I’ve been managing influencer and UGC campaigns for our brand across both markets for about eight months now, and I’m hitting a wall when it comes to proving value to our CFO. The metrics just don’t feel comparable—what works as a benchmark in Russia doesn’t translate to the US, and vice versa. We’re running parallel campaigns with different creators, different audiences, different seasonality patterns. When I try to aggregate the results into one coherent ROI story, it falls apart.
The challenge isn’t just the numbers themselves. It’s that we’re using different attribution models, platforms report differently, and our US team and Russian team are basically working in silos. I’ve got data, sure, but I can’t confidently say whether our US pilot is actually more efficient than our Russian core business, or if we’re just comparing apples to oranges.
I’ve started thinking about building a standardized reporting template that both teams feed into—something where we define what ‘conversion’ means consistently, what we’re actually trying to measure (immediate sales vs. brand lift vs. community growth), and what realistic benchmarks should be. But I’m wondering if anyone here has already cracked this. What does your actual ROI reporting look like when you’re running creator campaigns across these two very different markets? How do you present it to leadership without them asking “but what does this actually mean?”
This is exactly the problem I’ve been wrestling with too. Here’s what I’ve found works: stop trying to compare them directly. Instead, build two separate dashboards with identical KPIs, then create a third meta-dashboard that shows efficiency ratios. For example, CAC by market, ROAS by creator tier, and cost-per-engaged-follower. Then the CFO conversation becomes: ‘In Russia, we’re hitting X CAC; in US, we’re at Y; here’s why they differ and what we’re optimizing for.’ The second piece is to define your attribution model before you run the campaign. We use a hybrid approach: 30-day last-click for lower-funnel, and MTA (multi-touch attribution) for upper-funnel brand metrics. Russian team tracks coupon codes and UTM parameters religiously. US team focuses more on incrementality testing—geo-lifts on specific creator launches. Both feed into the same template, but the math isn’t forced to be identical. One more thing: if your platforms aren’t giving you apples-to-apples data, pull raw data and standardize it yourself. Yes, it’s extra work, but that’s where the actual insight lives.
Also, I’d strongly recommend you establish a baseline before you expand. Pick one creator in each market, run identical campaign structures, and measure everything religiously for 60 days. That gives you your first credible benchmark. Then every subsequent campaign can be measured against that baseline, not against some industry average that might not apply to your product category or audience. We did this, and it completely changed how we talk to finance about creator ROI.
The framing here is crucial. You’re not actually trying to compare markets—you’re trying to understand efficiency per market and then decide resource allocation. That’s a different conversation with the CFO. What I’d suggest: build a simple model that shows: (1) revenue influenced by creator campaigns per market, (2) total spend, (3) incremental revenue (this is the hard part—use incrementality testing if you can), and (4) ROAS or CAC depending on your business model. Then layer in a risk-adjusted view: US campaigns are newer, so they carry more learning costs, but they have higher revenue potential. Russia is proven but more competitive. That narrative matters more than perfect apples-to-apples metrics. For the actual mechanics, make sure both teams are using the same UTM structure, the same conversion window, and the same definition of ‘influenced.’ Where do you stand right now on incrementality testing? That’s usually the biggest gap.
I’ve been dealing with this exact issue expanding from Russia to Europe. What I realized is that the CFO doesn’t actually want perfect comparability—they want to trust that you know what you’re doing in each market and that you’re not wasting money. So I started spending less time building perfect metrics and more time building credibility through consistency. Every month, I show: ‘Here’s what we said we’d measure, here’s what we actually measured, here’s how we’re adjusting.’ After three months of that, the CFO stopped asking as many questions because the narrative was coherent, even if the markets don’t perfectly align. That said, standardizing your reporting structure is non-negotiable. You need the same columns, same definitions, same update cadence across markets.
This is a great question, and I think it also points to something bigger: are your US team and Russian team actually sharing learnings? Because if they are, you can position your reporting around shared insights, not just metrics. Like: ‘Here’s what the Russian team optimized for that the US team is now testing. Here’s the resource cost and the early results.’ That narrative is much more compelling to leadership than ‘here’s the ROI number.’ People trust process and learning more than they trust a single metric, especially cross-market.
From an agency perspective, this is where clients mess up the most. They try to be too clever with attribution models before they have enough clean data to justify the complexity. Start simple: direct conversions tracked via coupon code or UTM, 30-day window, backfill any blind spots with survey data or customer interviews. Get three months of clean data, then layer in incrementality testing or MTA. Right now, you’re probably spinning wheels on methodology instead of gathering signal. Focus on signal first.
From a creator’s perspective, I can tell you that the best campaigns I’ve been part of are the ones where the brand has a specific conversion metric in mind from the start. Like, they don’t hand you a brief and say ‘go viral’—they say ‘we’re measuring sales through your unique code, and here’s the expected price point.’ When they’re clear about that, I can shape my content to actually convert, not just entertain. If your brand teams aren’t aligned on what success looks like, that confusion probably flows downstream to creators, which means the data quality suffers from the start.