I think one of the biggest mistakes I made early on was waiting until I had a full strategy before testing anything. We spent weeks planning a US market entry, figured out budgets, selected creators, designed campaigns—and then watched half of it underperform.
The real issue: we didn’t actually know what would work in the US market. We were making assumptions based on what worked in Russia. Market dynamics are different. Audience expectations are different. Media consumption is different. And we didn’t find that out until we’d already committed the budget.
Now we do something different. Before we commit serious money to expanding into a new market or launching with a new audience segment, we run small tests. Like, really small—$1000-2000 budget across 5-10 creators, one round of UGC content, see what sticks.
The learnings from these small tests change everything. Sometimes the angle we thought would land completely misses. Sometimes it’s not the content but the demographic we’re targeting. Sometimes it’s the media format—video converts better than carousel, or vice versa.
The barrier I see for most teams (including myself) isn’t capital—it’s psychology. We want to have it figured out before we test. We want the plan to be right. But that’s not how markets work. You have to be willing to be wrong, cheaply, repeatedly.
What’s actually preventing you from running these validation tests? Is it the budget investment, the time overhead, or something else? And when you have run small tests, how have they actually changed your strategy?
You’re naming something really important—the psychology of certainty before action. Most brands want to have all their ducks in a row before they move, but markets don’t reward that approach.
I always tell my brand partners: small tests are your cheapest insurance policy. $1500 test might save you from a $50k campaign that doesn’t work.
One thing I’d add: share your test results with creators. When you’re learning what resonates in a new market, bring creators into that learning. They often have insights about their audience that change how you approach the next test. It builds collaboration instead of just transactional work.
This is exactly why I recommend brands invest in relationships with creators in new markets early. The creator can tell you what will resonate before you even spend budget on production. They’re basically your market research.
Small test campaigns are gold, but don’t run them in isolation. Talk to the creators about what they see. Get their read on the market. That qualitative data is just as important as the performance numbers.
Have you found that creator feedback from tests changes your hypothesis for the next test?
The data supports this approach. Brands that run 2-3 small test campaigns before a major expansion typically see 35-50% better ROI on their eventual full-scale campaign compared to those that don’t validate.
But here’s what I’d challenge: you need to be systematic about what you’re testing. Each test should answer a specific hypothesis. Test 1: audience hypothesis. Test 2: content angle hypothesis. Test 3: platform/format hypothesis.
If you’re running random tests, you’ll accumulate data but not insights. You’ll stay stuck in trial-and-error mode.
What specific hypotheses are you testing in your cross-market pilots? Are they sequential, or are you testing multiple variables at once?
The psychology you’re describing is real, and I see it in every team that struggles with expansion. But data-wise, here’s the cost of not testing:
Failing at scale (committing $50k and getting 2% ROI) costs $49k in losses. Testing at $2k costs $150 in learning. The math is obvious, but teams fight it.
For cross-market UGC specifically, I’d recommend:
- Test 1: Does this audience respond to UGC at all? (format/authenticity validation)
- Test 2: Which content angle resonates? (message/positioning validation)
- Test 3: Which creator profile works? (creator-market fit validation)
Each test informs the next. By test 3, you know way more.
How are you currently structuring sequential tests, or are you running these in parallel?
This hits home. We’ve done exactly what you’re warning against—we had a plan, we executed, and half of it didn’t work because we were building on assumptions.
The testing approach makes sense, but I’m curious about the execution. How do you actually stay organized and learn from multiple small tests without it becoming chaotic? Like, we’ve run tests, but then we get pulled back to core business and lose the momentum of learning.
Also, when a test underperforms, how do you actually decide: is this a bad market, bad creator fit, or bad content hypothesis? How do you isolate what failed?
This is the difference between strategic brands and reactive brands. Strategic teams test and learn. Reactive teams guess and pray.
What I’d add: use tests to also evaluate your sourcing process. If you’re testing 10 creators and only 2 perform well, maybe your sourcing criteria needs refinement. That feedback loop is where real market understanding develops.
For cross-market specifically, I’d test with creators who already have some audience diversity if possible. It tells you if they can code-switch effectively, which is crucial for bilingual/multi-market work.
How are you currently selecting which creators to test with in new markets?
From a creator’s perspective, I actually love when brands want to test first. It feels less risky for me too—like, I know this could be a fit, but we’re exploring together instead of committing blindly.
The thing I notice: brands that test well are usually transparent about what they’re learning. They’ll tell me “this worked, this didn’t” and ask for my take. That collaboration is so much better than just getting feedback like “needs to be different.”
When you’re running these tests, are you giving creators honest feedback about performance? That teaches us too.
I think the barrier for most teams isn’t actually money—it’s fear of being wrong publicly. Like, you’re worried that if you test and it underperforms, people will judge you for spending budget on something that didn’t work. But that’s literally how learning works.
As a creator, I’m more interested in working with brands that are willing to experiment and iterate than brands that have everything perfectly figured out but are rigid. The experimental mindset usually leads to better partnerships.
Have you found that your team gets more creative and engaged when you frame tests as learning rather than as “do this perfectly”?
You’ve identified a critical strategic insight: the cost of wrongness decreases exponentially when you test early. A $2k test that saves you $40k in losses is 20x ROI on the testing investment itself.
But teams often don’t track this. They think “we spent $2k on tests” without calculating “we avoided $40k in losses because of those tests.”
For cross-market validation specifically, I’d structure tests like this:
- Audience segments to test (narrow, specific cohorts)
- Content hypotheses per segment
- Creator-market fit assessment
- Platform/format optimization
Then move budget from tests to winning segments.
Are you tracking the opportunity cost of not testing, or just the direct cost of tests?