How do I actually compare what works: measuring influencer campaign success when my Russian and US data tell totally different stories?

Стратегии инфлюенс-маркетинга (RU)
1

I’m a marketing director for a company that sells to both Russian and US audiences, and we’ve been running influencer campaigns in both markets for about a year. Here’s my problem: the metrics don’t speak the same language.

In Russia, I’m tracking engagement rate, conversion rate, and repeat purchase rate. A successful campaign shows up as high engagement + decent conversion, and repeat purchase rate is usually 35-40% for similar products.

In the US, engagement rates are lower (about half), conversion rates are comparable, but repeat purchase rate is terrible—usually 12-18%. But somehow, the overall ROI numbers end up similar. When I try to explain this to my boss, I sound like I’m making excuses instead of reporting facts.

I started asking myself: Am I measuring the wrong things? Should I be tracking different metrics for each market? Or is there actually a unified measurement system that accounts for these differences?

I’ve been trying to build a standardized analytics template that works for both markets, but every time I think I have something, I hit a wall. A KPI that makes sense for Russia doesn’t translate to US performance, and vice versa.

Last month, I collected data from about 20 comparable campaigns (10 Russian, 10 US) and tried to normalize everything. It was a mess. The Russian campaigns show up as “high engagement, moderate conversion, high loyalty.” The US campaigns are “low engagement, decent conversion, low loyalty.” If I weight them equally, I can’t tell which market is actually performing better for the business.

I think the issue is that I’m trying to force a system that doesn’t exist. Different markets, different audience behavior, different success definitions.

How do you actually compare campaign performance across Russia and US when the metrics themselves tell different stories? What’s your framework for saying, “This works” without losing the nuance of regional differences?

2

You’re right that direct metric comparison is the wrong approach. Here’s what I do instead:

I convert everything to a single, business-relevant metric: Customer Lifetime Value (LTV) to Customer Acquisition Cost (CAC) ratio.

Breakdown:

  • Calculate CAC for each market (total campaign spend ÷ new customers acquired)
  • Calculate LTV for each market (revenue from customer over 12 months, accounting for repeat purchases)
  • Compare as ratio: LTV:CAC (healthy is 3:1 or better)

For your data: If Russian repeat purchase is 35-40% and US is 12-18%, that directly impacts lifetime value. A campaign that looks lower-engagement in the US might still have solid LTV:CAC if the AOV is higher or the customers are higher-value.

This framework accounts for regional differences automatically. You’re not forcing metrics to be identical—you’re comparing business outcomes.

I also track payback period: How quickly does the customer acquisition cost get recovered? Russian campaigns: usually 20-35 days. US: usually 45-70 days. That’s a real difference that metrics don’t show.

Once you shift to LTV:CAC and payback period, comparability becomes straightforward. You’re not comparing apples to oranges anymore—you’re comparing actual business impact.

What’s your average order value in each market? That’s the key number I need to help you calculate this.

3

I struggled with exactly this when scaling my company. Here’s what helped:

I stopped trying to create one measurement system and created two, but with a single comparison layer.

Russia system: engagement + repeat purchase + brand sentiment (I ask customers “would you recommend?”)
US system: conversion + AOV + net new customer count

Then I compare these at the business layer: Which market is growing faster? Which is more profitable? Which has better unit economics?

Turns out Russia was showing stronger engagement and loyalty signals, but US was generating more revenue per customer. Both are “working,” just differently.

Once I accepted that different markets need different success definitions, everything got clearer. My reporting to the board changed: instead of “are these campaigns hitting uniform benchmarks?” it became “are we hitting OUR market-specific targets?” Much clearer conversation.

Maybe that’s what you need—two measurement systems with a unified business outcome layer on top.

4

You know what’s interesting? From the partnership side, I’m constantly hearing from creators in both markets that they feel their audiences respond differently to the same brand.

Russian creators tell me their audiences want immediate value: “Does this work? Would you use it again?” US creators say their audiences want authenticity and story.

That might be why your metrics look so different. The audiences themselves have different purchase motivations. Russian audience = utility-focused. US audience = narrative-focused.

If that’s true, then comparing them directly is actually the wrong move. You’d need a measurement system that accounts for those different motivations.

Maybe meet with some of the creators you worked with in both markets? They might give you insight into why the metrics diverge the way they do.

5

Okay, so from a creator standpoint: engagement rate doesn’t actually tell you anything about quality anymore. Like, I can get high engagement by posting controversial stuff, but that doesn’t mean the brand’s customers are actually buying.

What you should track instead: Is the engagement from actual potential customers or just random people? In Russia, I find that people engage because they actually want the product. In the US, people engage because they want to join the community or the conversation.

That’s why your repeat purchase rates are so different. Russian buyers: buy because they want the product. US followers: engage because they like the vibe, but they’re not necessarily buyers.

Maybe instead of comparing engagement rates directly, compare “purchase-intent engagement”? Like, track which comments are actual interest vs. just social currency.

It won’t fix the comparison issue completely, but it might make you feel less crazy about the data.

6

Here’s the practical workaround I use: Stop treating Russia and US as comparable data sets for benchmarking purposes.

Instead, maintain two separate scorecards:

  • Russia scorecard: Track what matters for Russian campaigns (engagement, repeat purchase, community strength)
  • US scorecard: Track what matters for US campaigns (conversion efficiency, AOV, customer quality)

Then, measure both against their own baselines. Is the Russian campaign hitting Russian success targets? Is the US campaign hitting US targets?

This removes the confusion. You’re not comparing apples to oranges. You’re reporting: “Russian campaigns performing well against Russian benchmarks. US campaigns performing well against US benchmarks.”

It’s actually more defensible to leadership because you have clear, separate logic for each market. Not confusing universal metrics that don’t make sense.