As global digital ad spend pushes past the trillion dollar threshold and AI becomes embedded in everything from creative production to campaign optimization, growth marketing is entering a more disciplined era. The playbooks that once worked in a single market or channel no longer translate cleanly across regions, business models, or increasingly automated platforms. For companies scaling across borders, especially in SaaS and multi market B2C, the margin for error is shrinking.
Jovana Zrnic has built her career inside that complexity. Over the past seven years, she has worked across the US and 13 EMEA markets, partnering with more than 45 companies through startup, corporate, and freelance roles. Her focus is not on one off campaigns, but on building scalable growth frameworks, strengthening CRM infrastructure, automating workflows, and integrating AI driven systems that improve execution without sacrificing strategic control. In this interview, Zrnic discusses what global expansion really demands, why high performing teams treat execution as seriously as strategy, how to rethink attribution in a post ATT world, and where AI creates measurable lift, and where it introduces risk.
Q: You have spent seven years working across the US and EMEA with both B2B and B2C teams. How did that mix of markets and business models shape the way you think about growth marketing today?
That cross-market exposure fundamentally shaped how I approach growth. When you manage paid media across 13 EMEA markets, each with different consumer behaviors, payment ecosystems, and competitive dynamics, you learn quickly that broad-stroke strategies don’t scale. What converts in the U.S. often underperforms in Northern Europe, and what works in Southern Europe may fall flat in the Nordics. The versatility forced me to develop a deeply contextual approach: diagnose the specific environment first, then design the system around it.
I believe that mindset, the discipline to constantly experiment, adapt, and resist defaulting to what worked last time, is the most valuable skill a growth marketer can have today. In an industry where global digital ad spend has surpassed $1 trillion and AI is reshaping every channel simultaneously, the marketers who thrive are the ones comfortable with everlasting reinvention.
Q: You have partnered with more than 45 companies across startups, corporate teams, and freelance roles. What patterns have you noticed in how high-performing teams approach growth compared to those that struggle to scale?
The pattern I see most consistently is that high-performing teams treat execution with the same rigor as strategy. I personally believe strategy is overrated and execution is underrated; the best teams master both equally. McKinsey’s research supports this: 81% of high-growth companies outperform specifically in data and analytics mastery, and 71% have adopted agile, cross-functional collaboration processes. That matches what I’ve observed firsthand.
The teams that scale are ones where every member operates as an individual builder while also functioning as an effective collaborator. There’s a builder mindset, particularly relevant in this AI era, where each person takes clear ownership over their domain, tools, and outcomes. The other distinguishing factor is communication: streamlined, direct, and candid. HubSpot’s research shows marketers with a single source of truth are 56% more likely to be strongly aligned with sales, yet only 35% of marketers claim that alignment exists. The teams that close that gap are the ones that grow.
Q: Much of your work focuses on building scalable growth frameworks rather than one-off campaigns. What does a modern growth operating system look like in practice?
It depends heavily on the business model. What works for a B2B SaaS company looks quite different from a D2C brand or a multi-market B2C operation. But I can speak to what I’m building right now as a useful example. A modern growth operating system has three layers: a strong CRM infrastructure that serves as the single source of truth for pipeline and customer data; AI-enhanced project management that streamlines planning, execution, and cross-functional visibility; and increasingly, AI agents that handle the manual lift (reporting, optimization tasks, data enrichment) orchestrated by humans who make the strategic decisions.
Every team member has clear ownership over their respective domains, tools, and projects. That clarity is what enables a lean team to move fast. Gartner’s 2025 CMO Spend Survey found that 95% of CMOs now prioritize GenAI investment, with nearly half citing time efficiency as the primary ROI driver. That aligns with what I see in practice: the operating system itself should be designed to eliminate friction, so the team can focus on the decisions that actually move the business.
Q: Attribution is a major challenge for marketers right now. How should teams be thinking about combining platform signals, first-party data, and experimentation when making budget decisions in 2025?
My approach starts with infrastructure: server-side tracking combined with first-party data imports across all platforms. Once your data foundation is solid, creative and in-platform experimentation becomes a regular part of optimization rather than a special project.
Beyond that, I’m a strong believer in layering incrementality testing into the paid media mix. At New Balance, we ran geo-lift tests on Google, alongside brand lift and conversion lift studies on Meta to validate whether platform-reported results held up against controlled holdout groups. That combination, testing incrementality across both search and social with different methodologies, gave us a much more honest picture of where spend was actually driving net-new revenue versus taking credit for conversions that would have happened organically. The industry has been moving in this direction since the post-ATT era, and what’s encouraging is that open-source tools like Google’s Meridian and Meta’s Robyn are now making marketing mix modeling accessible beyond enterprise teams with six-figure measurement budgets, though they still require meaningful data science capability to implement properly.
Monthly budget revision remains essential, with larger strategic shifts on a quarterly basis when the data warrants it. But the teams that consistently outperform are the ones treating measurement as a continuous, evolving system, not a quarterly report.
Q: AI is now embedded across creative, bidding, and operations. Based on your experience, where does AI actually drive meaningful lift in performance marketing, and where does it introduce risk?
Creative is where AI delivers the most consistent and measurable impact. Meta’s own data shows that ad sets running three to ten creative variations reduce cost per action by 46%, and top DTC brands now produce 50 to 70 new ads weekly on Meta alone, according to Motion’s 2025 Creative Trends Report. That volume simply wasn’t feasible without AI-assisted production. Campaigns experience a 41% drop in click-through rate after the same user sees an ad more than four times, so the speed of creative iteration directly affects performance. I’ve seen this time and again: creative volume and velocity are now table stakes.
Operations is another clear win. AI-enhanced workflows for reporting, budget pacing, and campaign management should be standard. I see minimal risk here if you test on a controlled sample first. Worst case, you redo something, which is not a significant setback.
Bidding is where I’m more cautious. AI-driven bidding means less direct control, immediate spend consequences, and often limited transparency into what actually drove the result. There’s a reason independent incrementality testing has shown that platform-reported conversions can significantly overstate true incremental impact. I lean into automated bidding where the objective, platform, and campaign structure support it, but there are still scenarios where I prefer more manual control. It depends on the context, and I think any marketer who tells you otherwise isn’t being honest about the tradeoffs.
Q: You have worked extensively with SaaS companies as they scale across multiple markets. What mistakes do teams most often make when trying to replicate growth strategies from one region to another?
The most common mistake is assuming that what worked in the most competitive market, typically the U.S., will transfer directly to other regions. A Frontline Growth analysis found that 50% of U.S. software companies expanding to Europe don’t have a single marketing resource in the market a year after arriving. That tells you how severely teams underestimate the localization required.
Positioning should be 100% tailored to the target audience’s psychographics in each region. I learned from managing campaigns across 13 EMEA markets that what motivates a purchase decision in Southern Europe differs fundamentally from Northern Europe. Even basic operational details vary dramatically: in the Netherlands, iDEAL processes 60 to 70% of online payments; in Sweden, Klarna appears on roughly 98% of BNPL-enabled sites; in Spain, 57% of physical store transactions are still cash. Customer service preferences diverge too. Interestingly, McKinsey found that Gen Z consumers are 30 to 40% more likely to call than millennials, which contradicts the digital-native assumption many companies use to justify chatbot-first strategies across all markets. The point is: rather than going in and presenting yourself, start from how the audience in each specific market perceives your niche, then adjust everything (positioning, payments, support channels) accordingly.
Q: Measurement and experimentation are central to your approach. How do you help teams move beyond vanity metrics and build decision-making frameworks they can trust?
It starts with asking the right questions, because the metrics problem is really a decision-making problem. Gartner found that only 52% of CMOs can successfully prove marketing’s value to the business, and Forrester reports that 64% of B2B marketing leaders don’t trust their own organization’s measurement for decision-making. That’s a systemic issue, not a tools issue.
My framework is straightforward. We always begin with “Should we do it?” and that requires a hypothesis backed by a specific data point, not intuition. If the answer is yes, we move to “How do we do it?” and build a structured approach. If the question is “Should we expand into a new region?” We say “yes” based on an evidence-backed hypothesis, then break the initiative into workstreams that are mutually exclusive but collectively exhaustive. Each stream has its own success criteria tied to business outcomes, not vanity metrics. The key is that every experiment maps back to a revenue or pipeline metric the C-suite cares about. HubSpot’s data shows marketers who compute their ROI are 1.6 times more likely to receive higher budgets, so building that measurement discipline isn’t just good practice, it’s how you earn organizational trust and investment.
Q: Looking ahead, what skills or mindset shifts do growth marketers need to develop if they want to stay effective as AI, automation, and data complexity continue to accelerate?
Two shifts matter most. First, accept that the learning curve and discomfort are now permanent features of the job, and find a way to embrace that process. Anything involving AI doesn’t work perfectly at first; it takes iterations and fine-tuning. The marketers who will thrive are the ones who treat that iteration cycle as the work itself, not as an obstacle to the work.
Second, and this is the one I feel most strongly about: don’t outsource your critical thinking to AI. Don’t blindly follow every trend and platform update. When you prompt an AI tool, bring your own ideas, hypotheses, and strategic context first. Don’t just ask, contribute! That’s how you learn, grow, and remain irreplaceable. The professionals who use AI as an amplifier for their own judgment will lead. The ones who use it as a substitute will become redundant.
