I think I’m going crazy. We just wrapped an influencer campaign that ran simultaneously in Russia and the US. On paper, both had similar budgets, similar influencer tiers, and comparable audience sizes.
Russia: 8.2% engagement, 340 conversions, 3.2x ROAS.
US: 2.1% engagement, 120 conversions, 0.9x ROAS.
Same strategy. Different planets.
I’ve been trying to figure out what’s actually different. Is it the creators? The audience? The product positioning? The attribution model? Or am I just measuring things completely wrong?
Every time I dig into the data, I find something new that doesn’t match up. Engagement rates are tracked differently. Conversion definitions are inconsistent. The sales cycles might actually be different. Platform algorithms favor different content types. I honestly can’t tell if the US campaign genuinely underperformed or if I’m comparing apples to oranges.
I need to build a system where I can actually trust my cross-market metrics. Right now, I can’t justify anything to my leadership because I can’t confidently say whether the issue is strategy, execution, measurement, or just market reality.
How do people actually standardize ROI measurement across markets? What framework do you use to make metrics comparable when markets are fundamentally different?
Okay, this is the exact problem I’ve spent the last year solving. The frustrating truth: your metrics aren’t wrong, they’re probably measuring different things.
Let me break down what I found:
Engagement patterns by culture:
- Russian audiences tend to engage faster and more emotionally (quick reactions, comments)
- US audiences engage differently (saves, shares, but sometimes takes longer)
- Platforms weight these differently per market
Result? Your 8.2% vs 2.1% engagement might both be healthy—you’re just measuring different behavior patterns.
Here’s how I fixed it:
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Abandon “engagement rate” as a cross-market metric. Instead, track:
- Time to first engagement (measure speed)
- Engagement depth (likes vs comments vs shares — these matter differently by market)
- Conversion point (where does action happen in the funnel?)
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Define conversion consistently. Is it:
- Click from social → landing page?
- Landing page → product page?
- Product page → purchase?
- Repeat purchase?
These have wildly different timelines by market. Russia often converts faster; US might take 3-7 days.
-
Attribution windows. This killed me. We were using 7-day attribution for both markets. Switched to 1-day for Russia, 3-day for US. ROAS suddenly made sense.
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Build market-specific benchmarks instead of comparing directly. Track your own Russia performance against your own Russia history. Track US against US history. Don’t compare Russia 3.2x to US 0.9x—compare Russia 3.2x to “is this up or down from last quarter?” and US 0.9x to “is this improving or declining?”
The framework I use now:
- Universal metrics: Cost per acquisition, customer lifetime value, repeat purchase rate (these transcend markets)
- Market-specific metrics: Engagement rate, ROAS (because conversion paths differ)
Do you know your customer acquisition cost in each market independently? That might be your answer.
We had this exact same problem. Spent weeks trying to figure out if our US campaign was actually failing or if we were just measuring wrong.
Turned out: both. The campaign underperformed and we were measuring it badly.
Here’s what we discovered:
Russia: Our influencers post on Thursday evening, followers engage immediately (Friday morning scroll), and conversion happens within 24 hours. Average customer takes 1.2 days to buy.
US: Influencers post Tuesday-Wednesday, but followers don’t engage until evening/night their time (so it’s delayed for us). Conversion takes 3-5 days. Average customer takes 5+ days to buy.
We were using the same 24-hour attribution window for both. Missed like 60% of US conversions because they happened on day 3-4.
What fixed it:
- Switched attribution windows by market (24h Russia, 72h US)
- Started tracking engagement time zone adjusted so we could see the real pattern
- Stopped trying to make ROAS match between markets—instead checked if each market was improving independently
After that adjustment, US campaign actually showed 2.1x ROAS once we counted full attribution.
Biggest lesson: different markets have different customer behaviors. Your metrics need to reflect that, not ignore it.
One more thing—we also realized our US influencers picked the wrong posting times. We tested afternoon posts and engagement went up 35%. Small thing, but it mattered.
What attribution window are you currently using for both?
This is a systems problem, not a measurement problem. Let me walk you through the right framework.
Layer 1 — Standardize Definitions:
First, audit what “conversion” means in each market. Different products have different paths. E-commerce? SaaS? B2B? The definition changes.
For your situation, I’d recommend:
- Define conversion at identical points in both funnels (e.g., both = customer completes first purchase)
- Track the path to conversion separately (Russia might be 1-day; US might be 5-day — document this)
- Report both as valid, not as comparable numbers
Layer 2 — Create Market-Specific Baselines:
Don’t compare Russia to US. Instead:
- What’s YOUR historical Russia performance? (baseline = previous campaigns)
- What’s YOUR historical US performance? (baseline = previous campaigns)
- Is this campaign beating or lagging your own historical performance in each market?
This removes the “why don’t they match” problem entirely.
Layer 3 — Attribution Windows (This Is Critical):
Your 0.9x ROAS in US screams “wrong attribution window.” Most cross-border campaigns default to 7-day, but:
- E-commerce US: 1-3 days (fast purchase cycles)
- SaaS US: 7-30 days (consideration happens)
- Russia: Often 1-2 days (faster impulse purchases)
You probably need 3-day or 7-day for US, 1-day for Russia.
Layer 4 — The Dashboard Structure:
Metric | Russia YTD | Russia Last Campaign | Variance | US YTD | US Last Campaign | Variance
CPA | $X | $X | +/-% | $Y | $Y | +/-%
ROAS | 3.2x | 2.8x | +14% | 0.9x | 1.1x | -18%
CAC Payback | 1.2 mo | 1.5 mo | ↑ faster | 2.8 mo | 2.5 mo | ↓ slower
You’re not comparing Russia to US; you’re tracking each market independently against its own history.
One final thought: Get a data analyst on your team or outsource this to someone who specializes in cross-market attribution. This isn’t something you can accurately manage in a spreadsheet. The time you’ll save in clarity is worth the cost.
What’s your current attribution tool setup?
I work with a lot of brands on this, and here’s what I’ve noticed: different markets have different influencer dynamics too.
In Russia, influencers often have super-engaged, tight-knit communities. Smaller follower counts, but intense loyalty. Engagement is immediate and authentic-feeling.
In the US, influencer dynamics are different. Larger follower counts, but engagement patterns are more spread out. It feels less intimate.
So when you compare metrics, you’re not just comparing audience behavior—you’re comparing different types of influencer-audience relationships.
This affects ROI in ways that pure metrics don’t capture. A Russian macro-influencer with 200K followers might deliver authentic engagement with 8% rates. A US influencer with 200K followers might deliver wider reach with 2% rates but better conversion downstream.
What I recommend:
- When briefing influencers in each market, be explicit about what you’re optimizing for (engagement? reach? conversion?)
- Track quality metrics, not just numbers (sentiment, audience relevance, repeat engagement)
- Build relationships with influencers in each market so you understand their audience better
I’ve actually found that direct conversations with influencers about their audience behavior can teach you more than any analytics dashboard.
Want to set up a call to talk through this? Sometimes it helps to map out the influencer landscape in each market.
This is a classic cross-market problem I see constantly. Here’s my systematic approach:
Step 1: Audit Your Current System
- What tools are tracking what?
- Are the definitions identical across tools?
- What’s your attribution window per market? (This is usually where everything breaks)
Step 2: Establish Ground Truth for Each Market
- Run one perfect metric per market independently
- For you, I’d suggest: Customer Acquisition Cost (CAC). This is harder to misinterpret.
- Russia CAC: $X. US CAC: $Y. These might be different—that’s okay.
- Track whether each is improving or declining over time.
Step 3: Build a Comparative Framework
- Stop trying to compare raw numbers (3.2x vs 0.9x)
- Instead, compare performance trends
- Is Russia ROAS up or down quarter-over-quarter?
- Is US ROAS up or down quarter-over-quarter?
- Both improving? Both declining? You have actionable insight.
Step 4: Segment Your Reporting
- Market-specific reports (Russia report shows Russia data + Russian benchmarks)
- Separate reporting (not comparative)
- Executive summary (which markets are improving, which need attention)
Step 5: Implement Properly
- Use a tool like Mixpanel, Amplitude, or hire a data engineer
- Don’t try to spreadsheet this. You’ll miss something.
- Build the system once, properly, then monitor it.
The 0.9x ROAS is probably real, but it might also just indicate that your US campaign needs different optimization—different creators, different positioning, different timing. Not necessarily a failure.
How much budget are you allocating to analytics infrastructure?