I’ve been trying to build confidence benchmarks for influencer campaigns across Russia and the US, and I keep hitting the same wall: I have good historical data from campaigns we’ve run, but I don’t have any way to know if we’re actually performing well relative to what’s normal in each market.
Doing a campaign with a 100k-follower creator and getting 5% engagement—is that good or mediocre? Depends on the market, the platform, the niche, the time of year. But I don’t have solid external data to check against.
I’ve looked for industry benchmarks, but most of what I find is either:
- Too generic (“average engagement on Instagram is 3%”) and doesn’t account for creator size, country, platform nuance
- From competitors or consultants who won’t share exact numbers
- Outdated or based on questionable methodologies
The problem gets worse when I try to compare across markets. A creator performance that’s “good” in one region might be “average” in another just because of platform saturation, algorithm differences, or audience size expectations.
I know there are other people scaling internationally who are dealing with this. Are you building benchmarks from your partner network? Using some kind of industry consortium? Finding external analysts who have cross-market data? Or are you just working from your own historical data and accepting you might be flying blind?
I need a way to validate that our spending decisions make sense, and right now I feel like I’m guessing.
The hard truth: there’s no perfect external benchmark database for influencer performance because the variables are too specific. Creator size, niche, platform, audience quality, posting frequency, creative quality, timing—all of it matters.
But here’s what actually works: build your benchmarks inductively from your own data and from conversations with people in the community who operate in your space.
For internal benchmarks:
- Segment campaigns by creator size (micro <100k, mid 100k-1m, macro 1m+), niche, and platform
- Calculate median engagement, click-through rate, and conversion rate for each segment from your historical campaigns
- Update quarterly as you get more data
For external validation:
- Find 3-5 people in the bilateral community who work in similar niches and markets
- Share your benchmarks with them anonymized. Compare. See where you’re outliers
- If your micro-influencers are outperforming everyone else’s, you either have an advantage (good relationships, smart targeting, creative) or you’re measuring wrong
I’ve been doing this for about a year, and the benchmarks stabilize after 15-20 full campaigns per segment. Before that, it’s just noise.
The real power comes when you realize your benchmarks are your competitive advantage. If you know that you consistently get 7% clicks from creators performing at the 50th percentile when the market average is 4%, that’s you being better, or that’s you reporting metrics differently. Either way, knowing that gap is valuable.
What segments are you tracking?
One tactical thing: at the end of every quarter, I’m not just looking at ROI—I’m looking at variance. Some campaigns hit my benchmark, some outperform, some underperform. The ones that outperform, I analyze hard. Was it the creator? The niche? The timing? The creative? Once I understand what moves the needle, I can isolate benchmarks further.
Example: I thought all Fashion creators on my list were equivalent. But when I segmented by “audience location,” I realized creators with more concentrated Moscow audiences outperformed by 25%. That changed everything about how I’m strategizing for Russia.
That’s the level of granularity you need before you can trust your benchmarks and especially before you compare across markets.
Анна’s approach is solid, but I want to add a structural comment: you’re looking for benchmarks because you’re trying to validate decisions. But benchmarks are backward-looking. What you actually need is a predictive model.
Here’s the difference: a benchmark tells you “creators like this usually perform at X level.” A predictive model tells you “given these specific inputs (creator size, audience quality, niche, platform, content type, timing), this campaign will likely perform at Y level with Z uncertainty.”
You build a predictive model by accumulating data and finding relationships. After 30-40 campaigns across both markets, you’ll start seeing patterns. Creator with 80k followers, beauty niche, Instagram: you can predict engagement range within reasonable bounds.
For cross-market prediction: the model has to include a “market multiplier.” A creator in Moscow with 50k followers probably reaches fewer total people than a creator in LA with 50k followers, just because of market saturation. That multiplier becomes part of your model.
Do this well, and you can actually forecast ROI before you hire the creator. That’s when you move from reactive measurement to proactive strategy.
I solve this by being part of what I call a “benchmarking consortium.” It’s 8-10 non-competing agencies and brands that share anonymized campaign data monthly. We all get access to the aggregated benchmarks, which is way more robust than any single company’s data.
There are a few of these groups online if you know where to look. Some are industry-specific. Some are just “agencies working internationally.” The value is insane because you’re comparing against real-world performance, not theoretical models.
Second solution: hire an external analyst for a quarter. Someone who’s done this work for multiple companies. They come in, validate your methodology, help you build proper benchmarks, document it, and leave. It costs $5-10k but saves you from building a broken model.
Third solution: leverage the bilateral community more directly. There are people here running campaigns in both markets right now. Form a small group, share data (obviously sanitized), and build shared benchmarks. It’s way more powerful than trying to do it alone.
We brought in advisors from both markets when we were building our expansion benchmarks, and that was huge. Not consultants selling a solution—actual founders who’d scaled in both regions. They told us which metrics matter (different in each market, by the way) and which are noise.
They also told us plainly: your first 10 campaigns in the new market aren’t useful for benchmarking. You don’t know the market well enough yet. So we ran those as learning campaigns, not measured against any standard. At campaign 11, we had a baseline.
Might sound wasteful, but it prevented us from making bad allocation decisions based on bad data.
If you don’t have advisors yet, this community is the place to find them.
I love that you’re being systematic about this because it changes how you partner with creators too. When you have real benchmarks, creator negotiations go faster. You can say, “based on your size and niche, here’s what we expect your contribution to look like.”
Also, creators themselves often know the benchmarks better than brands do. They see performance across clients. They know what realistic numbers look like in their market. Tap into that. They’ll actually help you build better benchmarks if you ask directly.
I’ve facilitated conversations where a creator and a brand aligned on what success looks like based on real market data, and it changed everything about that partnership.
Real talk from the creator side: I know exactly what my engagement benchmarks are because I track my own performance constantly. When brands benchmark me against numbers from 2022 or against other creators in totally different niches, it’s obvious they don’t have good data.
If you’re building benchmarks, talk to the creators. We know what works in our audiences. We know what hashtags are saturated. We know what platforms are dead weight. We can tell you if your benchmarks are realistic or if you’re setting creators up to fail.
Also, benchmarks change fast. What was true for TikTok engagement six months ago is completely different now because of algorithm shifts. You build competitive benchmarks by staying in conversation with people actively posting, not by looking at historical data alone.