We launched a campaign across Russia and the US at the same time, and I got two completely different readings on how it performed. In Russia, all the metrics looked solid—engagement was good, conversion rates were hitting targets, ROI made sense. In the US, engagement was lower but conversion was actually higher. Cost per acquisition was different, customer lifetime value projections diverged.
So I had to figure out: is this campaign successful or not? The numbers were telling me different stories depending on which market I looked at. And I realized I didn’t have a framework for actually interpreting that.
The metrics aren’t wrong—they just reflect different market conditions. But most discussions about campaign success treat metrics like they’re universal. They’re not. A “good” engagement rate in one market might be average in another. Customer acquisition patterns are completely different. Purchase cycles vary.
I’m realizing I need to develop a way to compare performance across markets that accounts for these differences, or at least helps me understand what’s driving the divergence. Is it market maturity? Audience behavior? The product positioning? Our execution?
How do you actually make sense of cross-market metrics? Do you have a framework for comparing performance across different markets, or are you just tracking everything independently and hoping the patterns make sense?
This is a problem I deal with constantly, and the solution is to stop thinking about absolute metrics and start thinking about relative performance and variance.
Here’s my framework: for every campaign, I establish baseline metrics for each market BEFORE launch. What’s the historical engagement rate for similar content in Russia? What’s the typical conversion rate? Once I have those baselines, I measure performance as variance from baseline, not in absolute terms.
Example: if historical engagement in Russia is 4% and we hit 5%, that’s +25% variance. If historical engagement in the US is 2% and we hit 2.5%, that’s also +25% variance. Both are equally successful, even though the absolute numbers look different.
I also track what I call “market context multipliers.” US audiences have more ad-blocking, longer consideration cycles, and different seasonality. Russian audiences may have higher engagement but lower purchase intent on certain product categories. These aren’t flaws in the data—they’re just realities of the market.
The key is documenting baseline metrics and market context for every geography you operate in. Then when campaign results come in, you can interpret them against those baselines.
Another thing I do: segment performance metrics by cohort—not just by market, but by audience segment within markets. Sometimes one demographic is responding well while another isn’t, and that’s a more actionable insight than aggregate market performance.
Anna’s approach is solid on the data side. Let me add strategy layer on top: you need to understand what each metric actually signals in each market.
Engagement rate might signal “audience interest” in a mature market, but in an emerging market it might signal “novelty.” Conversion rate might signal “product-market fit” in one market but “limited competition” in another. Same metric, completely different meaning.
When I’m analyzing cross-market metrics, I ask: what is this metric actually telling me about market dynamics, not just campaign performance? If engagement is lower in the US but conversion is higher, that might mean US audiences are more selective but more committed to purchase. That’s valuable strategic insight, not a failure of the campaign.
Also—and this is critical—separate market performance from campaign performance. A campaign that underperforms in one market might not be a bad campaign; it might be a bad fit for that market. Understanding that difference changes how you iterate.
My recommendation: create a market context document for each geography. Include: typical engagement rates, customer acquisition costs, purchase cycles, seasonal variations, competitive landscape. Then when campaign results come in, you can interpret them in context.
We struggled with this during our US expansion. We’d launch a campaign, get data back, and have no idea what it meant because we were comparing US results to Russian benchmarks.
What helped us: we stopped trying to compare markets directly. Instead, we asked: what is the market telling us? In Russia, high engagement but low conversion might mean “good job reaching the audience but weak offer.” In the US, lower engagement but higher conversion might mean “we’re reaching a smaller, more qualified audience.”
We also realized that a successful campaign in one market might look completely different in another. In Russia, we got results through volume and engagement. In the US, we got results through targeting and precision. Both were working; they were just different.
Now we set performance expectations by market upfront, before we launch. “Based on what we know about these markets, here’s what success looks like in each one.” That prevents the confusion later.
I would add that the metrics you choose to track should reflect what audiences care about in each market. In Russia, communities and trust might be huge engagement drivers. In the US, it might be differentiation and value proposition.
When we analyze campaign performance across markets, I make sure the team talks to creators in each market about what they’re seeing. Creators often notice engagement patterns that don’t show up cleanly in data. They can tell you: “This content is resonating for this reason” or “This demographic is engaging but not buying.” That qualitative insight is as important as the metrics.
I also encourage teams not to get too rigid about metrics. Some of the best campaigns don’t fit neat KPIs because they’re generating value in unexpected ways. Track what you said you’d track, but stay curious about what else is happening.
When I compare campaign performance across markets for clients, I use what I call a “performance dashboard” that’s specific to each market. It includes: absolute metrics, variance from baseline, cohort performance, and trend lines.
The variance column is key—it shows you not whether a campaign succeeded in absolute terms, but whether it succeeded relative to historical performance in that market. That’s how you actually compare performance across markets.
I also flag anomalies. If conversion is higher but engagement is lower, that’s worth investigating. Sometimes it’s a positive signal; sometimes it means you’re reaching the wrong audience with the right offer. You need that context to interpret it.
One thing that helps: monthly reviews with market teams where we’re not just looking at numbers, we’re looking at what’s changing. Market conditions shift, audience behavior evolves, competitive dynamics change. Your campaign interpretation should account for all of that, not just raw numbers.
From a creator perspective, I notice that engagement patterns are very different between platforms and markets. What works on TikTok in Russia might completely fall flat on TikTok in the US because the algorithm, audience, and content trends are different.
So when you’re looking at metrics, ask: are you comparing the same content across different markets, or different content tailored to each market? If it’s the same content, the variance might be platform/market dynamics. If it’s different content, you need to evaluate it separately.
I also notice that creators interpret metrics differently based on their audience. What I consider good engagement might be different from what a brand manager considers good engagement. That’s why talking directly with creators about their channel dynamics is so valuable. They know their audience in a way spreadsheets can’t capture.