I’ve been managing influencer campaigns for both markets for about two years now, and honestly? The ROI tracking used to be a complete mess. I’d run campaigns simultaneously in Russia and the US, get reports back, and then spend weeks trying to figure out what actually worked.
The problem was that I had no baseline for comparison. A blogger in Russia with 50k followers performs completely differently than a US blogger with the same follower count. Engagement rates, conversion patterns, even what “success” means—it’s all different. I was basically throwing darts in the dark.
About six months ago, I started using cross-market benchmarks to set actual targets before launching campaigns. Instead of hoping for good results, I now look at what similar campaigns achieved in each market and set KPIs based on that data. It sounds obvious, but it changed everything.
Now I can actually compare apples to apples. When a Russian micro-influencer with 15k followers gets 8% engagement and a US micro-influencer with similar size gets 3.5%, I understand why instead of assuming one is just “better.” And more importantly, I can predict what my budget will actually return before I commit it.
The tracking itself got easier too—once I standardized how I measure results across both markets, consolidating the data stopped feeling like a nightmare. I built one dashboard that shows ROI for each campaign in local currency and then normalized metrics, so I can actually see patterns.
What specific metrics are you currently tracking when you run campaigns on both markets? And how do you decide what counts as a “win” when you’re comparing results across different regions?
This is exactly the issue I see most brands struggling with. The key insight you’re touching on is that engagement rates and conversion metrics are market-dependent, not universal. In Russia, we typically see higher engagement on micro-influencers (5-12%), while US micro-influencers frequently benchmark at 2-4%. If you’re not accounting for this, you’ll either overpay for US influencers or completely underestimate Russian talent.
Here’s what I track across markets:
- Engagement rate (likes + comments / followers)
- Cost per engagement (total spend / total engagements)
- Cost per click (for link-based campaigns)
- Conversion rate (if we have pixel tracking)
- Cost per conversion (ROAS)
The mistake I see constantly is brands comparing raw numbers without normalization. A campaign with 100k impressions in Russia and 100k in the US will not generate the same results because audience intent, platform algorithms, and cultural resonance differ significantly.
One practical thing: I always A/B test with at least 3-4 influencers per market before scaling. This gives me real data for that specific market segment, not borrowed benchmarks. Then I use those micro-benchmarks to extrapolate for larger campaigns.
Also—and this is critical—make sure you’re tracking attribution correctly. Are you using UTM parameters? Promo codes? Pixel tracking? Because if you’re relying on influencers to “report” results, you’re missing data. The best setup I’ve seen is a combination of:
- Unique discount codes per influencer (tracks direct sales)
- UTM parameters (tracks traffic and behavior)
- Pixel/conversion tracking (shows purchase events)
Since you’re working across two markets with different payment systems and currencies, standardize everything to one base currency for comparison and run your analysis in a spreadsheet or BI tool. I use a simple Google Sheets model that pulls data from our analytics and calculates normalized ROI.
What’s your current attribution setup looking like?
Strong execution here. You’ve identified the core problem—lack of market-specific baselines—and solved for it, which is where most brands fail. Here’s what elevates this further:
You mentioned setting KPIs based on cross-market benchmarks, which is good. But the next level is understanding why benchmarks differ and building a predictive model. For example:
- Russian micro-influencers: Higher engagement, lower conversion (cultural engagement patterns)
- US micro-influencers: Lower engagement, potentially higher conversion (audience intent)
This means your budget allocation should reflect expected outcomes, not just spend blindly across both markets.
One thing to stress: Benchmark data is only as good as its source. If you’re pulling from industry reports, verify they’re from comparable audience segments. A beauty benchmark for Gen Z won’t apply to millennial B2B audiences.
Your dashboard approach is solid. I’d add a cohort analysis layer—track not just overall ROI, but ROI by influencer tier (nano, micro, macro) and by content type (product placement, UGC, story takeover, etc.). This reveals which combinations work in which markets.
How granular are you getting with your cohort breakdowns, and are you seeing meaningful differences in performance by influencer tier?
I love that you’re focusing on the data side, because that’s what partnership decisions should be built on! But I want to add something from the relationship angle: standardized metrics also help you communicate better with influencers.
When you have clear, market-specific benchmarks, you can have honest conversations with creators about what “success” looks like for your partnership. Instead of vague expectations, you can say “Based on similar campaigns in your market, we’re targeting 6% engagement and 2% click-through rate.” This sets collaborative expectations and usually leads to better partnerships because there’s no surprise at the end.
I’ve also found that influencers respect brands that come prepared with data. It signals professionalism and serious intention for a real partnership, not just a one-off post.
Are you sharing these benchmarks with your influencers upfront, or keeping them internal?