Shortlisted for Bank and Payments Marketer of the Year, Dan Moshkovich makes the case for embracing AI…
In the traditional B2B software-as-a-service (SaaS) playbook, scaling up means hiring an army of enterprise sales representatives, renting premium office space, and aggressively chasing leads from the top down.
But at Chargeflow, the automated chargeback protection platform that recently closed a $35m Series A funding round, that formula is being turned entirely on its head.
“We’re not sales-led. Marketing drives our growth,” says Dan Moshkovich, Chargeflow’s proud Vice President of Marketing.
Speaking candidly about the company’s expansion, Moshkovich paints the picture of a hyper-engineered, data-driven architecture designed to intercept clients exactly where their financial pain is highest, whether that is inside an app store marketplace or within the neural networks of a generative AI engine.
Under Moshkovich’s direction, Chargeflow has compartmentalized its market strategy into distinct channels, assigning strict, dedicated ownership to maximize each one. It is an approach that ruthlessly prioritises efficiency over raw corporate headcount.
“We are very data-driven,” he says. “We measure everything carefully and have built our own marketing data warehouse. This allows us to determine what works and what doesn’t.”
This clinical look at data has given Chargeflow an operational agility that traditional enterprise software providers struggle to match.
By constantly measuring multi-touch attribution, Moshkovich ensures that capital is deployed only to high-yielding funnels.
“Efficiency, both operationally and in spend, is a major focus,” he says. “We trim what isn’t working. There are many levers in that operation, and it’s not isolated; it’s a cross-functional effort.”
Two playbooks
To manage a global customer base exceeding 20,000 businesses, including major household names like Sweetgreen, Miro, and Fanatics, Chargeflow splits its go-to-market engine into two core channels: product-led growth (PLG) for small and medium enterprises (SMEs) and sales-led growth (SLG) for the enterprise market.
For smaller merchants, the marketing approach relies heavily on performance and organic channels, pushing them toward a frictionless self-serve model.
Because these merchants are fiercely protective of their margins, the messaging focuses almost entirely on the company’s performance metrics. “They’re more sensitive to actual win-rate performance,” Moshkovich observes.
“They want to keep as much money in their pockets as possible and don’t want to spend time on the issue.”
For enterprise customers, the pitch changes completely. Large corporations view automated software with skepticism if it functions as an opaque “black box.”
Consequently, Chargeflow’s enterprise marketing shifts to emphasize visibility, robust reporting, and deep data export features.
To bridge the gap between marketing-generated demand and enterprise closures, Moshkovich relies on close collaboration with Gabi Kobrin, Chargeflow’s US-based Vice President of Sales.
Operating out of a newly expanded office at Rockefeller Plaza in New York, the two teams run highly coordinated accounts-based and field marketing operations.
“In a high-growth company, teams must work tightly together,” Moshkovich notes.
“Marketing works extremely closely with the sales team to ensure they have everything they need to succeed and build pipeline.”
Perhaps the most radical shift in Moshkovich’s marketing playbook is his aggressive move into Generative Engine Optimization (GEO).
As executive buyers increasingly shift their research habits away from traditional search engines like Google and toward conversational AI platforms, Chargeflow is trying to outmaneuver its competitors by optimising for large language models (LLMs).
When a modern payments executive or e-commerce merchant asks ChatGPT, Claude, or Perplexity to recommend a chargeback management solution, Moshkovich wants Chargeflow to be the definitive answer.
“A major challenge everyone is looking at now is the GEO landscape, how to leverage it when people use Claude or ChatGPT,” he says.
Navigating this new digital frontier requires reverse-engineering an entirely new type of recommendation engine.
“It’s murky how LLMs make decisions. We’re figuring out which data points matter and how to get in front of them, review sites, Twitter feeds, sentiment signals. How do we ensure Chargeflow is ingested as a natural recommendation?”
While the traffic flowing from AI search tools is still minor compared to traditional web searches, the conversion potential is uniquely high.
“Volume from these channels is still small relative to traditional search, but it’s growing fast and the buyer intent is high,” Moshkovich explains.
Rather than treating AI search as a long-term experiment, Chargeflow is deploying capital into the channel immediately to secure an early footprint before the market matures. “We’re treating it as a channel to win early, not one to wait on,” says Moshkovich.
“We want to be the number-one answer when someone asks an AI agent for a chargeback solution.”