How monday.com Built a Signal‑Driven ABM Machine at Global Scale
monday.com has made its mark on the B2B SaaS landscape by helping teams manage workflows, projects, and cross-functional processes at scale. The company is widely recognized for its product-led growth (PLG) engine that has driven rapid adoption across organizations of all sizes.
But as monday.com matured and set its sights on the enterprise market, leadership issued a clear mandate for growth. "The main request from the CEO was, 'I want you to build a scalable, systematic machine like the PLG that will be the top tier,'" recalls Shai Masot, who leads monday.com's global enterprise team. It was an ambitious goal, and one that would require rethinking how the company approached demand generation from the ground up.
We can see everything in one place. We can see that the data is correct, understand the signals, and finally optimize our campaigns.
Shai Masot
Strategic Demand & Lead Generation
More Data, Less Clarity
Despite having access to massive amounts of data, the team faced a fundamental problem: nothing was connected.
Account lists lived in one platform. Intent signals came from another. Product usage data sat in a separate database. And marketing engagement metrics were somewhere else entirely.
"Nothing is working together," Masot says. "You're in endless iteration."
What was breaking down:
Inaccurate account lists. CRM data was often stale or misaligned, leading to wasted outreach
Mismatched signals. Third-party data was frequently tied to the wrong regions or segments, reducing actionability
Siloed data sources. CRM, product usage, and marketing engagement lived in separate tools with no unified view
Sales misalignment. Teams were often out of sync, leaving sales unaware of what content prospects had consumed or where accounts stood in the funnel.
"At the end of all of that, you have this black box. You don't necessarily know what signals you're seeing, what signals you're optimizing to, and how you measure yourself,” says Masot.
The result was a team stuck spending the majority of its time on operations and coordination instead of strategy, optimization, and the kind of personalization at scale that enterprise ABM demands.
From Fragmented Data to a Unified Revenue Engine
After gaining early access to ZoomInfo's GoToMarket Studio (GTM Studio), Masot and his team saw an opportunity to change how they built and ran demand gen programs. Instead of a sweeping overhaul, they started with a focused proof of concept.
"It was remarkably simple," Masot says.
The team selected a single sales team and defined a clear objective: improve personalization to drive larger deals and higher-quality pipeline.
This avoided complexity to provide value quickly.
They centralized their data in GTM Studio:
CRM data
Product usage signals
Marketing engagement (content, webinars, downloads)
All this data got mapped to custom fields for a single, enriched view of each account.
The team used GTM Studio's audience development tools to build dynamic, always-on audiences that could be filtered by firmographics, intent signals, product usage, and buying stage. This eliminated the need to rebuild lists from scratch for every campaign.
Those audiences then got pushed directly to:
LinkedIn
Facebook
ZoomInfo Display
Google Display Network
Sales workflows
And reps got real-time notifications about account movement and buying committee activity.
For the first time, everything worked together: "We can see everything in one place. We can see that the data is correct, understand the signals, and finally optimize our campaigns," says Masot.
One of the most significant unlocks came from GTM Studio's AI enrichment. The team could create custom AI-generated fields with data from multiple sources to produce new signals and indicators, all without opening a single ticket or waiting on an operations team.
"It's like this aha moment," Masot says.
The Results: 4X Scale. 4X Faster.
The impact was immediate and measurable. What began as a single-team pilot quickly expanded into a global operation.
4X program scale: from 8 ABM sales teams across 4 programs, to 22 sales teams running 12 programs across 5 global regions.
4X faster program launch: what previously took nearly four months for 8 teams now takes just one month for 22 teams.
Full funnel visibility: sales teams now have real-time insight into account status, audience movement, and buying signals.
But the biggest shift came in how the team's day-to-day experience changed. Instead of spending the majority of their time on operational coordination, monday.com's demand gen team could focus on what they were hired to do: strategy, creativity, and optimization.
The Machine Is Just Getting Started
For Masot, the experience with GTM Studio has reinforced a broader conviction about where AI-powered go-to-market tools are headed. "AI is supposed to give you the ability to be creative, to optimize, to see the full picture," he says. "It gives you the ability to drop the operations and take your marketing abilities to the next phase."
It's a vision monday.com is already living. What started as a single proof of concept with one sales team has grown into a global, signal-driven revenue engine. And Masot says the foundation that made it possible was having a technology partner willing to grow alongside them.
For teams still stuck in the operational grind, Masot’s advice is simple: start small, get your data aligned, and trust that the machine, once built, will do the rest.
