I’ve been wrestling with this for months now. We’re trying to scale a Russian beauty brand into the US market, and every time I pitch a campaign to our CFO, I get the same question: “How do we know if these numbers are good?”
The problem is clear—I can find case studies from Russian agencies showing solid ROI on influencer partnerships, but when I look at what “good” looks like for US campaigns, the data feels completely different. Different platforms, different audience behaviors, different pricing. I don’t even know if I’m comparing apples to apples anymore.
Last month, I tried aggregating data from three different campaign case studies (two from Russia, one from the US), and the influencer cost-per-engagement varied by 300%. Was that a market difference? A quality difference? A measurement difference? I honestly couldn’t tell.
What I really need is a way to see how other brands have tackled this exact problem—not just isolated case studies, but actual comparisons showing how Russian-rooted brands navigated the transition and what their real benchmarks ended up being. Are there patterns in how successful companies set expectations for cross-market campaigns? How do you validate your numbers when the markets are that different?
This is exactly why I started building a comparison framework last year. Here’s what I learned: you can’t directly compare Russian and US influencer metrics without normalizing for platform mix first.
In Russia, Instagram and VK dominate influencer work. In the US, it’s Instagram, TikTok, and YouTube. Cost-per-engagement on TikTok can be 5-10x lower than Instagram, but the conversion rates are completely different depending on your product category. Beauty works differently on TikTok than, say, SaaS.
What actually worked for us: I pulled 15 case studies from different beauty brands (7 Russia-focused, 8 US-focused) and normalized everything to cost-per-conversion instead of cost-per-engagement. That was the equalizer. When we looked at the data that way, the benchmarks became comparable.
I also started tracking platform distribution as a percentage of total spend. A campaign that’s 40% Instagram vs. 40% TikTok is fundamentally different. Once we accounted for that, the variance dropped significantly.
The hard truth: if you’re mixing markets in a single benchmark, you need to either (1) separate by platform distribution, or (2) use conversion metrics instead of engagement. Engagement metrics will mislead you every single time.
Do you have access to the actual platform breakdowns from those case studies you pulled, or just aggregate numbers?
One more thing—I’d recommend looking at the actual creator tier distribution. A US campaign with mostly macro-influencers (100k+) will have completely different ROI than a Russia campaign weighted toward micro-influencers (10k-50k). The pricing and audience quality are so different.
I started tracking this as a standard dimension: what percentage of the budget went to macro vs. micro vs. nano? That single variable explained about 40% of the variance I was seeing. Once I controlled for it, the numbers felt way more reliable.
I love this question because it’s forcing everyone to be more intentional about partnerships! From my side (partnerships), I’ve noticed that successful cross-market campaigns often involve finding intermediaries or agencies that understand both markets deeply.
When we were setting up collaborations between Russian creators and US brands (or vice versa), the benchmark conversations happened early. We’d sit down with both sides and say, “Here’s what success looks like in your market, here’s what success looks like in theirs—now let’s agree on a middle ground.”
One thing that helped: connecting with agencies or consultants in each market who could provide local benchmarks. Not just case studies, but people who actually know the current rates and performance expectations. The market changes fast, especially with platform algorithm shifts.
Have you thought about reaching out to a local partner in the US who could help validate benchmarks? Sometimes that conversation is worth more than any aggregate data.
We hit this exact wall when we expanded from Russia to Eastern Europe last year. My advice: don’t try to force a single benchmark. Instead, build separate benchmarks for each market, then look for the ratio.
For us, a US influencer campaign cost roughly 2.5x more per engagement than our Russian baseline, but conversion rates were 1.8x higher. So our actual ROI was better in the US despite higher costs. That comparison made sense to our investors.
The case studies we found most useful weren’t the highly successful ones—they were the medium-sized ones where they explicitly called out their mistakes and market adjustments. Those showed us where to expect friction.
Also, I started asking creators directly about their pricing logic in each market. That’s when I understood the variance. A creator in Russia might charge $500 for a post; the same quality creator in the US charges $1,500. It’s not just inflation—it’s audience access and platform saturation.
The fundamental issue here is that you’re trying to create certainty in an inherently uncertain space. Benchmarking across markets is useful, but only if you’re clear about what you’re measuring and why.
Here’s my framework: instead of looking for a single cross-market benchmark, establish a baseline in each market, then measure your campaigns against their respective baselines. The comparison isn’t “Is my US campaign better than my Russia campaign?”—it’s “Is my US performance improving quarter-over-quarter?” and separately, “Is my Russia performance improving?”
The 300% variance you’re seeing? That’s actually normal when you’re mixing markets without controlling for variables. It doesn’t mean your data is bad; it means you need a more structured approach.
I’d recommend: (1) Pick one metric that translates across markets (e.g., cost-per-qualified-lead or cost-per-purchase), (2) Build a case study database with the variables that matter (platform, creator tier, product category, audience geography), and (3) Use that to make predictions, not as hard rules.
Your CFO wants confidence. Give them a range with confidence intervals instead of a single number. “Based on 12 comparable case studies, we expect 2-4x ROAS with a 70% confidence interval.” That’s honest and defensible.
From my side as a creator, I can tell you why the numbers feel so different: expectations are totally different between markets. In Russia, brands often accept lower engagement rates because the audience is more niche. In the US, brands expect higher engagement because there’s more competition for attention.
I charge differently in each market because my audience is different and the market rates are different. When benchmarking, you have to account for what creators are actually charging and why.
Also, the way brands measure success is different. Russian brands I work with often care about awareness and reach. US brands obsess over conversion. That changes how we structure the campaign, which changes the results.
If you’re comparing case studies, make sure you’re looking at creators who had similar goals in each market. Apples to apples.