"How in the hell did you figure it out?": What I Learned from a RevOps Manager This Week
"I've tried to build something similar very ineffectively, you know, with limited AI knowledge. And you know, even trying to leverage automation tools, pulling in Google Sheets data connected to CRM data, and just cannot for the life of me... When you pinged me, I thought, How in the hell did this guy figure it out?"
That raw frustration in his voice perfectly captured what we're seeing across hundreds of conversations with revenue operations professionals.
Here's someone who clearly knows what he needs, has the technical curiosity to try building it himself, and yet finds himself stuck in the same data purgatory that's plaguing companies everywhere.
The Clean Data Paradox
What struck me most about this conversation wasn't just his frustration—it was his brutal honesty about data hygiene. He laid out the reality most companies face but rarely admit:
"Our marketing automation is probably not at a space where our data hygiene is clean enough for a solution like this yet. Our Salesforce hygiene is pretty clean. Our marketing ops data is not so much."
This is the dirty secret of B2B revenue operations: you can have world-class CRM hygiene, but if your marketing data is messy, you're still flying blind on the questions that matter most. Questions like "which channels led to MQLs that led to the most closed won deals?" or "what's the difference in activity patterns between closed won and closed lost opportunities?"
The RevOps manager on our call knew exactly what he wanted to analyze.
He could articulate sophisticated multi-touch attribution questions that would unlock serious insights. But the gap between knowing what questions to ask and actually being able to answer them felt insurmountable.
The Stack Migration Treadmill
Here's what really got my attention: this company isn't standing still. They're moving from Pardot to HubSpot by year-end, adding Demand Base for intent signals, and clearly investing in their revenue infrastructure. They're doing everything "right" according to the MarTech playbook.
But listen to what he said about their future state:
"We're going to have a lot more signals and just data that we need to interpret in real time ways."
More data.
More signals.
More complexity.
The promise is always that the next tool, the next migration, the next integration will finally give them the unified view they need. Instead, it often just creates more sophisticated ways to be confused.
The Moment Everything Clicked
About halfway through our demo, something shifted. We showed him how sales reps could get instant answers to questions like "tell me the activity from ABC Credit Union in the last 25 days" without digging through HubSpot files or Salesforce reports.
His response was immediate and visceral:
"That's huge, man. Being able to feed back actionable data in a conversational way, where sales is going to pick up the phone and hop on a discovery call... tell me everything this person has looked at on our site that we know about within the last 30 days. Like, oh boom, easy, right?"
This wasn't about features or functionality.
This was about eliminating the friction between having data and actually using it to have better conversations with prospects.
What Sales Teams Actually Need
The conversation revealed something crucial about what sales teams really want from all this data infrastructure. It's not more dashboards or more reports. It's contextual intelligence delivered exactly when and how they need it.
As he put it: "I have some friends who use Clay, which is cool. I have some friends that use UserGems, which again is cool. But like, none of that helps sales. It's all just kind of more slop to spam customers."
That phrase—"more slop to spam customers"—captures the fundamental problem with so much sales technology. We've gotten incredibly sophisticated at generating data and signals, but we've failed at translating those signals into genuinely helpful insights for the humans who need to act on them.
The Real RevOps Challenge
By the end of our call, I realized this RevOps manager represented something much bigger than one company's data challenges. He's part of a generation of revenue professionals who are:
- Technically sophisticated enough to understand what's possible
- Frustrated by the gap between promise and reality in current tools
- Drowning in an expanding stack that creates more complexity than clarity
- Desperate for solutions that actually help their sales teams have better conversations
His comment about his colleague asking on LinkedIn for exactly what we solve wasn't coincidence—it's a signal that the market is ready for a different approach.
What This Means for Revenue Leaders
If you're in RevOps, marketing operations, or sales enablement, this conversation probably sounds familiar. You're caught between the promise of data-driven revenue growth and the reality of fragmented systems, messy data, and sales teams who can't easily access the insights they need.
The good news is that we're finally reaching a tipping point. The combination of AI-powered analysis and conversational interfaces is making it possible to bridge that gap between having data and actually using it effectively.
The companies that figure this out first—that can turn their sales teams into data-powered conversation machines instead of report-reading dashboard warriors—are going to have a significant competitive advantage.
The Path Forward
What inspired me most about this conversation was the RevOps manager's immediate grasp of what becomes possible when you eliminate the friction between questions and answers. Within minutes, he was envisioning scenarios where his sales team could get instant, contextual intelligence about every prospect interaction.
That's the future of revenue operations: not more data, but better access to the insights hidden in the data you already have.
If you're struggling with similar challenges—if you find yourself saying "How in the hell do we figure this out?"—know that you're not alone. The solution isn't necessarily cleaner data or more sophisticated tools. Sometimes it's about finding a better way to have conversations with the information you already have.
What resonated most with you about this story? I'd love to hear about your own experiences with the gap between data promise and sales reality.
