revops

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.

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

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.

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.

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.

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

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)

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

• 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

• 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

Response time is the single biggest factor in lead conversion:

• Contact within 5 minutes = 21x more likely to qualify (Lead Connect)
• After 30 minutes, conversion drops by 10x
• 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

• 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

• Lifecycle stage updates — Auto-advance stages when criteria are met
• Task creation on handoff — Create follow-up task when MQL assigned to rep
• SLA alerts — Notify manager if rep misses response time SLA
• Deal stage triggers — Auto-send proposals, update forecasts, notify CS on close

• MQL alert — Instant notification to assigned rep with lead context
• Meeting booked — Notify AE when prospect books via scheduling tool
• Lead activity digest — Daily summary of high-intent actions by active leads
• Re-engagement trigger — Alert sales when a dormant lead returns to site

• Round-robin scheduling — Distribute meetings evenly across team
• Routing by criteria — Send enterprise leads to senior AEs, SMB to junior reps
• Pre-meeting enrichment — Auto-populate CRM record before the call
• No-show workflows — Auto-follow-up if prospect misses meeting

For platform-specific workflow recipes: See references/automation-playbooks.md

• 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

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

• 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

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

• 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

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