Building a data-driven attribution model for influencer campaigns when you're stuck between two markets—where do I actually start?

Стратегии инфлюенс-маркетинга (RU)
1

I’ve been thinking about this problem for a while now. We’re a Russian SaaS company trying to build a real presence in the US, and our first instinct was to hire influencers to build awareness. But the moment we did, I realized we had no way to prove the ROI to our board—and worse, we had no framework to decide which influencers to invest in next.

The problem is that there’s a ton of advice out there about influencer ROI, but most of it assumes you’re working in one market with one set of audience expectations. We’re not. We’re bridging two completely different marketing cultures and audience behaviors.

I started researching what other founders have done, and I found a few people who seem to have cracked this. They’re using bilingual experts and cross-market frameworks that actually account for the differences instead of pretending they don’t exist. But I don’t have a playbook yet.

Here’s what I’m trying to figure out:

  1. Should I build two separate attribution models from day one, or try to find commonality first? I’m worried that if I silo things, I won’t be able to make strategic decisions across both markets.

  2. What metrics actually matter for comparison? CAC makes sense, but CAC in rubles is different from CAC in dollars, and that’s before we even account for market differences in purchase behavior.

  3. How much of this is proprietary/company-specific, and how much can I borrow from other founders? Are there actual frameworks or templates that work for cross-market influencer ROI, or is everyone building from scratch?

  4. At what point does it make sense to bring in external expertise? A US-based marketing consultant who understands both markets sounds valuable, but I’m not sure if that’s premature for our stage.

I’m hoping to learn from people who’ve actually built something similar. What did you do differently that made the attribution problem manageable instead of impossible?

2

I appreciate how directly you’ve framed this. Most founders dance around this problem; you’re actually naming it, which is half the battle.

Here’s my honest take: you should NOT try to find commonality first. That’s a trap. Instead, build two separate models from day one, but give them a unified reporting layer on top. Here’s why—trying to force commonality in the model will lead to bad data, which leads to bad decisions, which costs you way more than the effort of maintaining two systems.

The unified layer is where you actually compare them. You’ll report something like: “Channel A (Russian influencers) is operating at X efficiency, Channel B (US influencers) is operating at Y efficiency, here’s why they’re structurally different, and here’s the combined story for the board.”

On metrics: CAC is fine as a starting point, but you need to go deeper. Track these in parallel across both markets: customer acquisition cost, customer lifetime value, cohort retention rates (by acquisition source and market), and repeat purchase rate. When you see which influencers are driving high-quality, repeatable customers versus one-time buyers, you’ll have real insight.

About templates: there’s no magic framework that works out of the box. But the structure is repeatable. You need: (1) clear conversion definition, (2) attribution window per market, (3) cost tracking, (4) outcome tracking, and (5) a monthly review process. Everything else is implementation details.

On external expertise: hire it now, not later. Not a full-time hire, but someone who’s done this before—either a consultant or a fractional CMO who’s managed cross-market campaigns. The ROI on that investment is massive because they’ll help you avoid the expensive mistakes I see founders make constantly.

3

Mark’s absolutely right about the two-model approach, and I’d add something from my experience: the key to making this work is having ONE source of truth for your data, even if the models diverge.

What I mean: all your raw campaign data (spend, impressions, clicks, conversions) should live in one place—ideally a data warehouse or even a well-structured Google Sheets if you’re early stage. Then, on top of that unified data layer, you build your two market-specific attribution models.

This way, when your board asks, “How much did influencer campaigns contribute to revenue across both markets?” you can give a clear answer. But when you’re making tactical decisions about which influencers to hire next, you’re using market-specific logic.

Also, about templates: I built one for my current company, and I’m happy to share structure (not the proprietary bits, obviously). The core framework is: define your conversion event, choose your attribution window, track every touchpoint, then model it. The hard part is choosing the right attribution model—first-touch, last-touch, multi-touch, etc. I’d recommend multi-touch with a market-specific weight distribution. For Russian audiences, I weight more toward mid-funnel awareness. For US audiences, I weight more toward conversion-focused interactions.

4

On currency: don’t convert everything to one currency in your model. Keep spend and revenue in native currencies, then compare efficiency metrics (CAC, ROAS, etc.) separately. This removes the noise of exchange rate fluctuations and keeps your focus on actual marketing performance.

5

Here’s the framework I use for my clients, and it’s been pretty scalable:

Phase 1 (Weeks 1-2): Define your conversion event and attribution window. Keep it simple at first.

Phase 2 (Weeks 3-4): Set up tracking infrastructure. UTM parameters on every link, promo codes, pixel-level conversion tracking if you can.

Phase 3 (Campaign 1): Run a small test campaign in each market with 3-5 influencers. Track everything.

Phase 4 (Analysis): Look at the data. Which influencers drove qualified leads? Which drove volume but low quality? Build your model based on what you actually see, not what you theorized.

Phase 5 (Scale): Use those insights to allocate bigger budgets.

The key is: don’t try to build the perfect model before you have data. Build a rough framework, get real data, then iterate.

About hiring external help: if you can afford it, do it. If you can’t, at least do a consulting sprint with someone who’s done this before. Even 2-3 days of their time will save you months of spinning.

6

I want to add a creator’s perspective here, because I think there’s something important that gets missed in these discussions.

When a brand comes to me with a really unclear attribution model or asking me to hit metrics that don’t make sense for my audience, I either ghost them or negotiate hard on rate. It’s because I know the data they’re using is probably not even real—it’s guesses or benchmarks from other markets.

What would actually help you most is being transparent with influencers about what you’re measuring and why. When I know a brand is genuinely trying to measure ROI instead of just guessing, I actually try harder to deliver results—not because I’m being manipulated, but because I respect the professionalism.

So when you build your model, keep in mind that creators will either buy in or opt out based on whether the metrics feel realistic. If you’re asking a micro-influencer in Russia for a 5% conversion rate when they know their typical rate is 1-2%, they’ll know your model is broken.

Talk to creators. Let them inform your benchmarks. Build models that actually match reality instead of fantasy. That’s what will make this work.