I’m running some influencer pilot campaigns in the US right now for my relocation service, and I’m hitting a methodological wall. Back in Russia, I know all my benchmarks cold—conversion rates, cost per lead, LTV. I know what good looks like. But here in the US market testing phase? I have no idea if my early numbers are actually okay or if they’re a disaster.
The problem is more specific than just “different market.” It’s that I’m using partly Russian-based creators (who understand relocation logistics really well) and partly US-based creators (who understand the US audience). When I look at campaign performance, I can’t tell if weaker performance is because my messaging isn’t resonating, because the creators don’t fit the relocation niche, because US audiences need different positioning, or just because I’m new and have zero brand presence here.
I’ve been pulling some data from the hub and trying to find case studies from other founders who’ve done this, but everyone’s situation is slightly different. Has anyone actually built a reasonable comparison framework when you’re essentially testing in a market where you have no historical control? Like, how did you decide what metrics actually matter when everything is an experiment?
What would you use as a baseline if you couldn’t rely on your home market numbers?
Okay, this is a really solid question because it highlights the actual methodology problem. You can’t use your Russian benchmarks as a baseline for the US market—different regulatory environment, different creator economics, different audience demographics. So here’s what I’d actually recommend:
Build a two-layer comparison framework:
Layer 1: Industry benchmarks. Find published data on US influencer campaign performance in adjacent niches (relocation is small, but home services, moving, travel guides all have public data). What’s the typical engagement rate? What’s a reasonable conversion rate? This becomes your external baseline.
Layer 2: Your own cohort comparison. Run all your US pilot campaigns through August, then segment by: creator type (Russian-based vs. US-based), audience size (micro vs. mid-tier), content format (UGC vs. edited), messaging angle (cultural fit, logistics expertise, customer stories). Then compare performance within those segments. The segment that outperforms tells you what actually works in the US market.
The mixed creator approach is actually smart, but it makes measurement harder because you’re essentially running multiple experiments. Document that upfront. Don’t try to compare Russian-based performance directly to US-based—segment them separately, learn what each does well, then optimize.
What’s your current spend allocation across those creator types? That might shape how you stratify your analysis.
You’re dealing with a classic problem: you’re trying to measure effect when the system itself is unstable. Here’s the framework I’d use.
First, separate signal from noise. In a new market with a new brand and mixed creator types, noise dominates. Your early metrics are going to be all over the place. Accept that.
Second, choose a directional metric that’s more stable than conversion rate: engagement rate per dollar spent, or cost per engaged user. These are less dependent on audience familiarity with your brand and more reflective of whether creators are actually reaching interested people. If a US creator’s cost per engaged user is 30% higher than a Russian-based creator, that’s signal. That tells you something about audience targeting or content resonance.
Third, measure learning velocity. Your second month should perform better than your first month because you’re optimizing. If nothing’s improving—if you’re not seeing better metrics across any dimension—then you’ve got a market fit problem, not a measurement problem.
Fourth, accept that some of your campaigns are going to fail, and that’s the data you needed. A creator who underperforms isn’t a waste—they’re proof that, say, long-form educational content doesn’t move US relocation audiences. That insight is valuable.
How many campaigns are you running in parallel, and over what timeline?
I love that you’re thinking about this systematically. Here’s something I’ve noticed from a partnership perspective: a lot of founders in your position try to measure too much too early. You have maybe 4-6 weeks before you should have some directional clarity, not perfect data.
What I’d do differently: focus on creator feedback as much as campaign metrics. Like, sit down with the US-based creators and ask what felt off about the messaging, what resonated, what made them hesitant to market the campaign harder. Their gut instinct about why performance landed a certain way is often more valuable than the numbers themselves at this stage.
I’ve had founders realize through creator conversations that their messaging was technically resonating but felt inauthentic to US audiences—and that’s something the data alone wouldn’t tell them. The creator knew it, though.
Maybe set up some structured feedback calls with your best and worst-performing creators? That might clarify whether you’re measuring the right things. Happy to help you think through conversation frameworks if that feels useful.
I’m in month 3 of US influencer testing right now, so I feel your pain. Here’s what’s been weirdly helpful: I stopped trying to measure absolute performance and started measuring trends within segments.
So I’m tracking:
- Month 1 vs. Month 2 performance for the same creator (iteration signal)
- Russian-based creators specifically (since I know they understand product)
- US-based micro-creators (higher engagement potential)
- UGC vs. produced content performance
Then I’m asking: which segment improved most month-over-month? That’s where I’m doubling down.
Honest thing though: my month 1 data was pretty bad across the board. But month 2 was noticeably better. The improvement was signal that something was working. I don’t know my US benchmarks yet, so I’m using “am I getting better?” as my baseline instead of “am I hitting X%?”
Do you have the budget and timeline to run another month of testing? Because that comparison might be more valuable than trying to nail external benchmarks right now.
Smart approach segmenting creator types, but here’s something to layer in: before you compare anything, understand your attribution model. Are you tracking clicks? Actual conversions? SQLs? Cost per session? This is going to shape your entire baseline question.
In my experience with founders testing new markets, the most useful baseline is actually a peer baseline. Like, reach out to 2-3 other relocation services (even if one is in a different new market like Canada or EU) and ask for their early US performance numbers. Not exact data, just directional: “What’s your cost per lead running at in month 1-3?”
That gives you a reality check faster than benchmarks, because you’re comparing against someone solving the same problem, not generic influencer industry data.
Second, I’d actually suggest running a control campaign with a known-good performer (maybe a solid US micro-influencer doing something similar) alongside your experimental campaigns. That control gives you a baseline for what should work and makes it way clearer when one of your creative angles underperforms.
How much visibility do you have into what competitors are doing in terms of creator partnerships right now?