I’m in a tough spot right now. We’re a Russian e-commerce brand prepping our first serious push into the US market, and our CFO is basically asking: “prove to me that influencer partnerships will move the needle before we commit real budget.”
The problem? We have solid ROI data from our domestic campaigns, but the US market is completely different. Different creators, different audience expectations, different conversion patterns. I keep running into the same wall: how do you set realistic benchmarks when you don’t have a baseline?
I’ve been thinking about this wrong, I think. Instead of trying to predict exact numbers, maybe I should be asking: what’s the minimum viable ROI threshold that justifies a pilot? And where do I actually find comparable data from brands that have done the Russia-to-US jump?
Some of my team mentioned tapping into case studies from other cross-border expansions, but I’m skeptical about how directly they’d apply. We sell completely different products.
Has anyone built a credible ROI narrative for entering a new market without relying on historical data? What data points actually convinced your leadership to fund the initial budget, and how did you frame the risk?
I’ve tackled this exact problem. The key insight I found: stop looking for “perfect” benchmarks and instead build them backwards from your unit economics.
Here’s what worked for us: I took our best-performing Russian campaigns and reverse-engineered the cost-per-acquisition threshold that would still hit our target margins in the US market. Then I talked to US-based marketers about typical creator performance ranges—not their specific numbers, but the patterns.
Once I had that threshold, I framed the pilot as a data-collection exercise, not a full commitment. “We’re investing $X to understand if this channel can hit Y cost-per-action. If it does, we scale. If not, we pivot.” That language shifted the conversation from “prove it works” to “let’s measure what works.”
I also pulled together creator category data—micro vs macro performance splits, engagement decay patterns over 60 days—and showed that similar product categories (not identical, but similar) had consistent ranges. That removed some of the guesswork.
One more thing: I always present the pilot parameters clearly. How many creators? What’s the campaign length? What’s the success metric? That clarity made the CFO way more comfortable with the risk because it felt bounded.
One tactical thing I’d add: set up a shadow dashboard before you launch. Track everything—CPM, engagement rate decay, conversion windows, LTV cohorts—using the exact same metrics you’ll use for full-scale campaigns. That way, when the pilot ends, you have zero friction translating results into a scale decision.
The other insight was realizing that influencer ROI in a new market is as much about audience composition as campaign performance. Run some demographic analysis on creator audiences. If there’s misalignment between the creators popular in that market and your actual customer profile, that’s a red flag—not a reason to cancel, but a reason to adjust your creator selection strategy.
This is such an important question, and I love that you’re thinking about it strategically rather than just hoping for the best.
From a partnership perspective, I’d suggest connecting with 2-3 agencies or managers in the US market who’ve worked with Russian or international brands before. Not to steal their playbooks, but to understand what they’ve learned about what works and what doesn’t. These conversations often reveal patterns that benchmarks alone won’t show you.
I’ve found that having those relationships in place before you launch actually reduces risk because you have on-ground partners who understand both cultures and can help you interpret early results. Plus, if your initial pilots show promise, those partners become your foundation for scaling.
Have you thought about starting with a collaboration partner in the US market as part of your pilot? Someone who knows the landscape? That takes some pressure off needing perfect data upfront because you have expert judgment backing your decisions.
The way I’ve approached this with DTC brands is to separate the ROI question into two parts: (1) What’s the CAC we can afford based on LTV? and (2) What’s the historical CAC for influencer campaigns in that market segment?
You’ve already got part 1 figured out—that’s your unit economics in the US. For part 2, you don’t need exact data from your category. You need comparable data. What’s the influencer CAC range for e-commerce in the US? That’s the benchmark.
Then the pilot becomes: “Can we hit this range or beat it with our creator selection strategy?” If yes, it scales. If no, you need a different creator profile or compensation model.
The framing matters enormously here. Don’t pitch it as “prove influencers work.” Pitch it as “let’s validate our hypothesis about creator performance in this market and refine our strategy based on real data.” That’s a testable, bounded commitment—exactly what CFOs want to fund.
I went through this with my first international expansion, and honestly, the thing that helped most was admitting to leadership: “We don’t have perfect data, but here’s what we do know and here’s what we’re willing to risk to find out.”
What made a difference for us was bringing in one trusted partner who had done similar transitions. Not to validate everything, but to pressure-test our assumptions. That conversation often revealed blind spots we didn’t even know we had.
Also, I’d recommend running a micro-pilot first—like $10K across 10-15 creators for 30 days. Treat it as an experiment, not a campaign. Collect every signal you can. Then make the CAC calculation based on real data, not projections. That’s way easier to present to your CFO than speculation.
Here’s what I tell clients: your first pilot isn’t about proving influencers work broadly. It’s about finding which specific creators or creator categories work for your brand in that market.
I typically recommend a stratified pilot approach: allocate your budget across different creator tiers (macro, mid-tier, micro) and different content types. See which buckets hit your ROI threshold. Then in your second phase, you optimize your mix based on what you learned.
The beauty of this approach is that it gives you ammunition for scaling. You can say, “In phase 1, we found that mid-tier creators with engagement rates above X delivered Y CAC. In phase 2, we’re scaling this profile.” That’s a data-backed narrative that CFOs understand.
I’ve seen this work better than trying to predict exact ROI upfront, which is a fool’s errand in a new market anyway.
From the creator side, I’ll say this: the biggest ROI miss I see is when brands don’t give creators enough context about what success looks like. If you’re testing with US creators and they don’t understand your conversion funnel or your actual product, the collaboration will underperform.
So when you’re running the pilot, be really clear with creators about what metrics matter to you. Is it clicks? Impressions? Actual conversions? Once they know that, they can optimize their content strategy around it. I guarantee your ROI will be better.
Also, don’t just look at big creators for your pilot. Some of the best ROI I’ve seen comes from micro-creators who have hyper-engaged audiences. The engagement-to-reach ratio is often way better, which means better conversion rates. Just a thought as you’re building your creator mix.