Suede Revenue Operations

Suede RevOps maps the lifecycle rules, data contracts, scoring, routing, and handoffs that connect acquisition, sales, and customer success. It produces implementation-ready operating logic while keeping production CRM mutation behind live-schema review and approval.

Before Starting

Check for product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Gather this context (ask if not provided):

1. GTM motion: Product-led (PLG), sales-led, or hybrid?
2. ACV range: What's the average contract value?
3. Sales cycle length: Days from first touch to closed-won?
4. Current stack: CRM, marketing automation, scheduling, enrichment tools?
5. Current state: How are leads managed today? What's working and what's not?
6. Goals: Increase conversion? Reduce speed-to-lead? Fix handoff leaks? Build from scratch?

Work with whatever the user gives you. If they have a clear problem area, start there. Don't block on missing inputs: use what you have and note what would strengthen the solution.

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Core Principles

Single Source of Truth

One system of record for every lead and account. If data lives in multiple places, it will conflict. Pick a CRM as the canonical source and sync everything to it.

Define Before Automate

Get stage definitions, scoring criteria, and routing rules right on paper before building workflows. Automating a broken process just creates broken results faster.

Measure Every Handoff

Every handoff between teams is a potential leak. Marketing-to-sales, SDR-to-AE, AE-to-CS: each needs an SLA, a tracking mechanism, and someone accountable for follow-through.

Revenue Team Alignment

Marketing, sales, and customer success must agree on definitions. If marketing calls something an MQL but sales won't work it, the definition is wrong. Alignment meetings aren't optional.

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Lead Lifecycle Framework

Stage Definitions

Stage	Entry Criteria	Exit Criteria	Owner
Subscriber	Opts in to content (blog, newsletter)	Provides company info or shows engagement	Marketing
Lead	Identified contact with basic info	Meets minimum fit criteria	Marketing
MQL	Passes fit + engagement threshold	Sales accepts or rejects within SLA	Marketing
SQL	Sales accepts and qualifies via conversation	Opportunity created or recycled	Sales (SDR/AE)
Opportunity	Budget, authority, need, timeline confirmed	Closed-won or closed-lost	Sales (AE)
Customer	Closed-won deal	Expands, renews, or churns	CS / Account Mgmt
Evangelist	High NPS, referral activity, case study	Ongoing program participation	CS / Marketing

MQL Definition

An MQL requires both fit and engagement:

• Fit score: Does this person match your ICP? (company size, industry, role, tech stack)
• Engagement score: Have they shown buying intent? (pricing page, demo request, multiple visits)

Neither alone is sufficient. A perfect-fit company that never engages isn't an MQL. A student downloading every ebook isn't an MQL.

MQL-to-SQL Handoff SLA

Define response times and document them:

• MQL alert sent to assigned rep
• Rep contacts within 4 hours (business hours)
• Rep qualifies or rejects within 48 hours
• Rejected MQLs go to recycling nurture with reason code

For complete lifecycle stage templates and SLA examples: See references/lifecycle-definitions.md

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Lead Scoring

Scoring Dimensions

Explicit scoring (fit): Who they are:

• Company size, industry, revenue
• Job title, seniority, department
• Tech stack, geography

Implicit scoring (engagement): What they do:

• Page visits (especially pricing, demo, case studies)
• Content downloads, webinar attendance
• Email engagement (opens, clicks)
• Product usage (for PLG)

Negative scoring: Disqualifying signals:

• Competitor email domains
• Student/personal email
• Unsubscribes, spam complaints
• Job title mismatches (intern, student)

Building a Scoring Model

1. Define your ICP attributes and weight them
2. Identify high-intent behavioral signals from closed-won data
3. Set point values for each attribute and behavior
4. Set MQL threshold (typically 50-80 points on a 100-point scale)
5. Test against historical data: does the model correctly identify past wins?
6. Launch, measure, and recalibrate quarterly

Common Scoring Mistakes

• Weighting content downloads too heavily (research ≠ buying intent)
• Not including negative scoring (lets bad leads through)
• Setting and forgetting (buyer behavior changes; recalibrate quarterly)
• Scoring all page visits equally (pricing page ≠ blog post)

For detailed scoring templates and example models: See references/scoring-models.md

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Lead Routing

Routing Methods

Method	How It Works	Best For
Round-robin	Distribute evenly across reps	Equal territories, similar deal sizes
Territory-based	Assign by geography, vertical, or segment	Regional teams, industry specialists
Account-based	Named accounts go to named reps	ABM motions, strategic accounts
Skill-based	Route by deal complexity, product line, or language	Diverse product lines, global teams

Routing Rules Essentials

• Route to the most specific match first, then fall back to general
• Include a fallback owner: unassigned leads go cold fast and waste pipeline
• Round-robin should account for rep capacity and availability (PTO, quota attainment)
• Log every routing decision for audit and optimization

Speed-to-Lead

Response time is one of the largest controllable factors in lead conversion. Widely cited vendor studies (Lead Connect, InsideSales) report order-of-magnitude differences; treat the ordering as reliable and the multipliers as unverified until measured on your own data:

• Contact within 5 minutes: the target; qualification rates fall steeply after it
• After 30 minutes, conversion drops sharply
• After 24 hours, the lead is effectively cold

Build routing rules that prioritize speed. Alert reps immediately. Escalate if SLA is missed.

For routing decision trees and platform-specific setup: See references/routing-rules.md

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Pipeline Stage Management

Pipeline Stages

Stage	Required Fields	Exit Criteria
Qualified	Contact info, company, source, fit score	Discovery call scheduled
Discovery	Pain points, current solution, timeline	Needs confirmed, demo scheduled
Demo/Evaluation	Technical requirements, decision makers	Positive evaluation, proposal requested
Proposal	Pricing, terms, stakeholder map	Proposal delivered and reviewed
Negotiation	Redlines, approval chain, close date	Terms agreed, contract sent
Closed Won	Signed contract, payment terms	Handoff to CS complete
Closed Lost	Loss reason, competitor (if any)	Post-mortem logged

Stage Hygiene

• Required fields per stage: Don't let reps advance a deal without filling in required data
• Stale deal alerts: Flag deals that sit in a stage beyond the average time (e.g., 2x average days)
• Stage skip detection: Alert when deals jump stages (Qualified → Proposal skipping Discovery)
• Close date discipline: Push dates must include a reason; no silent pushes

Pipeline Metrics

Metric	What It Tells You
Stage conversion rates	Where deals die
Average time in stage	Where deals stall
Pipeline velocity	Revenue per day through the funnel
Coverage ratio	Pipeline value vs. quota (target 3-4x)
Win rate by source	Which channels produce real revenue

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CRM Automation Workflows

Platform-agnostic build list: the set to design before the stack is decided, in build order:

1. MQL alert + assignment: instant rep notification with lead context
2. SLA escalation: notify the manager when response time is missed
3. Score-driven stage promotion: advance lifecycle stage when criteria are met
4. Meeting booked / no-show: notify the AE; auto-follow-up on a miss
5. Closed-won handoff: create the CS task and update the forecast
6. Stale deal alert: flag a deal past 2x average days in stage
7. Recycled-lead nurture: return disqualified leads to marketing with a reason code
8. Activity digest + re-engagement: daily high-intent summary; alert on a dormant lead's return

For the trigger and action definitions per platform: HubSpot workflows, Salesforce Flow equivalents, Calendly/SavvyCal, and Zapier patterns, including round-robin, routing by criteria, and pre-meeting enrichment: See references/automation-playbooks.md. Read it once the CRM is known; build from the list above when it is not.

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Deal Desk Processes

When You Need a Deal Desk

• ACV above $25K (or your threshold for non-standard deals)
• Non-standard payment terms (net-90, quarterly billing)
• Multi-year contracts with custom pricing
• Volume discounts beyond published tiers
• Custom legal terms or SLAs

Approval Workflow Tiers

Deal Size	Approval Required
Standard pricing	Auto-approved
10-20% discount	Sales manager
20-40% discount	VP Sales
40%+ discount or custom terms	Deal desk review
Multi-year / enterprise	Finance + Legal

Non-Standard Terms Handling

Document every exception. Track which non-standard terms get requested most: if everyone asks for the same exception, it should become standard. Review quarterly.

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Data Hygiene & Enrichment

Dedup Strategy

• Matching rules: Email domain + company name + phone as primary match keys
• Merge priority: CRM record wins over marketing automation; most recent activity wins for fields
• Scheduled dedup: Run weekly automated dedup with manual review for edge cases

Required Fields Enforcement

• Enforce required fields at each lifecycle stage
• Block stage advancement if fields are empty
• Use progressive profiling: don't require everything upfront

Enrichment vendors are listed under Tool Integrations below, with the provenance, freshness, lawful-basis, and credit-cost checks that apply before proposing any of them.

Quarterly Audit Checklist

• Review and merge duplicates
• Validate email deliverability on stale contacts
• Archive contacts with no activity in 12+ months
• Audit lifecycle stage distribution (look for bottlenecks)
• Verify enrichment data accuracy on a sample set

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RevOps Metrics Dashboard

Key Metrics

Metric	Formula / Definition	Benchmark
Lead-to-MQL rate	MQLs / Total leads	5-15%
MQL-to-SQL rate	SQLs / MQLs	30-50%
SQL-to-Opportunity	Opportunities / SQLs	50-70%
Pipeline velocity	(# deals x avg deal size x win rate) / avg sales cycle	Varies by ACV
CAC	Total sales + marketing spend / new customers	LTV:CAC > 3:1
LTV:CAC ratio	Customer lifetime value / CAC	3:1 to 5:1 healthy
Speed-to-lead	Time from form fill to first rep contact	< 5 minutes ideal
Win rate	Closed-won / total opportunities	20-30% (varies)

Dashboard Structure

Build three views:

1. Marketing view: Lead volume, MQL rate, source attribution, cost per MQL
2. Sales view: Pipeline value, stage conversion, velocity, forecast accuracy
3. Executive view: CAC, LTV:CAC, revenue vs. target, pipeline coverage

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Output Format

When delivering RevOps recommendations, provide:

1. Lifecycle stage document: Use the stage template shape in references/lifecycle-definitions.md for each stage: Entry criteria / Exit criteria / Owner / Actions on entry / SLA / Required fields / Recycle path. Do not invent a different shape.
2. Scoring specification: Fit and engagement attributes with point values and MQL threshold
3. Routing rules document: Decision tree with assignment logic and fallbacks
4. Pipeline configuration: Stage definitions, required fields, and automation triggers
5. Metrics dashboard spec: Key metrics, data sources, and target benchmarks

Format each as a standalone document the user can implement directly. Include platform-specific guidance when the CRM is known.

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Tool Integrations

These are evaluation examples, not guaranteed integrations. Verify the live schema, authenticated account, permissions, API limits, pricing, and vendor documentation before proposing implementation.

Tool Category	Examples	Verify Before Use
CRM	HubSpot, Salesforce	Objects, fields, ownership, automation, permissions
Scheduling	Calendly, SavvyCal	Routing logic, availability, privacy
Enrichment	Clearbit, Apollo	Provenance, freshness, lawful basis, credit cost
Automation	ActiveCampaign, Zapier	Trigger scope, retries, rollback, secrets
Partner operations	Introw, Crossbeam	Account matching, deal rules, commissions

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Boundaries

• Do not change production CRM fields, routing, automations, permissions, SLAs, ownership, or records without reading the live schema, previewing the exact effect, and receiving explicit approval.
• Do not delete, merge, deduplicate, enrich, or reassign records as a planning step, and do not expose personal data in reports.
• Do not redefine lifecycle stages or claim pipeline impact from modeled data; document assumptions and the stakeholders who must approve the operating rule.

Routing

• Use suede-prospecting to build and qualify the source list.
• Use suede-cold-email for approved outbound sequence copy.
• Use suede-sales-enablement for collateral and rep-facing materials.
• Use suede-analytics for pipeline measurement and attribution.
• Use suede-pricing when deal-desk work turns into a pricing decision: discount tiers that keep getting overridden, a non-standard term everyone requests, or list-price and packaging changes.