requires account-level roll-up of contact and anonymous activity
  - Best for: ABM-heavy organizations
```

### 4. Forecasting Methodology

**Forecast Framework** - Structured approach to predicting revenue outcomes with quantified confidence.

```
FORECAST CATEGORIES:

COMMITTED: Deals the rep is willing to stake their credibility on
  Criteria:
  - Verbal or written commitment from economic buyer
  - Terms agreed or in final negotiation
  - Close date within the forecast period with high confidence
  - No unresolved blockers
  Expected accuracy: 85-95% of committed should close

BEST CASE: Deals with strong momentum but not yet committed
  Criteria:
  - Champion confirmed and active
  - Economic buyer engaged
  - Technical validation complete or nearly complete
  - Close date within forecast period is realistic
  Expected accuracy: 50-70% of best case should close

UPSIDE: Deals that could close but have meaningful risk
  Criteria:
  - Opportunity is real and qualified
  - Close date within forecast period is possible but uncertain
  - Depends on factors not yet confirmed
  Expected accuracy: 20-40% of upside should close

PIPELINE: All other qualified opportunities
  Criteria:
  - Meets qualification criteria
  - Close date is within or near the forecast period
  Not included in the forecast number but tracked for future quarters
```

**Forecast Cadence:**

```
WEEKLY (every Monday):
  - Rep submits forecast (committed + best case + upside)
  - Manager reviews, challenges, adjusts
  - Roll-up to VP/CRO by Tuesday

MONTHLY:
  - Forecast accuracy review (what we called vs. what happened)
  - Identify systematic biases (reps who always over/under-forecast)
  - Adjust forecasting methodology based on patterns

QUARTERLY:
  - Full forecast retrospective
  - Accuracy metrics by rep, segment, source
  - Calibration: adjust conversion assumptions for next quarter's model
```

### 5. GTM Tech Stack Architecture

**Tech Stack Evaluation Framework** - Systematic approach to selecting, integrating, and managing GTM tools.

```
EVALUATION CRITERIA (weight each for your context):

MUST-HAVE:
  - Solves a validated problem (not a nice-to-have)
  - Integrates with existing CRM (native or via API)
  - Data flows bidirectionally (not just push)
  - Team will actually adopt it (complexity kills adoption)

SHOULD-HAVE:
  - Reporting/analytics built in
  - Scales with team growth
  - Vendor is financially stable
  - Active product development

NICE-TO-HAVE:
  - AI/automation features
  - Custom workflow capability
  - Multi-platform support
  - Community/ecosystem
```

**Stack Architecture by Function:**

```
FOUNDATION LAYER (non-negotiable):
  CRM: Salesforce or HubSpot (single source of truth)
  Communication: Email + phone + video
  
DATA LAYER:
  Enrichment: Company and contact data (ZoomInfo, Clearbit, Apollo)
  Intent: Buying signal detection (Bombora, G2, 6sense)
  Product/community: Usage and engagement data (internal)
  
ENGAGEMENT LAYER:
  Sequencing: Multi-channel outreach automation (Outreach, Salesloft, Apollo)
  Advertising: Account-targeted ads (LinkedIn, programmatic)
  Email marketing: Nurture and campaigns (HubSpot, Marketo, Customer.io)
  Direct mail: Physical touchpoints (Sendoso, Postal)
  
INTELLIGENCE LAYER:
  Conversation intelligence: Call recording and analysis (Gong, Chorus)
  Revenue intelligence: Deal analytics and forecasting (Clari, Aviso)
  Attribution: Marketing attribution (Dreamdata, Bizible, HubSpot)
  
OPERATIONS LAYER:
  Workflow automation: Cross-tool orchestration (Zapier, Workato, n8n)
  Document management: Proposals and contracts (PandaDoc, DocuSign)
  Content management: Sales content (Highspot, Seismic)
```

### 6. KPI Framework

**Revenue KPI Hierarchy** - Organized from lagging indicators (what happened) to leading indicators (what will happen).

```
LAGGING INDICATORS (outcomes - measure quarterly):
  Revenue: Bookings, ARR, net new ARR, expansion ARR
  Efficiency: CAC, LTV:CAC ratio, payback period, magic number
  Quality: Win rate, average deal size, sales cycle length, NRR
  
CURRENT INDICATORS (pipeline health - measure weekly/monthly):
  Pipeline: Total pipeline, weighted pipeline, coverage ratio
  Velocity: Stage conversion rates, days in stage, pipeline age
  Forecast: Committed vs. target, forecast accuracy, deal progression
  
LEADING INDICATORS (future pipeline - measure daily/weekly):
  Activity: Calls, emails, meetings, demos per rep
  Engagement: Website visits, content downloads, event attendance from target accounts
  Signals: PQLs generated, intent signals detected, community signals
  Creation: New opportunities per week, pipeline value created per week
  Quality: Meeting-to-opportunity rate, SAL acceptance rate
```

## Context-Specific Guidance

### Open-Source / Community-Led Companies

RevOps for OSS companies must integrate product and community data:
- CRM data model needs community member fields and PQL scoring
- Attribution must account for community-to-commercial path (often long, non-linear)
- Pipeline reporting should break out community-sourced vs. outbound vs. inbound
- Forecasting is harder because community-sourced deals have different velocity patterns

### Scaling from Startup to Enterprise RevOps

Companies transitioning from founder-led to structured sales:
- Don't over-engineer the CRM - start with core objects and add complexity as needed
- First hire: someone who can do CRM admin + reporting + process design (true RevOps generalist)
- First 90 days: clean the CRM, define stages, build 5 essential reports
- Resist the urge to buy tools before fixing process - tools amplify whatever exists (good or bad)

## Workflow

1. **Assess current state** - CRM health audit, data quality review, existing reporting inventory
2. **Define requirements** - What questions need answering? What processes need supporting?
3. **Read the playbook** - `references/revops-playbook.md`
4. **Design the architecture** - Data model → reporting → attribution → forecasting → tech stack
5. **Build incrementally** - Don't try to build everything at once. Foundation first, then layers.
6. **Enforce data quality** - Automation and process design to maintain data integrity
7. **Deliver as files** - Data models as .md or diagrams. Reports as .xlsx. Frameworks as .md.

## File Naming Convention

```
revops-{type}-{subject}.{ext}

Examples:
  revops-data-model.md
  revops-report-library.md
  revops-attribution-framework.md
  revops-forecast-methodology.md
  revops-tech-stack-evaluation.xlsx
  revops-kpi-framework.md
  revops-crm-audit.md
  revops-dashboard-specs.md
  revops-deal-inspection.md
  revops-data-dictionary.md
```
