I’ve been wrestling with this for a while, and I’m realizing standard ROI models might not be cutting it when you’re running campaigns in parallel across Russia and the US.
The problem I’m running into: our metrics are scattered. We track different KPIs for each market (Russia focuses more on engagement, US on conversion), we’re using different tracking systems, and we don’t have a unified way to compare performance apples-to-apples. When it comes time to present to leadership, I have separate stories for each market, but no unified narrative.
I know the platform has resources on cross-market benchmarks, and I’ve started pulling some data to try to build a consistent framework, but I’m not confident I’m doing it right. Like, should I be normalizing everything to a single KPI? Should I be building two separate ROI models and just presenting them side-by-side? How do I even account for the fact that conversion windows are different, creator pools are different, everything is different?
I’ve built ROI models before, but they’ve always been single-market. The complication of running dual-market campaigns is that I need a framework that actually holds up when finance or the CEO asks, “So which market’s influencer strategy is actually working better?”
Has anyone built a framework for this that actually holds up under scrutiny? What metrics did you pull into your cross-market ROI model, and how did you normalize them so the comparison made sense? Specifically, what helped you convince C-suite that the ROI story across both markets was real?
This is exactly my day job. The key insight: don’t try to build one ROI model. Build two operationally distinct models with one meta-framework on top.
Here’s what I mean: Russia and US have different conversion paths, different average order values, different customer acquisition costs. So the ROI formula needs to be market-specific. But you also need a meta-layer that lets you compare them fairly.
My framework: I calculate ROI for each market using market-appropriate metrics. For Russia, I use engagement-weighted LTV (because our conversion lag is longer and repeat purchase is higher). For US, I use direct-attributed revenue. Then, I normalize both to a percentage of ad spend, and that’s what I compare.
The magic is the normalization step. I’ve also introduced a “confidence score” for each market’s ROI—basically an assessment of how clean the attribution is and how much noise is in the data. Finance loves this because it’s honest about what you actually know versus what you’re estimating.
One more critical thing: I track cohort-to-cohort, not aggregate. So I compare US campaigns from Q1 to US campaigns from Q2, separately from Russian Q1 to Q2. That lets you see performance trends within each market before you ever try to compare across markets. Much cleaner story.
Also, I’d strongly recommend using the platform’s bilingual hub to pull case studies from other companies doing this. I’ve found 4-5 solid examples of cross-market ROI frameworks, and studying how other companies normalized their metrics was worth more than any framework I built from scratch. Sometimes it’s just seeing someone else’s structure that makes your own model click.
Anна’s framework is solid. From my perspective as someone who’s scaled this across multiple DTC brands, I’d add one layer: build your ROI model around the unit economics that matter most to your business, not generic metrics.
Here’s the question I always ask first: what does your finance team actually care about? Is it CAC? LTV? Payback period? Repeat purchase rate? Once you know that, you build your cross-market ROI framework backward from that metric.
For instance, if your business cares most about repeat purchase LTV, then your Russia and US models should both track repeat rates and LTV—but you’ll calculate them differently because the purchase behaviors are different. That’s actually more convincing to leadership than forcing a single metric across both markets.
My tactical advice: go to your CFO or finance lead first, before you build anything. Ask them: “If I could give you one cross-market ROI metric, what would make you feel confident about influencer spend?” Build your model around that answer.
One more thing: I always include a sensitivity analysis. Like, “if Russia has a 20% attribution error, how does that change the ROI story?” Showing that you’ve thought about uncertainty actually builds more confidence than pretending your numbers are perfect. Leadership knows there’s noise—they want to see that you’ve accounted for it.
Real talk: I’ve failed at this a few times before I figured out what works. The temptation is to build some beautiful, complex model that tries to be perfectly fair across both markets. Don’t do that. It’s overcomplicated and hard to defend.
What actually worked for us: we built a simple ROI story for each market, identified the one metric that mattered most to our board, and then presented both stories honestly—with clear notes about where they’re different and why.
Our board cared about CAC. So we calculated influencer CAC for Russia and CAC for US separately, showed the numbers, and said “here’s why they’re different.” We weren’t trying to make them the same. We were saying, “This is working in market A this way, and in market B this way.”
That honesty actually built more credibility than if we’d tried to normalize everything into some artificial metric. Finance appreciated that we weren’t hiding complexity—we were just explaining it clearly.
As someone who spends a lot of time connecting brands with creators and tracking partnership success, I’ll say this: the ROI conversation is important, but don’t let it overshadow the relationship piece. Some of the best long-term influencer value comes from ongoing partnerships that don’t show up immediately in conversion metrics.
I’ve seen brands build their cross-market ROI model and then realize they missed the partnerships that became strategic assets over time. My advice: track both the transactional ROI and the relationship health—creator retention rates, repeat collaboration rates, community size growth for partnered creators. That’s the full picture of influencer health across two markets.
But for the immediate ROI question you’re asking: I’d definitely go with Анна’s framework. She’s thought this through more rigorously than most.
Here’s what I’ve built with my clients: a dashboard that shows side-by-side ROI for each market, with a clear note about why they’re being measured differently. No false equivalencies. Just transparency.
We use three KPIs per market: one engagement metric, one conversion metric, and one lifetime value metric. That gives leadership a 3D picture instead of a single number they can misinterpret. And we update it weekly, so we can catch problems fast.
Honestly though, the biggest unlock is getting finance and marketing to agree on the framework before you run campaigns. Most of my clients run campaigns first, then try to build the ROI story backward, which is always messy. If you align on measurement upfront, everything else gets easier.
If you want, I could walk your finance team through a framework. I’ve got a template that’s worked well with other brands doing cross-market stuff.