Building a cross-market influencer vetting scorecard that actually works for both US and Russian audiences

DTC & UGC Dynamos
1

We finally bit the bullet and built a custom vetting scorecard for creators we want to work with across both markets. And honestly? It’s one of the best decisions we’ve made this year.

Here’s why we needed it: our standard evaluation sheet was designed for US creators. We were using it on Russian creators too, and it was like trying to wear one shoe that doesn’t fit both feet. Engagement benchmarks were different, audience demographics worked differently, content norms weren’t the same.

What went into our scorecard:

Audience alignment (same framework, different thresholds)—Does the creator’s audience match our target demo? For US, we look at geographic reach and interests. For Russia, we also look at region within the country, city concentration, because that matters way more than it does in the US market. Different data points, same goal.

Content quality—This one actually is pretty universal. We assess production value, consistency, authenticity. But the “style” of authentic differs. Russian creators often build trust through longer-form, narrative content. US creators lean toward short, rapid-hit content. We score them by their own market standards, not ours.

Brand safety—This is where it got tricky. We had to sit down with people who actually understand the cultural context. What’s a red flag in Moscow might be normal in New York, and vice versa. We brought in local team members to help calibrate this section.

Performance history—Past campaigns, honest feedback from other brands, growth trajectory. This one is pretty straightforward and works the same way everywhere.

Engagement authenticity—This was the hardest one. We’re looking at comment quality, audience interaction patterns, any signs of inflated metrics. But again, we had to adjust for market. What looks like engagement in one audience might be normal rapport-building in another.

The scorecard isn’t perfect. But it’s helped us be consistent and intentional about who we partner with, rather than just relying on intuition or random metrics.

Has anyone else built something like this for bilingual or cross-market work? What did you actually include, and what did you leave out because it didn’t matter?

2

Отличный подход к системе! Статистически говоря, это имеет смысл—вы создали локально-оптимизированные критерии вместо глобальной одеяла.

Мы пошли похожим путём, но добавили ещё один слой: weighted scoring. Не все критерии равны для всех типов кампаний. Для performance marketing—engagement authenticity и audience alignment вес 40%, контент 20%. Для brand awareness—контент 35%, engagement 30%.

Итоговый результат: система даёт score от 1 до 100. Мы никогда не работаем с creators ниже 65, за редкими исключениями для специальных кейсов.

Дальнейший вопрос: как вы калибруете локальные пороги? Есть ли у вас минимальные engagement rates для Russian vs US creators, или вы используете относительные ранкинги внутри каждого рынка?

3

Супер кейс! Я возьму это на заметку. В моей работе часто проблема в том, что люди используют одну шкалу для всех, или, наоборот, не имеют вообще никакой шкалы.

Что я делаю при встречах с creators: интуитивно проверяю примерно то же самое, но без формальной структуры. Ваша идея—это как раз то, что нужно формализовать. Особенно когда работаешь с несколькими людьми в команде.

Вопрос: когда вы встречаетесь с creator первый раз, используете ли вы этот scorecard сразу, или сначала знакомитесь, а потом применяете? Боюсь, что если сразу доставить им список критериев, может быть странно…

4

Мы в ранней стадии запуска на разные рынки, поэтому пока у нас нет формального scorecard. Но ваш пример показывает, что нам нужно снизить неопределённость в процессе отбора.

Пока мы выбираем creators интуитивно—смотрим на их контент, разговариваем, чувствуем. Но когда начнём масштабировать, это не будет работать.

Вопрос: сколько времени у вас ушло на то, чтобы построить этот scorecard с нуля? Это был процесс “сядьте, подумайте, напишите” или вы учились через пилоты, видели, что работает, а что нет?

5

This is operational excellence. We’ve done something similar, though our version is a bit more streamlined. Here’s what we learned: keep the scorecard simple enough that you can actually use it, but structured enough that it forces you to think about what matters.

Our version: 5 core criteria, each scored 1-5, weighted differently by campaign type. Takes maybe 10 minutes per creator to evaluate properly. We found that anything more complex becomes a checkbox exercise instead of actual analysis.

Big insight for cross-market work: don’t try to make the scorecard culture-neutral. Make it culture-aware. Explicitly acknowledge that Russian creators might have different engagement patterns, different audience types, different content norms. Build that into the scoring logic itself.

One thing you might add: track how often your scores correlate with actual campaign performance. If creators you scored 80+ consistently deliver and 60-70 creators mostly underperform, your calibration is working. If not, adjust.

Are you building feedback loops back into your scorecard, or is it pretty static once you’ve defined it?

6

I love that you’re being intentional about this, but I want to offer the creator perspective: make sure this scorecard doesn’t just measure metrics. Because metrics can be gamed or look weird for legitimate reasons.

Like, what if a creator has lower engagement because their audience is actually really selective? Or what if they post less frequently because they’re focused on quality over quantity? Those could be huge strengths, but they might score lower on a pure numbers scorecard.

I’d say: definitely have metrics. But also build in a “why” element. Why does this creator do what they do? What’s their philosophy? Because sometimes the best creators to work with are the ones who are thoughtful about their boundaries, not just maximizing every stat.

Also—different markets might value different things. Russian creators I know tend to be more relationship-focused, build deep connections. US creators might be faster to turn and more transactional. Neither is better, just different approaches. Make sure your scorecard values both styles.

7

You’re approaching this the right way, but I’d push you one level deeper: what specific business outcomes are you trying to predict with this scorecard?

Because the criteria you choose should map directly to campaign outcomes. If your goal is direct sales, engagement authenticity and audience alignment matter most. If it’s brand awareness, reach and content quality might be more important.

What we’ve found: generic scorecards don’t work as well as outcome-specific ones. We actually have three versions—one for awareness, one for engagement, one for conversion—and we choose which scorecard to use based on the campaign type.

For cross-market work: absolutely build in market-aware thresholds. But also build in outcome-aware weighting. A Russian creator might score differently when you’re doing a US-targeted campaign vs. a Russian-targeted campaign, depending on what you’re actually trying to achieve.

Second question: how are you validating that this scorecard actually predicts performance? Are you tracking correlation between scores and campaign results? If not, you should—that’s how you know if your system is actually useful or just organized.