I’ve been thinking about how we could improve our ROI storytelling, and I think partnership data might be the missing piece.
Right now, we analyze our campaigns individually. Campaign A did X, Campaign B did Y. But nobody’s connecting the dots across campaigns to see the bigger ROI picture. Like, does running two campaigns from different creators simultaneously amplify results? Or does it cannibalize? We don’t know.
I’ve heard some agencies and platforms talking about compiling anonymized case studies from multiple campaigns and partners to identify patterns. The idea is: aggregate enough data across different campaigns, different creators, different markets, and suddenly you start seeing real ROI drivers that individual campaigns can’t reveal.
The challenge is, that data is messy. Different tracking setups, different definitions of success, different audience overlap. But if you could normalize it, there might be something there.
Also, I’m curious whether the bilingual angle—having input from both US and Russian market experts analyzing the same pool of campaign data—produces better insights. Like, does a US strategist and Russian analyst looking at the same cross-campaign data spot patterns that a single perspective would miss?
Has anyone actually done this? Pooled campaign data across multiple partnerships or team members to get a broader view of what really moves ROI? And if so, how do you avoid the data quality issues? How do you make it scalable?
I’m trying to figure out if this is worth the setup cost or if I’m chasing phantom insights.
Yes, this absolutely scales, but you need structural discipline.
Here’s what I’d do:
Step 1: Standardize collection
Every campaign gets logged in a single system with identical fields:
- Spend (USD/RUB, converted to single currency)
- Reach, engagement, conversions (same definitions)
- Creator info (tier, niche, audience demographic)
- Market, campaign objective, duration
Step 2: Normalize by cohort
You can’t directly compare a $5K awareness campaign to a $50K conversion campaign. So segment by:
- Campaign objective (awareness/consideration/conversion)
- Creator tier (micro/mid/macro)
- Market (US/RUS)
- Product category
Step 3: Calculate benchmarks within each cohort
Now you can say: “For mid-tier conversion campaigns in the US, median CAC is $35.” Anyone above/below that becomes a learning case.
Step 4: Look for correlations
Do macro creators consistently outperform micro? Not if you control for audience quality. Does bilingual campaign mix do better? Can’t tell if you’re not isolating the variable.
For the bilingual insight angle: Absolutely, having multiple perspectives helps. A US analyst might see trend patterns; a Russian analyst might catch execution nuances. But only if you structure the analysis to require both perspectives, not make it optional.
One warning: this is a lot of data work upfront. Make sure you have someone (or a tool) owning data quality, or garbage in = garbage out.
How much historical campaign data do you have? That determines whether this is viable or if you need to build it going forward.
I think you’re onto something, but I’d frame it differently: this is about relationship data, not just campaign data.
When you’re aggregating cases across partnerships, you’re learning not just about campaign mechanics; you’re learning about which creators collaborate well, which creators complement each other, which creator pairs drive better outcomes than solo campaigns.
That’s almost more valuable than individual campaign analysis.
For example: If you notice that Influencer A + Influencer B always produce synergistic results (higher combined ROI than expected), that becomes a repeatable partnership formula. You keep them together.
I’d set up both quantitative data (CAC, ROAS, etc.) and qualitative feedback from creators on collaboration dynamics. Did they communicate well? Were their audiences aligned or different? Would they work together again?
Then share successful collaboration patterns with other partnerships. Like, “We found that micro + macro creator pairings in the beauty vertical outperform solo campaigns by 25%.” Now you’re helping others scale, plus you’re building the hub reputation.
Also, I think the bilingual element really shines here. A Russian partnership manager might notice collaboration patterns that a US manager misses because they understand different relationship dynamics.
Would you be open to doing a joint quarterly partnership review where both US and Russian teams present? That might unlock some blind spots.
We’ve done this to some extent as we scaled, and it’s been valuable but also humbling.
It’s valuable because patterns do emerge. We found, for example, that campaigns with clear, single creative direction consistently outperformed campaigns with “let’s try three angles.” Took analyzing 30+ campaigns to see it clearly, but once we saw it, we stopped doing multiple angles.
It’s humbling because a lot of our assumptions were wrong. We thought follower count mattered way more than it does. We thought seasonal timing mattered more than it does. We thought our home market (Russia) operations were more efficient than they actually were until we ran the cross-market comparison.
For the setup cost: there’s definitely friction. Different teams tracked things differently. We had to do a lot of backfill and normalization. It took maybe a month of work to get 6 months of historical data clean enough to analyze.
But the ongoing cost is minimal. You’re just adding discipline to what you’re already doing.
Bilingual analysis: eh, I’m less convinced this adds significant value unless you have market-specific differences in campaign types. If both markets are running similar campaign strategies, having a Russian analyst debrief US data doesn’t surface much. But if you’re running different strategies by market, then having both perspectives makes sense.
Real talk: How much of your campaign data is actually clean and comparable right now? That’s the real blocker.
This is exactly what we’re doing with our portfolio of clients, and it’s become a competitive advantage.
Here’s the infrastructure we built:
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Campaign Data Lake: Every agency campaign feeds into a centralized db with standardized fields. We have ~400 campaigns logged across 30+ clients.
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Benchmarking Engine: Clients can see how their CAC compares to industry benchmarks. Macro visibility.
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Pattern Recognition: We run quarterly analysis to identify what’s working and what isn’t. Are video campaigns outperforming carousels? By how much? In which niches?
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Collaborative Case Studies: We anonymize wins and share across clients. Everyone learns.
The bilingual partnership angle: We also work with both US and Russian clients, so we have US campaign data and Russian campaign data. The insights are wild—same product, different market strategy produces completely different ROI.
Scalable? Absolutely. But you need someone owning data governance. One person, part-time at least. Without it, data quality degrades and insights become useless.
For you: If you don’t have in-house data infrastructure, this might be overkill. But if you’re running 20+ campaigns a quarter across multiple markets, it’s a no-brainer investment.
Where are you at on campaign volume?
From a creator perspective: I genuinely appreciate when brands analyze what worked and apply it to future collabs. Like, if a brand figures out my audience resonates better with tutorial-style content than story-style content, and they use that learning for the next campaign, the results are always better.
So pooled cross-campaign analysis? Great idea, as long as it’s used to improve briefs and targeting, not to game creators or find “formula” replacements.
One thing I’d warn about: sometimes campaigns underperform because of external factors—algorithm changes, bad timing, platform issues. Don’t over-index on pattern-finding if you’re not accounting for those.
Also, if you’re going to aggregate data, make sure creators know you’re doing it and how you’re using it. Transparency builds trust. If I find out my campaign data was quietly bundled into some analysis, I’m less likely to want to work with you again.
But if you say, “Hey, we’re compiling anonymized insights from multiple campaigns to improve how we work—want to see the findings?” Now I’m invested in your success.
This is a solid idea, but scale it carefully. Here’s why:
The problem with pooled analysis:
If you aggregate campaigns from different markets, different product categories, different business models, the patterns become noise. You need to segment before you draw conclusions.
The right approach:
- Create cohorts: Group by market, product category, campaign objective, creator tier.
- Benchmark within cohorts: Find the median performance for each cohort.
- Identify outliers: Campaigns that beat/miss benchmarks are your learning cases.
- Look for mechanisms, not just correlations: Don’t just say “Macro creators outperform.” Ask why. Is it audience quality? Creator credibility? Budget size?
Bilingual analysis value:
Depends on whether you’re running fundamentally different strategies by market or the same strategy with market adaptation. If same strategy, bilingual review doesn’t add much. If different strategies, then yes—having analysts from each market compare notes surfaces strategic blind spots.
Your real ROI driver:
The value isn’t in finding patterns. It’s in acting on patterns. Too many teams analyze, then ignore findings. Build in a feedback loop: What did we learn? How did we change strategy? What was the impact of that change?
That’s where ROI actually improves.
How much of your current analysis findings actually make it into changed strategy vs. just getting filed away?