This is driving me insane. On paper, our LATAM campaigns look incredible—lower costs, higher engagement rates, amazing metrics. But the moment I try to put them side-by-side with US campaign data and calculate unified KPIs, everything falls apart.
I can’t figure out if LATAM is actually outperforming, or if I’m just measuring different things. The benchmarks are inconsistent, the cost structures are different, the audience behaviors are wildly different. When I present cross-market ROI to leadership, they rightfully call out that I’m comparing apples to oranges.
The real problem is that we don’t have a shared dashboard or unified framework for setting KPIs across regions. Every market has its own reporting structure, and translating that into a coherent picture is nearly impossible.
I feel like there has to be a better system—something that lets us define regional benchmarks, set unified KPIs that account for market differences, and actually track campaign performance in a way that makes sense for decision-making. Right now, I’m stuck in spreadsheets trying to manually normalize data.
How do you actually structure ROI tracking and comparison across different markets? What KPIs do you use that work universally, and how do you account for regional variation without losing the ability to compare?
Okay, this is exactly what I spent 6 months solving. The core issue: you can’t compare raw metrics across regions. You need normalized metrics.
Here’s what we did:
Stop comparing these (they’re useless across regions):
- Raw engagement rates
- Cost per post
- Follower growth
Start comparing these (normalized metrics):
- Cost per engaged follower (accounts for audience size variation)
- Conversion rate as % of engaged audience
- Customer acquisition cost (CAC) from influencer channel
- Return on ad spend (ROAS) normalized by market conditions
- Customer lifetime value (CLV) from influencer-sourced customers
The process:
- Define your “north star” metric—for us, it’s CLV from influencer-sourced customers, divided by CAC. That’s your true ROI.
- Track all supporting metrics that lead to it: clicks, conversions, repeat purchase rate, etc.
- Set regional benchmarks for each supporting metric based on historical data in that region.
- Report both regional benchmarks AND how each campaign performs relative to its regional benchmark.
Example: If average conversion rate from LATAM influencers is 2.5% and from US is 1.8%, a campaign performing at 2.0% in LATAM is below regional benchmark, while 1.9% in US is above benchmark. That’s useful information that raw comparison isn’t.
For the dashboard piece: we use attribution software + spreadsheet framework. You track: campaign ID, region, creator, engagement metrics, click-through, conversions, revenue, CAC, and ROI.
What’s your current attribution setup? Are you able to track which customers came from which influencers?
This is a classic analytics maturity issue, and Анна’s framework is solid. I’d layer in one more strategic consideration:
You need to define your KPIs by business objective, not by region. So instead of “engagement rate in LATAM,” you’re tracking “awareness KPI” (which might be different in LATAM vs. US, but the objective is the same).
Here’s how we structured it:
Universal KPIs (same across regions, adjusted for market conditions):
- Customer acquisition cost (adjust for regional unit economics)
- Return on influencer spend
- Brand lift metrics (measure with surveys, account for cultural differences in survey response)
Regional KPIs (acknowledge that different markets need different approaches):
- Engagement rate benchmarks (unique to region based on platform usage patterns)
- Influencer tier performance (micro vs. macro might perform differently)
The reporting model:
- Dashboard 1: Universal KPIs by region (easy comparison)
- Dashboard 2: Regional benchmarks and regional performance (context)
- Dashboard 3: Trend analysis (are both regions improving? Declining?)
When leadership asks “which region is outperforming,” you can answer: “LATAM has higher engagement, US has higher conversion. Here’s what that means for our strategy.”
The key insight: Raw numbers aren’t comparable, but business outcomes are. Focus there.
Real problem here: you’re probably not tracking causality, just correlation.
When a campaign “performs well” in LATAM, what actually caused the performance? Was it the creator? The product? The audience readiness? The timing? When you can’t isolate causality, you can’t compare across regions because different factors might be driving success in each market.
For our international expansion, we started asking: “What are we actually testing here?” If we’re testing whether a creator can drive conversions, we need to control for other variables. If the product sells better in one market, that’s market conditions, not influencer performance.
What helped: We started running parallel tests. Same creative, different creators. Same creator, different regions. Same region, different audiences. By isolating variables, we could actually see what mattered.
It slowed things down initially, but the data we have now is actually usable. We can make smarter decisions about where to invest because we know what’s actually driving results.
Might be worth investing time in a testing framework before you try to build unified KPIs.
From creator perspective—make sure you’re tracking what succeeds and when. Different times of year, different content types, different audience moments.
I’ve noticed that my LATAM audience engages more during certain seasons (campaigns around specific holidays perform differently), while my US audience has different patterns. If brands just looked at aggregate numbers, they’d miss that timing matters way more than they think.
Maybe part of your ROI confusion is seasonal variation, not region? Just a thought.