I’ve been wrestling with this problem for months now. We started running influencer campaigns in the US market using our basic discovery tools, and they worked okay. But when we tried to expand into Russia, everything fell apart. The metrics don’t translate. An influencer with 100k followers in Moscow isn’t comparable to someone with the same following in New York. Engagement rates vary wildly. Fake followers are harder to spot across different platforms and regions.
Here’s where I’m stuck: I built a basic scoring matrix for US influencers—engagement rate, audience quality, brand fit, historical performance. But when I tried applying the same weights to Russian creators, it completely broke down. The engagement expectations are different. The platforms are different (VK vs. Instagram). Even audience behavior is different.
What I’ve realized is that you can’t just apply one framework globally and hope it works. I’ve been testing a bilingual approach where I weight the same authenticity signals differently based on region—but I’m honestly not confident it’s catching the right mix of creators.
I’m curious: are you scaling across multiple markets right now? How are you standardizing influencer quality without losing regional nuance? Are you using AI to help normalize these metrics, or is it mostly manual adjustments based on what you learn from each market?
This is exactly where most people fail with international expansion. The problem isn’t your scoring matrix—it’s that you’re treating engagement rate as if it’s a universal truth. It’s not.
Here’s what I found when we scaled our e-commerce campaigns: Russian audiences engage differently because of platform culture, time zones, and content preferences. Instagram engagement in Russia regularly hits 5-8% on quality accounts, while US audiences tend toward 2-3%. If you’re comparing them directly, you’ll reject viable Russian creators and overpay for mediocre US talent.
What actually works: Build region-specific benchmarks first. Segment your data by platform AND geography. Then create relative scoring instead of absolute thresholds. A 6% engagement rate in Russia might be equivalent to a 3.5% rate in the US, not because the metrics are different, but because the baseline expectations are.
The bilingual approach you mentioned—weighting signals differently—is on the right track, but you need to validate your weights with actual campaign performance data from both markets. Don’t assume; measure. Run 10-15 small test campaigns, track the ROI by influencer quality tier, then reverse-engineer your weights from the results.
How many campaigns have you run in each market so far? That’s your actual ground truth.
One more thing: don’t forget audience composition analysis. A Russian influencer might have 80% audience from Russia, 15% from other Cyrillic-speaking countries, and 5% international. Meanwhile, a US influencer’s audience is probably 70% US, 20% English-speaking countries, and 10% random international.
If your campaign targets Russian consumers, that Russian influencer’s reach is actually more concentrated and valuable. But if you’re only looking at follower count and engagement rate, you’ll miss that. Pull audience demographic data—location, language, interests—and build that into your matrix.
I’d be curious what AIs you’re using to automate this vetting process, because most tools don’t account for regional audience composition out of the box.