I’m running into a fundamental problem that I haven’t found a good answer for yet.
We’re trying to optimize influencer selection across Russian-speaking and English-speaking markets, and we’re using AI to benchmark influencers—comparing their metrics, predicting performance, that kind of thing. But I keep running into situations where the numbers tell one story and reality tells another.
Example: I have a Russian influencer and a US influencer with almost identical follower counts (both around 200k) and similar engagement rates (both around 3%). But when I actually look at the data more carefully, the Russian creator’s followers are mostly in Russia and neighboring countries, while the US creator’s followers are spread across North America with some international reach. The comment quality and sentiment are different. The posting cadence is different. The brand partnerships they’ve done in the past are in completely different categories.
So when I benchmark them against each other to predict which one would perform better for a particular campaign, I’m essentially trying to compare apples and oranges while the AI is telling me they’re both just “medium-engagement accounts.”
I think the real issue is that I’m treating benchmarking as a one-size-fits-all metric, when really I need market-specific benchmarks. But then how do I actually use that data for cross-market strategy? Do I abandon the idea of comparing creators across markets? Or do I need a completely different framework?
I’m especially curious about how other people handle this when you’re trying to scale a campaign across both markets simultaneously. Do you create separate strategies per market and then try to connect the dots? Or do you have a unified approach?
You’ve identified the core problem perfectly. Here’s what we’ve learned: stop trying to compare influencers directly across markets. Instead, compare them within their markets using local benchmarks, and then evaluate them based on how well they fit your campaign objectives.
What works for us is a two-tier approach:
- Local optimization: find the best influencers in each market using market-specific benchmarks
- Strategic fit: evaluate whether those local winners align with your unified brand message
For example, if you’re running a product launch campaign across both markets, you’re not looking for “equivalent” influencers—you’re looking for influencers in each market who can deliver your message authentically to their local audience. A Russian creator with 200k followers and 3% engagement might be your strongest bet in Russia, while a different US creator with similar metrics is your strongest bet in the US. They don’t need to be comparable; they need to be locally excellent.
That said, there ARE moments where cross-market comparison matters. If you’re scaling a proven campaign from one market to another, then yes, you want to find someone with similar characteristics to your original performer. But even then, I’d use that original creator’s data as a reference point rather than as a constraint. Your new creator should have proven performance in their local market, not just similar-looking metrics.
From my perspective as a creator, this makes total sense. My audience is different from a creator in Russia even if our numbers look similar. I grew my following in a specific way, at a specific time, with specific content. That history matters. And honestly, what works for me in terms of brand partnerships is totally dependent on my community’s interests and trust.
I think you need to ask creators more direct questions about their audience composition, not just rely on the metrics. Where are their followers from? What industries have they successfully worked with? What’s their engagement quality like? That information won’t show up in a spreadsheet benchmarking exercise, but it’s crucial for actually predicting campaign success.
You’re thinking about this correctly, but you need to formalize it into a statistical framework. Here’s what I’d recommend:
Don’t benchmark creators against each other. Benchmark campaign performance vectors.
What I mean: instead of “influencer A vs. influencer B,” ask “which influencer’s audience distribution best matches the geographic and demographic profile of our target customer in this market?” That’s a completely different question.
For cross-market strategy, you need:
- Clear target customer profiles per market (geography, interests, spending power, etc.)
- Influencer audience composition data that maps to those profiles
- Historical campaign data showing which audience types convert best for your product category
Then, use AI to match influencers to target profiles, not to compare influencers to each other. A Russian creator whose audience skews Moscow-based professionals might outperform a US creator with identical metrics if your target customer is literally Moscow-based professionals.
The benchmarking doesn’t go: creator A vs. creator B.
It goes: creator A’s audience vs. your target profile in market A.
One practical tip: collect campaign performance data with clean attribution per market. Over time, you’ll build reliable predictive models because you’ll have real outcomes to validate against. Right now, you’re trying to benchmark without enough ground truth data. Start small, measure everything, and let your historical campaign data inform future benchmarking.