I’ve been working on this problem for months now, and I think I finally cracked it. We have a Russian-rooted e-commerce brand that’s been running influencer campaigns in both markets, but every time I tried to compare results, the numbers looked completely different. At first I thought we were just doing something wrong, but it turned out the issue was deeper—we weren’t measuring the same things in the same way.
The real challenge wasn’t the data itself. It was that Russian influencer campaigns tracked engagement and reach differently than US campaigns. Instagram Reels perform differently in Moscow than in New York. TikTok creator economics are wildly different. And when you’re trying to justify budget allocation to executives, you need to be able to say: “This strategy works better than that one,” not “These two countries run on completely different playbooks.”
I ended up building a standardized metrics framework using what I found in the platform’s bilingual resources. Instead of comparing raw numbers, I started normalizing metrics by market size, creator tier, and platform type. For example, instead of saying “5K engagement,” I started tracking engagement rate as a percentage of follower base and average per-post reach. That way, a micro-influencer campaign in Moscow and a similar-tier campaign in New York actually became comparable.
What helped most was connecting with other analysts in the community who were facing the same problem. Someone shared how they were handling currency differences and audience demographic shifts, and that pushed me to think about seasonal variations too—which are massive between these two markets.
But I’m curious: how are you guys handling this? Are you building your own frameworks from scratch, or are you using some kind of standardized platform for cross-market comparisons? And what metrics do you actually trust when you’re comparing campaign performance across regions?
This is exactly the problem I’ve been documenting. The standardization piece is critical, but I think you’re still missing one layer—attribution modeling. When I started normalizing by engagement rate and reach, my CFO asked the obvious question: “But did it actually drive sales?” That’s when I realized normalization isn’t enough without consistent attribution.
Here’s what I found works: Define your conversion funnel identically across both markets first (click → view → add to cart → purchase), then work backwards. A lot of teams mess this up because Russian e-commerce has different checkout flows than US platforms do. The time from click to purchase is different. Payment methods are different. So your attribution window needs to be market-specific, but your measurement methodology needs to be standardized.
I started using a 7-day attribution window for US campaigns and a 5-day window for Russia after analyzing historical data, but the KPIs I report are always normalized to the same formula: (Revenue from influencer source / Campaign cost) = ROI%. Same formula, market-appropriate windows. That’s how you actually compare apples to apples.
The platform’s bilingual hub helped me document these differences so I could train my team consistently. Have you built out attribution windows yet, or are you still just comparing engagement metrics?
One more thing I noticed—don’t just standardize raw metrics. Standardize your data collection process too. I spent three weeks last quarter trying to figure out why my April numbers didn’t match my colleague’s April numbers. Turned out she was pulling data on the 15th of every month, I was pulling on the 1st. Obvious mistake, right? But it happens constantly when you’re coordinating across time zones.
I now have a checklist: Same platform for data extraction, same day of week for pulling reports, same time zone conversion (we use UTC for everything), same definition of what counts as a “completed action.” It sounds boring, but it eliminates so much noise. Your normalized metrics will only be as good as your data hygiene.
What tools are you using to standardize data collection across the two markets?
Oh, this is such an important conversation! I love that you’re tackling this systematically. You know what I’ve seen help teams most? Bringing together practitioners from both markets to align on definitions before you even start measuring.
I organized a workshop with three Russian influencer managers and three US-based strategists, and we literally spent an hour just defining what “engagement” means to each of them. Turns out the Russian team was counting replies and shares as engagement, but the US team was only counting likes and comments. No wonder the numbers didn’t match!
Once everyone’s speaking the same language—literally and figuratively—the data makes so much more sense. The platform has been great for this because people from both sides can jump into a conversation and share their actual definitions and processes.
Have you thought about doing something similar with your team? I think standardization is way easier when the people doing the work are part of creating the standard.
Also, have you connected with anyone on the platform who’s already solved this? I have a feeling there are at least 2-3 people in this community who’ve built dashboards or processes specifically for RU/US comparison. They might save you a ton of time. I can help make introductions if you want!
This is a solid foundation, but I’d challenge one assumption: normalizing metrics assumes both markets have mature influencer ecosystems where the fundamentals are similar. They’re not. US influencer marketing is more saturated and commoditized. Russian market is more relationship-driven and less transparent on pricing.
What I’ve found works better than pure standardization is creating market-specific KPI hierarchies. Your primary KPIs shape your secondary KPIs. In the US, I lead with CAC and LTV metrics because attribution is clean and predictable. In Russia, I lead with engagement and audience quality first because those are better predictors of campaign success when you don’t have perfect attribution.
Then you create a bridge metric that’s actually comparable: “cost per engaged follower” or “revenue per impression.” Those work across markets because they’re deliberately abstracted from market-specific mechanics.
Your normalization approach is moving in this direction. But I’d push you to think about whether you’re normalizing for efficiency or just conforming to a standard that might not reflect reality. What question are you actually trying to answer with these comparisons?
Also, watch out for the trap where you spend so much time perfecting the framework that campaigns actually start suffering because implementation slows down. The best framework is the one your team will actually use consistently. Don’t overcomplicate it.
Also, if you’re building this yourself, document every decision. Why this attribution window? Why this engagement definition? Why this platform conversion? Seriously, write it down. In six months when someone asks why US and RU numbers don’t match, you’ll thank yourself for having a decision log.
One question though—when you normalize metrics, are you also standardizing creator compensation? Because I’ve noticed brands are sometimes trying to standardize metrics while still paying differently by market. That’s… not great. If engagement standards are the same, compensation should reflect market costs, not arbitrary differences.