Most revenue operations teams struggle to prove their value.

They build dashboards, fix data issues, optimize processes. All important work. But when leadership asks the hard question (“What did you actually do for revenue?”) they’re stuck pointing to activity instead of outcomes.

Scott Sutton, CEO of Later and former VP of RevOps at ZoomInfo, has a different take. Sitting down with us on the GTM Science Podcast, he argues that RevOps has a unique advantage that most teams aren’t leveraging: it’s inherently measurable. Unlike other support functions, every process change, every workflow improvement, every system optimization can be directly tied to revenue outcomes.

The difference between teams that prove impact and teams that don’t?

Instrumentation.

“The beauty of RevOps is it’s very measurable,” Scott explains. But that measurability doesn’t happen by accident. It requires going in with a plan, modeling changes like a financial analyst, and tracking outcomes with the same rigor we apply to pipeline management.

In this newsletter, we’re breaking down Scott’s framework for proving RevOps impact, from the foundation of instrumentation to the systemic thinking that enables career-defining process changes.

You can listen to the full podcast episode on Spotify here or on Apple Podcasts here.

The Foundation: Instrument First, Prove Later

Before we can prove anything, we need data we can trust.

Scott doesn’t mince words here. “It doesn’t happen if you don’t have the instrumentation,” he says. Without proper tracking, we’re flying blind. We can’t measure what’s working, what’s not, or whether our changes made an impact.

The first priority is instrumenting the entire process. Leads to meetings. Meetings to completion or outcome. Opportunity creation to outcome. All the intermediate metrics in between. Set baselines, build functional models, then tweak and tune.

But Scott warns against overcomplicating this early. “Start with the definitive items,” he says. “Did we close the deal or not? That’s pretty black and white.” Complex multi-touch attribution and advanced channel tracking can come later, but only after we’re confident in the basics.

The other piece that separates effective RevOps teams from the rest is enforcement. Data quality doesn’t maintain itself. Scott’s approach at ZoomInfo was strict but fair: if a rep didn’t create an opportunity after completing a meeting successfully and confirming it was a good fit, they wouldn’t get another demo for a week. Two violations meant no demos for a month. And if opportunities weren’t in the system, reps wouldn’t get compensated for them.

“I don’t view it as highly punitive,” Scott says. “I do it as, like, this is the minimum expectation of your job that you have to go do to give our team line of sight.”

That line of sight is what makes everything else possible. Without it, we’re building process improvements on top of unreliable data. With it, we can start to model changes, predict outcomes, and prove impact with confidence.

How to Model and Prove Impact Before Making Changes

Once instrumentation is in place, the real leverage comes from systemic thinking: seeing how changes cascade through the funnel and predicting their downstream effects.

The best example comes from Scott’s time at ZoomInfo, where he made a potentially career-ending decision.

They cut 2/3rds of all leads.

ZoomInfo was processing around 6,000 leads per month. Scott’s team stopped calling back 4,000 of them. “That was kind of a wild decision because it would seem like that could be career suicide if results didn’t manifest,” Scott recalls.

But the decision was made on data. The analysis showed those leads had low win rates. By cutting them, the team could reallocate time to higher-quality opportunities. The model predicted that increased yield on remaining leads would offset the volume drop.

Scott explains the cascade: “If I increase the lead amount, often I do that by loosening the reins on my filtering. So then my overall lead quality goes down. I have a fixed amount of BDRs, they now have more leads to work. Because of the relative fewer minutes they can spend on each lead and then the reduced lead quality, our overall conversion rate’s gonna dip.”

The result? Meeting quality went up. ASP went up. Overall yield per rep went up. It worked so well they did it again.

“If you go in with a plan, you model it out, almost like a financial analyst, and then are able to see it through execution, you can start to see those early indicators of success. And ultimately the end yield of the outcome of that process change is very measurable,” Scott says. “But it doesn’t happen if you don’t have the instrumentation.”

When Measurement Becomes Critical: The Scaling Inflection Point

Scott identifies multiple managers as a critical breaking point when companies scale.

One or two leaders? Inconsistencies are manageable. Multiple managers running their own versions of the process? The system breaks down. Different qualification standards mean inflated pipelines, skewed win rates, and no way to tell what’s actually working.

“I think as soon as you get to having a manager layer you really gotta start to dial in some of those consistency factors in process and in metrics,” Scott explains.

Here’s the insight: it matters less what the definition is. It’s just that you have one.

That consistency lets us compare performance, identify what works, and prove impact at scale.

The Over-Engineering Trap

There’s a trap that catches even the best RevOps teams: measuring complexity they can’t action.

Scott admits he’s been guilty of this himself. The temptation is to build sophisticated attribution models, automate every edge case, and instrument every possible data point. But often, that complexity doesn’t actually help us make better decisions.

He offers two threshold questions that help determine whether added complexity is worth it:

Is there sufficient volume to offset the effort?

And…

Can we measure the high-level metric before breaking into sub-components?

The unfortunate pattern we often see? Teams that can’t figure out how many leads they had yesterday, trying to build 18-level multi-touch attribution.

The principle: nail the basics, then earn your way to complexity.

“We run what is a pretty simple GTM attribution and tracking. I just need some basic SAOs to make sure things are working,” Scott says.

With every addition, he asks: “What is the point? Why are we doing it? How will it actually help us?”

What This Means for You

If you’re a RevOps leader:

Start with instrumentation you can trust, enforce data hygiene with real consequences, and model changes before implementing them. Your superpower is measurability, but only if you’re measuring the right things.

If you’re a CRO:

Your RevOps team should tell you exactly what they delivered for revenue last quarter. Set the bar: every process change needs a model, early indicators, and measurable outcomes.

If you’re scaling past 10-15 reps:

Consistent definitions and processes are mandatory. Get alignment on what counts, how opportunities move, and what success looks like. Then enforce it.

Scott’s experience cutting 2/3rds of leads at ZoomInfo is the proof point. A decision that looked risky became defensible, repeatable, and career-defining because it was built on data, modeled in advance, and tracked through execution.

That’s the difference between RevOps teams that earn a seat at the table and teams that spend their time justifying their existence.

Are you building the instrumentation needed to prove your impact?

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