Your RevOps is probably broken. Here are 12 specific questions that tell you where — and in what order to fix it.
You don't know what's wrong with your revenue operations. You know something is — the CRM is a mess, the forecast is a guess, and nobody trusts the data. But you can't put your finger on exactly what to fix first.
So here's a diagnostic you can run right now: 12 questions, each one pointing to a specific breakdown that costs real money. Work through all 12 and you'll have a complete map of where your revenue engine is bleeding.
Run this test: pull 10 random deals from your pipeline and compare what your CRM says against the rep's last email or Slack thread about that deal. How many match?
If more than 2 are wrong, your CRM is a suggestion box, not a source of truth. Reps are working around the system because the system doesn't reflect reality. Every forecast, every report, every board presentation is built on a foundation nobody trusts.
What good looks like: 90%+ of open deals have activity in the last 7 days. Stage progression is criteria-driven. Contact and company records are deduplicated. No deal has a "next step" older than today.
If you're running last-click attribution in 2026, you're making decisions about where to spend marketing dollars based on a model that systematically lies to you. B2B buyer journeys average 6–8 touchpoints. Last-click credits the channel that happened to be present at close — not the channel that built the conviction to buy.
Ask: if your attribution model told you to cut organic content entirely, would you have enough confidence in it to actually do it? If the answer is no, the model is broken.
What good looks like: Multi-touch or U-shaped attribution. Marketing and sales agree on which channels actually close deals. Budget allocation is based on actual revenue influence, not last-touch credit.
Ask your VP of Sales right now. If they need to open a spreadsheet, scroll through their CRM, or say "let me get back to you," you have a pipeline visibility problem. Not a data problem — a systems problem. The data exists, but it's not structured for answers.
What good looks like: Real-time pipeline view with confidence scores, days-in-stage tracking, and at-risk flags. Any leadership meeting runs on CRM data without supplemental spreadsheets.
Does your CRM have a lead score field? Are the rules written down? Are they enforced automatically, or does a rep decide manually which leads to call?
If your answer involves "rep judgment" as a workflow step, you don't have lead scoring. You have a gut-feel queue that nobody can measure.
What good looks like: Automated lead score combining firmographic fit (company size, industry, revenue) and behavioral signals (email engagement, pricing page visits, content downloads). High-score leads trigger immediate outreach. Low-score leads enter a nurture sequence.
Sales and marketing both say "qualified lead." Do they mean the same thing? Is it written down? Is it measurable?
Most companies have an implicit definition that lives in someone's head. When marketing sends 50 MQLs and sales says 40 are garbage, nobody can resolve the argument because there was never a shared definition to begin with.
What good looks like: One document, agreed upon by both sales and marketing, specifying exactly what makes a lead sales-qualified. Clear enough that any rep can determine in 30 seconds whether a given lead qualifies.
Not your overall close rate. Your close rate broken down by: inbound vs. outbound, by segment (SMB vs. mid-market vs. enterprise), by ICP profile. If you're averaging 15% across everything, you don't know where you're winning or losing — and you can't replicate the wins.
What good looks like: Historical close rates by segment stored and updated quarterly. Marketing optimizes for the segments that actually close. Sales has clear expectations by deal type.
How does a deal move from "working" to "closing this month"? Is it a gut call? A manager approval? An automated probability based on historical data?
If your forecast is "what the VP of Sales says it is," you don't have a forecast — you have an opinion.
What good looks like: Stage-by-stage historical win rates drive forecast probabilities. Confidence scores update automatically based on deal activity and stage duration. The board can see the math, not just the number.
Marketing is measured on MQL volume. Sales is measured on closed ARR. When those are misaligned, you get the attribution war: sales says marketing leads are garbage, marketing says sales doesn't follow up fast enough.
Neither is wrong. They're optimizing for different things because nobody aligned their incentives.
What good looks like: Shared revenue goal. Marketing measured on SQL quality (not volume). Sales measured on close rate from SQLs. Both measured on pipeline velocity. Same outcome, same team.
When does a deal move from Demo to Proposal? From Proposal to Negotiation? Is there a specific activity, event, or signal that triggers the move — or does the rep decide when it "feels ready"?
Subjective stage progression is the root cause of every pipeline accuracy problem. If a rep can move a deal to Closed Won whenever they want, your pipeline is a list of hopes, not a forecast.
What good looks like: Each stage has a criteria checklist: required activities, required fields, required stakeholder contact. No criteria met = no stage progression. Automated alerts flag deals where stage duration exceeds historical norms.
This is the one nobody wants to audit. When your AEs earn the same commission on a $20K SMB deal and a $200K enterprise deal, they're going to stack the pipeline with $20K deals — which are cheap to close but expensive to maintain, and they kill your net revenue retention.
What good looks like: Compensation weighted by deal size, deal type, and revenue quality. AEs are incentivized to close the right deals, not just more deals. SPIFFs align team behavior with quarterly revenue priorities.
Your average B2B SaaS company pays for 110+ SaaS tools. Thirty percent are redundant. You're probably one of them.
Redundant tools create integration gaps, data inconsistency, and a reporting layer that nobody trusts because it's stitched together from five different sources that don't agree.
What good looks like: One CRM as the system of record. All other tools either integrate with it or are eliminated. Data flows automatically. No manual re-entry. No Zapier crutches for tools that don't natively connect.
Revenue operations is not a reporting function. If your RevOps person is building dashboards and running reports, they're doing sales ops, not RevOps. Real RevOps sits in the room where revenue strategy is decided, owns the data infrastructure that strategy runs on, and has the authority to push back when a request would break the system.
What good looks like: RevOps leads the weekly pipeline review, drives the forecasting methodology, owns the CRM data model, and escalates when data quality degrades. They're not in the org chart — they're in the decisions.
Most companies will find 4–6 failing checkpoints. That's normal. What matters is the sequence. Some breakdowns are foundational — fix them first and everything else improves. Others are symptoms that clear up once the root cause is addressed.
The three that matter most in order:
Fix those three and the rest become obvious. The 12-point audit tells you exactly where you stand. We run this diagnostic in 45 minutes on a working session — and at the end you have a priority list, not a consultant's report.
Want us to run the audit with you? Book a free RevOps audit. We'll go through all 12 checkpoints together and give you a written priority list in the same session.
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