I’ve been managing influencer campaigns for about three years now, and I thought I had attribution figured out. Then I started running parallel campaigns in Russia and the US, and everything fell apart.
The problem isn’t just that the platforms differ—it’s that the entire customer journey looks different. In Russia, I see a lot of direct conversions from Instagram Stories. In the US, the same campaign breeds a ton of click-throughs but the actual purchase happens days later through email. My Russian team measures success by immediate engagement; my US partners care about attributed revenue weeks down the line.
I started digging into how other teams handle this, and I realized I wasn’t alone. The real issue is that I’ve been using the same attribution window and the same metrics across both markets, which is basically nonsense. What works for benchmarking in one region completely misleads you in another.
I’m trying to understand: are you all using different attribution models for different markets, or do you have a unified framework that adapts? How do you even compare ROI between regions when the conversion paths are so different?
This is actually a critical problem that most brands skip over. I work with e-commerce data all day, and the attribution issue you’re describing is real.
Here’s what I’ve learned: you need to separate campaign attribution from market benchmarking. For attribution, I build separate models for each region because the funnel architecture is genuinely different. Russian audiences tend to convert faster but with lower cart values. US audiences take longer but spend more per transaction.
What I do is this: I set a 7-day attribution window for Russia (where most conversions happen quickly) and a 30-day window for the US. Then I calculate ROI separately for each market using its own baseline, not a global one.
The breakthrough came when I stopped trying to make the same metric work everywhere. Instead, I created a meta-metric: cost per attributed ruble or dollar, calculated independently. Then I compare those numbers across markets, not the raw conversion rates.
Have you tried segmenting your attribution model by market first, then pulling the insights back up to a strategic level?
One more thing—I’d recommend checking your pixel implementation. Half the time, attribution breaks not because of the market, but because the tracking setup is inconsistent. Russian platforms sometimes have different privacy settings or pixel delays. Make sure your US and Russian tracking are actually comparable before you blame the regions.
Also: what platforms are you running on? If you’re mixing VK, Instagram, TikTok across both regions, that’s adding another layer of complexity. Each platform has its own attribution quirks.
I love that you’re thinking about this systematically. From a partnership perspective, I see this problem show up all the time when brands and influencers don’t align on what success actually looks like.
When I’m negotiating a campaign with creators in both markets, I make sure we agree upfront on the attribution model. Don’t assume the influencer in the US is tracking the same way as the one in Russia. Some creators use UTM parameters religiously; others hand you a promo code and hope for the best.
I’ve started including attribution clarity in my briefs—basically, “here’s how we’ll measure this, and here’s what you need to do to feed us that data.” It’s boring stuff, but it prevents so much chaos later.
Have you talked to your creators about how they’re tracking their own performance? Sometimes the disconnect isn’t between markets—it’s between what you think they’re measuring and what they actually are.
You’re identifying a real structural problem. Most companies try to solve this by creating one global attribution model, and that’s backwards.
What I’ve seen work is the opposite approach: build your attribution model within each market first, using that market’s actual user behavior. Only after you have high-confidence attribution for Russia and the US separately should you try to compare them.
The key insight is that you’re not trying to make the metrics identical—you’re trying to make them intelligible and comparable. A 2x ROI in Russia might be equivalent to a 1.5x ROI in the US, depending on market maturity, competition, and user purchasing power.
One practical step: run a cohort analysis. Take users acquired through influencer channels in each market and follow them for 90 days. Track revenue, repeat purchase rate, and lifetime value. That gives you a much clearer picture of what’s actually happening than just looking at the immediate conversion window.
What’s your current attribution window in each market?
We hit this exact problem when we scaled to US markets. At first, I thought one of my regional teams was just bad at their job. Turns out they were good—they were just measuring something completely different.
What helped us was having both teams sit down and literally walk through a single customer journey for each market. We realized that in Russia, our customers buy because they see the product on an influencer’s feed and buy immediately. In the US, they see it, add it to a wishlist, compare with competitors, and come back through email a week later.
Same product, same campaign, different purchase behaviors. Once we understood that, we stopped trying to force everything into one model.
My advice: talk to your finance and data teams. They probably already know this stuff is broken—they just haven’t connected it to your influencer campaigns yet.
This is where a lot of agencies get unstuck. The attribution problem you’re describing is exactly why I push clients to use multiple attribution models simultaneously—first-touch, last-touch, and time-decay—for each market.
But here’s the real talk: most of your ROI confusion is probably coming from the fact that you’re comparing markets that are in completely different maturity stages. US influencer marketing is saturated; Russian influencer marketing is still growing. Your benchmarks should reflect that.
I’d recommend you pull in some US-based partners who work with DTC brands at scale. They’ve probably already solved this internally. What model are they using? How are they handling the attribution window problem?
Also—are you accounting for organic lift? Sometimes the influencer campaign creates brand awareness that converts weeks later through a completely different channel. That drives your attribution numbers down artificially.
From a creator’s perspective, I can tell you that the way I track my own conversions is probably different from how you’re tracking them. I use a mix of UTM codes, Linktree, and honestly just asking my audience directly what brought them to the brand.
I notice that US followers click links way more than Russian followers do. Russian followers tend to DM me or check out the brand’s account directly. So if you’re only counting direct link clicks, you’re missing a huge part of what I’m actually driving.
Maybe the problem isn’t your attribution model—maybe it’s that you’re not capturing all the ways people actually discover and buy the product?