# RevOps Architect Playbook

Reference playbook for the revops-architect skill. Read the relevant section before producing any RevOps artifact.

## Table of Contents

1. CRM Design Patterns
2. Data Quality Program
3. Reporting - Build Guide
4. Attribution - Implementation Guide
5. Forecasting - Detailed Methodology
6. Tech Stack Evaluation Process
7. KPI Benchmarks by Stage
8. CRM Audit Methodology
9. RevOps Team Design
10. Process Automation Patterns

---

## 1. CRM Design Patterns

### Salesforce Object Model (Enterprise Standard)

```
STANDARD OBJECTS (use as-is):
  Account → Contact → Opportunity → Activity

CUSTOM OBJECTS (add as needed):
  Community_Engagement__c  (for OSS signal tracking)
  PQL_Score__c            (product-qualified lead scoring)
  Competitor_Intel__c     (field intelligence submissions)
  Win_Loss_Analysis__c    (structured win/loss data)

CRITICAL RELATIONSHIPS:
  Account → Opportunities (1:many)
  Contact → Opportunity Contact Roles (many:many - ENFORCE THIS)
  Opportunity → Activities (via contact roles)
  Account → Community_Engagement__c (1:many, for OSS companies)
```

### HubSpot Architecture (Growth-Stage Standard)

```
USE HUBSPOT WHEN:
  - Team < 20 sales + marketing
  - ACV < $100K (or just moving upmarket)
  - Budget-conscious (HubSpot is cheaper at lower tiers)
  - Marketing + sales want one platform

USE SALESFORCE WHEN:
  - Team > 20 sales + marketing
  - ACV > $100K and complex deal structures
  - Need advanced customization, CPQ, or complex automation
  - Multiple business units or international operations

HUBSPOT KEY CONFIGURATIONS:
  - Deal stages mirror funnel architecture
  - Required properties enforced by deal stage
  - Lead scoring configured for MQL definition
  - Contact lifecycle stages defined and automated
  - Custom properties for ICP scoring, persona, PQL
```

### Field Design Principles

**Picklists over free text** - Wherever possible, use picklists for fields that will be used in reporting. Free text is unsearchable, unfiltereable, and inconsistent.

**Required fields by stage** - Use progressive profiling: fields become required as deals advance, not all at once at creation.

```
STAGE 1 (Discovery): Account, amount estimate, source, close date estimate
STAGE 2 (Evaluation): Champion, technical evaluator, competitor, use case
STAGE 3 (Proposal): Economic buyer, decision criteria, decision process, budget
STAGE 4 (Negotiation): Contract terms, legal contact, procurement contact
STAGE 5 (Closed): Win/loss reason (required), next steps for CS handoff
```

**Calculated fields** - Let the CRM do the math:
- Days in current stage (auto-calculated from stage change timestamp)
- Total pipeline age (auto-calculated from opportunity creation date)
- Weighted amount (amount × probability by stage)
- Account engagement score (rolled up from contact activities)

---

## 2. Data Quality Program

### The Data Quality Framework

```
DIMENSION 1: COMPLETENESS
  Are required fields populated?
  Measurement: % of opportunities with all stage-required fields complete
  Target: >95%
  Fix: Required field validation rules + weekly audit report

DIMENSION 2: ACCURACY
  Is the data correct?
  Measurement: Spot-check audit (monthly sample of 20 deals)
  Target: >90% accuracy on key fields (stage, amount, close date)
  Fix: Manager deal reviews that verify CRM matches reality

DIMENSION 3: TIMELINESS
  Is data entered promptly?
  Measurement: Average lag between event and CRM update
  Target: <24 hours for stage changes, <48 hours for activities
  Fix: Daily activity logging requirement + automated reminders

DIMENSION 4: CONSISTENCY
  Is the same data entered the same way?
  Measurement: Picklist usage (% using standard values vs. "Other")
  Target: <10% "Other" on any picklist
  Fix: Picklist refinement + training on when to use each value

DIMENSION 5: DEDUPLICATION
  Are there duplicate records?
  Measurement: Duplicate scan (accounts, contacts, opportunities)
  Target: <2% duplicate rate
  Fix: Automated dedup rules + quarterly cleanup
```

### Data Quality Enforcement Mechanisms

| Mechanism | What It Does | Effort | Impact |
|-----------|-------------|--------|--------|
| Validation rules | Prevent saving records with missing/invalid data | Low | High |
| Required fields by stage | Progressive data collection as deals advance | Low | High |
| Automated reminders | Slack/email nudges for stale or incomplete data | Low | Medium |
| Manager deal reviews | Weekly review catches errors and reinforces standards | Medium | High |
| Monthly data audit | Systematic review of a sample for accuracy | Medium | Medium |
| Quarterly cleanup | Bulk dedup, stale record archival, picklist refinement | High | High |

### CRM Hygiene Cadence

```
DAILY (automated):
  - Flag opportunities with no activity in 14+ days
  - Flag opportunities with close date in the past
  - Flag opportunities in early stages for 2x+ benchmark time

WEEKLY (manager):
  - Review flagged opportunities in team meeting
  - Verify stage accuracy on forecast deals
  - Ensure next steps are specific and dated

MONTHLY (RevOps):
  - Run data completeness report
  - Audit sample of 20 closed deals for accuracy
  - Review picklist usage for "Other" accumulation
  - Check for new duplicates

QUARTERLY (RevOps):
  - Full data cleanup sprint
  - Picklist refinement (add common "Other" values, retire unused)
  - Archive stale records (>6 months, no activity, not in active pipeline)
  - Review and update automation rules
```

---

## 3. Reporting - Build Guide

### Report Design Principles

1. **Every report answers one question.** If a report tries to answer three questions, build three reports.
2. **Audience determines format.** Executives get summaries with trend lines. Managers get detail with drill-down. Reps get action-oriented views.
3. **Leading indicators get daily views. Lagging indicators get weekly/monthly views.** Don't check revenue daily or activity monthly.
4. **Comparisons create insight.** Always show: current vs. target, current vs. prior period, current vs. benchmark. Numbers without context are noise.
5. **Automate delivery.** Reports should arrive in inboxes/Slack - not require someone to log in and run them.

### Essential Reports (build these first)

**Report 1: Pipeline Snapshot**
```
PURPOSE: "Do we have enough pipeline to hit our number?"
AUDIENCE: CRO, VP Sales, Sales Managers
CADENCE: Weekly (Monday delivery)

CONTENT:
  - Total pipeline by stage (table + bar chart)
  - Weighted pipeline vs. quarterly target
  - Pipeline coverage ratio (pipeline / target)
  - New pipeline created this week
  - Pipeline lost this week (closed-lost + slipped)
  - Net pipeline change
  
DRILL-DOWN:
  - By rep
  - By segment
  - By source
```

**Report 2: Forecast vs. Actual**
```
PURPOSE: "Are we going to hit this quarter's number?"
AUDIENCE: CRO, VP Sales, CFO
CADENCE: Weekly

CONTENT:
  - Bookings to date vs. quarterly target
  - Committed pipeline (high confidence)
  - Best case pipeline
  - Gap: target - (booked + committed)
  - Trailing forecast accuracy (how accurate have recent forecasts been?)
```

**Report 3: Conversion Funnel**
```
PURPOSE: "Where are deals stalling or dropping?"
AUDIENCE: VP Sales, Sales Managers, RevOps
CADENCE: Monthly

CONTENT:
  - Conversion rate at each stage (table + funnel visualization)
  - Compared to: prior quarter, benchmark
  - Average days in each stage
  - Deals that went backward (stage regression - a warning sign)
  
DRILL-DOWN:
  - By source (inbound vs. outbound vs. PLG)
  - By segment
  - By rep
```

**Report 4: Activity Dashboard**
```
PURPOSE: "Are reps doing enough of the right activities?"
AUDIENCE: SDR Manager, Sales Managers
CADENCE: Daily

CONTENT:
  - Calls, emails, meetings, demos by rep (today, this week, this month)
  - Activity vs. target (% of target achieved)
  - Connection rates (calls that resulted in conversations)
  - Meeting conversion (activities per meeting booked)
```

**Report 5: Source Performance**
```
PURPOSE: "Which pipeline sources are delivering, and which are behind?"
AUDIENCE: CRO, CMO, VP Sales
CADENCE: Monthly

CONTENT:
  - Pipeline created by source (outbound, inbound, PLG, community, partner, AE-sourced)
  - Pipeline vs. target by source
  - Win rate by source
  - Average deal size by source
  - Average cycle length by source
  - Cost per opportunity by source (if data available)
```

---

## 4. Attribution - Implementation Guide

### Building Multi-Touch Attribution

**Step 1: Define touchpoints to track**

| Touchpoint Category | Examples | Tracking Method |
|---------------------|----------|-----------------|
| Marketing campaigns | Ads, content syndication, events | UTM parameters + CRM campaign membership |
| Content engagement | Blog, whitepaper, case study, video | Marketing automation + CRM |
| Website activity | Page visits, pricing page, demo request | Web analytics + CRM (IP-to-account for ABM) |
| SDR outreach | Emails, calls, LinkedIn | Sequence tool + CRM activity |
| Events | Webinar, conference, dinner, workshop | Registration + attendance tracking |
| Community/product | Community engagement, product usage | Product telemetry + community platform |
| Partner | Referral, co-sell introduction | CRM partner field + partner portal |

**Step 2: Build the tracking infrastructure**

```
EVERY TOUCHPOINT MUST HAVE:
  - Timestamp (when did it happen?)
  - Contact (who was touched?)
  - Account (which company - for account-level roll-up)
  - Channel (what broad category - paid, organic, outbound, community)
  - Source detail (what specific campaign, content, or sequence)
  - Type (first touch, lead creation, opp creation, acceleration, close)

TECHNICAL IMPLEMENTATION:
  - UTM parameters on all marketing URLs (mandatory, no exceptions)
  - CRM campaign membership for all marketing activities
  - Activity logging for all sales touches (automated via sequence tool)
  - Web-to-lead tracking with source capture
  - Product/community data pipeline to CRM (API integration)
```

**Step 3: Choose and implement the model**

For most scale-stage B2B companies, start with the weighted multi-touch model:

```
First touch: 20% credit
  (What first brought this account to our awareness?)

Lead creation: 20% credit
  (What converted anonymous → known contact?)

Opportunity creation: 30% credit
  (What triggered the commercial conversation?)

Acceleration touches: 20% credit (split among all)
  (What kept the deal moving between opp creation and close?)

Close touch: 10% credit
  (What was the final interaction before closed-won?)
```

**Step 4: Report and iterate**

- Run attribution reports monthly
- Compare results across models (does changing the weights change the story?)
- Validate with qualitative data (do the attribution results match what reps and customers say?)
- Adjust weights annually based on what you learn

### Attribution Pitfalls

| Pitfall | Problem | Fix |
|---------|---------|-----|
| UTM gaps | 30%+ of touchpoints have no tracking | Audit UTM coverage monthly; make UTMs mandatory in campaign launch checklist |
| Self-reported source only | "How did you hear about us?" is unreliable | Use self-reported as a supplement, not primary attribution |
| Last-touch bias | Sales always gets credit; marketing undervalued | Use multi-touch model; report both first and last touch alongside |
| Dark funnel ignored | Community, word-of-mouth, content not captured | Add "community" and "organic/WOM" as source options; accept some will be uncapturable |
| Over-engineering | Building perfect attribution before building basic tracking | Start with first + last touch; upgrade to multi-touch after 2 quarters of clean data |

---

## 5. Forecasting - Detailed Methodology

### The Three Forecast Methods (use all three and compare)

**Method 1: Bottom-Up (Rep Calls)**
```
Each rep submits their forecast:
  Committed: Deals they believe will close this period
  Best case: Deals likely to close with some risk
  Upside: Deals that could close if everything goes right

Manager adjusts based on:
  - Historical accuracy of each rep (some over-forecast, some under)
  - Deal-level inspection (does the forecast match deal reality?)
  - Pattern recognition (deals that "look" like winners vs. at-risk)

Roll up: Sum of adjusted rep forecasts = bottom-up forecast
```

**Method 2: Pipeline Coverage (Mathematical)**
```
Forecast = Current pipeline × historical close rate by stage

For each stage:
  (# deals in stage × avg deal size × historical conversion rate from that stage to close)
  Sum across all stages = mathematical forecast

Adjust for:
  - Seasonality (Q4 typically higher, Q1 lower)
  - Pipeline age (older pipeline converts at lower rates)
  - Source mix (PLG converts differently than outbound)
```

**Method 3: Trend Extrapolation (Historical Pattern)**
```
Look at trailing 4-quarter pattern:
  Quarter-over-quarter growth rate
  Month-within-quarter booking pattern (what % closes in M1, M2, M3?)
  Apply pattern to current quarter's progress

This method catches systemic biases that bottom-up and coverage miss.
```

### Forecast Accuracy Measurement

```
FORECAST ACCURACY = 1 - |Forecast - Actual| / Actual

Track at:
  - Company level: Overall forecast accuracy
  - Rep level: Individual forecast accuracy (identifies coaching needs)
  - Stage level: Which stages are hardest to predict?
  - Source level: Are certain pipeline sources less predictable?

BENCHMARKS:
  - Committed accuracy: >85% (if <85%, committed criteria are too loose)
  - Best case accuracy: 50-70%
  - Overall forecast accuracy: >80% is excellent, >70% is acceptable
  - If accuracy is <60%, the problem is usually data quality or stage definitions
```

---

## 6. Tech Stack Evaluation Process

### Evaluation Workflow

```
STEP 1: DEFINE THE PROBLEM (before looking at tools)
  - What specific workflow or process needs improvement?
  - What does the team do today, and where does it break?
  - What would success look like with the right tool?
  - What's the cost of NOT solving this? (justifies budget)

STEP 2: REQUIREMENTS DOCUMENT
  Must-have requirements: (deal-breakers if missing)
  Should-have requirements: (strongly preferred)
  Nice-to-have requirements: (bonus but not critical)
  Integration requirements: (what must it connect to?)
  Budget constraints: (total and per-user)

STEP 3: SHORTLIST (3-5 tools max)
  Research: G2, TrustRadius, peer recommendations, analyst reports
  Filter by: must-have requirements + budget + integration compatibility
  Verify: vendor stability (funding, team size, customer count)

STEP 4: EVALUATE
  - Product demo with YOUR use cases (not their standard demo)
  - Trial/pilot with real users (2-4 weeks minimum)
  - Reference calls with 2-3 customers similar to your profile
  - Security and compliance review
  - Integration test with your CRM

STEP 5: DECIDE
  Score each tool against requirements (1-5 per requirement, weighted)
  Factor in: total cost of ownership (license + implementation + training + maintenance)
  Factor in: adoption risk (will the team actually use this?)

STEP 6: IMPLEMENT
  - Designate an internal owner (not the vendor's CSM)
  - Define success metrics before go-live
  - Phased rollout: pilot team → full team
  - Training: initial + 30-day reinforcement
  - 90-day review: is it delivering value?
```

### Build vs. Buy Decision Framework

| Factor | Build (internal) | Buy (vendor) |
|--------|-----------------|--------------|
| Core competency | Is this central to your business? | Is this a commodity function? |
| Maintenance | Can you maintain it long-term? | Vendor handles updates and bugs |
| Speed | Months to build | Days/weeks to implement |
| Customization | Exactly what you need | 80% fit with workarounds |
| Cost | Engineering time (expensive) | License fees (predictable) |
| Risk | Dependency on internal team | Dependency on vendor viability |

**Default to buy** unless the workflow is core to your competitive advantage or no vendor adequately serves your need. Engineering time spent building internal tools is engineering time not spent on the product.

---

## 7. KPI Benchmarks by Stage

### Revenue Efficiency Metrics

| Metric | Seed (<$5M) | Growth ($5-20M) | Scale ($20M+) |
|--------|-------------|-----------------|----------------|
| CAC payback (months) | 18-24 | 12-18 | 6-12 |
| LTV:CAC ratio | 2-3x | 3-4x | 4-6x |
| Magic number | 0.3-0.5 | 0.5-0.8 | 0.8-1.2+ |
| Net revenue retention | 90-110% | 100-120% | 110-130%+ |
| Gross margin | 60-70% | 70-80% | 75-85%+ |

### Sales Productivity Metrics

| Metric | Target | Warning | Critical |
|--------|--------|---------|----------|
| AE quota attainment (avg) | >80% | 60-80% | <60% |
| AE ramp time (to full quota) | <6 months | 6-9 months | >9 months |
| Pipeline coverage ratio | 3-4x | 2-3x | <2x |
| Forecast accuracy | >80% | 70-80% | <70% |
| Win rate (all deals) | >25% | 15-25% | <15% |
| Average sales cycle | Stable/decreasing | Increasing | >50% above benchmark |
| Average deal size | Stable/increasing | Decreasing | >20% below target |

### Marketing Efficiency Metrics

| Metric | Target | Notes |
|--------|--------|-------|
| Marketing-sourced pipeline % | 30-50% | Varies by GTM model |
| MQL-to-SQL conversion | 15-25% | If <10%, MQL definition is too loose |
| SQL-to-opportunity conversion | 40-60% | If <30%, qualification criteria misaligned |
| Cost per MQL | Varies by channel | Track trend, not absolute |
| Cost per SQL | $500-2,000 (enterprise) | Depends heavily on ACV |
| Cost per opportunity | $1,000-5,000 (enterprise) | Benchmark against ACV |

---

## 8. CRM Audit Methodology

### Quick CRM Health Check (run in 2 hours)

```
CHECK 1: PIPELINE REALITY
  Pull all open opportunities. Answer:
  - How many have close dates in the past? (Target: <10%)
  - How many have no activity in 30+ days? (Target: <15%)
  - How many have no next step defined? (Target: <5%)
  - How many are in Stage 1 for 60+ days? (Target: <10%)

CHECK 2: DATA COMPLETENESS
  Pull all opportunities created in the last quarter. Answer:
  - What % have source populated? (Target: >95%)
  - What % have competitor populated (for Stage 2+)? (Target: >80%)
  - What % have champion and EB identified (for Stage 3+)? (Target: >90%)
  - What % have win/loss reason on closed deals? (Target: >95%)

CHECK 3: PROCESS ADHERENCE
  Pull stage change history. Answer:
  - Are deals skipping stages? (Target: <5%)
  - Are deals going backward frequently? (If >10%, stage definitions are unclear)
  - Average time in each stage (compare to benchmarks)

CHECK 4: ACTIVITY LOGGING
  Pull activity data for the last month. Answer:
  - What % of reps log >10 activities/week? (Target: >90%)
  - What % of open opportunities have activity this week? (Target: >80%)
  - Are calls/emails being logged automatically via tools? (Should be)

CHECK 5: DUPLICATES
  Run dedup report on accounts and contacts. Answer:
  - Duplicate rate (Target: <2%)
  - Major merge candidates (same company, different names)
```

### Full CRM Audit (run quarterly, takes 1-2 days)

Extend the quick check with:
- Custom field audit (which fields are populated, which are dead)
- Automation audit (which workflows are running, are they still relevant)
- Integration audit (what's connected, is data flowing correctly)
- User adoption audit (who's logging in, who's not using the system)
- Permission audit (who has access to what)

---

## 9. RevOps Team Design

### Hiring Sequence

| Hire # | Role | When | Focus |
|--------|------|------|-------|
| 1 | RevOps Generalist | $3-5M ARR | CRM admin, basic reporting, process design |
| 2 | RevOps Analyst | $8-15M ARR | Advanced reporting, attribution, forecasting |
| 3 | Sales Operations | $15-25M ARR | Deal desk, comp plans, territory design |
| 4 | Marketing Operations | $15-25M ARR | Marketing automation, lead management, attribution |
| 5 | RevOps Manager | $25M+ ARR | Team leadership, strategy, cross-functional alignment |

### RevOps Generalist Job Profile (First Hire)

```
MUST HAVE:
  - CRM administration (Salesforce or HubSpot)
  - Report building and data analysis
  - Process design and documentation
  - Basic workflow automation
  - Communication skills (works across sales, marketing, CS)

NICE TO HAVE:
  - SQL or data analysis tools
  - Marketing automation experience
  - Sales methodology familiarity
  - Project management skills

RED FLAGS:
  - Pure CRM admin with no business context
  - Data analyst who doesn't understand sales process
  - Wants to rebuild everything from scratch before understanding what exists
```

---

## 10. Process Automation Patterns

### High-Value Automations (build these first)

| Automation | Trigger | Action | Impact |
|-----------|---------|--------|--------|
| Lead routing | New lead created | Score → assign → notify owner | Reduces response time |
| Stage change notifications | Opportunity moves stage | Notify manager + update timestamps | Visibility + data quality |
| Stale deal alerts | No activity in 14 days | Slack/email to AE + manager | Pipeline hygiene |
| Close date push alerts | Close date passes without close | Flag for manager review | Forecast accuracy |
| Win/loss required fields | Opportunity closed | Require reason before saving | Data completeness |
| Activity logging | Call/email completed | Auto-log in CRM via tool integration | Rep time savings |
| PQL routing | PQL score exceeds threshold | Create task for SDR + provide context | Signal response time |
| Renewal alerts | 90 days before contract end | Notify CS + AE | Retention |

### Automation Anti-Patterns

- **Over-automation early**: Don't automate a broken process - fix the process first
- **Alert fatigue**: Too many automated notifications = all get ignored. Start with 3-5 critical alerts.
- **Black box routing**: If no one understands how leads are routed, the system will be bypassed
- **No feedback loop**: Automations should be reviewed quarterly - are they still relevant?
