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Country was the strongest signal in our creator cold outreach sample
When a brand asks us where to spend their creator-outreach budget, the question is almost always framed as which platform: TikTok, YouTube, Instagram. A sample of our own recent campaign data says the platform question is downstream of a larger one. In the campaigns we looked at, the spread in reply rate between creator countries was several times wider than the spread between platforms.
We looked at a curated subset of our cold-outreach sends from a recent quarter. This piece walks through what we found and the methodology behind it.
The dataset
What follows is drawn from a sample of our completed campaigns, not the full book — a selected subset chosen to keep comparisons clean. We kept only leads whose last contact was sent at least fourteen days before we looked at the data, so every lead in the sample had a fair window to reply. We also excluded replies classified as automated — out-of-office messages, unsubscribes, and a handful of system-flagged threads. Reply in this analysis always means a human response.
Each creator carries a country tag from the standard profile-enrichment data our team uses when importing leads. For this analysis we restricted country-level claims to leads whose tag resolved unambiguously to a specific country. Leads tagged only with a content language — “English”, “Japanese” — were excluded from country tables. English-speaking creators live across many countries, and a language is not a country claim we can support.
Reply rates across the sample sat well above the 1–5% range commonly reported for SaaS cold email, which is one reason we think creator outreach is a structurally different channel and why the cross-country variance within it is worth examining.
Same pitch, three countries, three different worlds
The cleanest thing in the dataset is a single campaign — we’ll call it Campaign A, an AI consumer-assistant client — that went to creators across Latin America, Southeast Asia, and Africa. The outreach email, sender domains, seven-day sequence, and sending week were held constant at the campaign level. What changed between sub-groups was the country cohort the message landed in — and, of course, the creators inside each cohort, who differ from each other along dimensions we did not try to hold constant (vertical, follower tier, rate expectations, language match, local holiday calendars). So we’re comparing country slices of creators, not identical creators in different countries. With that caveat, the slice-level gap is the largest single effect in the sample.
Figure 1. Same pitch, same cadence, same week — reply rates span ~13% to ~64% across three countries, a roughly 5× spread inside a single campaign. Campaign A (AI consumer-assistant client). n ≈ 400; 14-day maturity cutoff; Wilson 95% CI; every pairwise gap clears Bonferroni at p < 0.05.
| Country | Leads | Reply rate | 95% CI |
|---|---|---|---|
| South Africa | ~125 | ~64% | 54 – 72% |
| Vietnam | ~125 | ~33% | 26 – 42% |
| Brazil | ~150 | ~13% | 9 – 20% |
Every pairwise comparison between these three countries clears the Bonferroni-adjusted 0.05 threshold on a two-proportion z-test. The biggest gap, South Africa versus Brazil, has a Bonferroni-adjusted p-value smaller than 10⁻¹⁵. In plain terms: the odds of that gap being a sampling accident are astronomically low.
Nearly two out of every three South African creators in this cohort replied. Fewer than one in seven Brazilian creators did. The email, the sender, the offer, and the follow-up cadence were all held constant at the campaign level — what varied is the country cohort, and everything that varies with it.
Under a looser seven-day maturity cutoff — which we ran as a sensitivity check — the same campaign expands to five countries, and eight of the ten pairwise comparisons remain Bonferroni-significant at p < 0.05. Mexico and Indonesia slot neatly between Vietnam and Brazil. The ordering is stable and the gaps are real.
Western markets look alike
The counter-finding is equally useful. We ran a separate campaign — we’ll call it Campaign B, an AI creative tool — that targeted creators in the United States, Canada, the United Kingdom, and Germany. Same copy, same sequence, same sending cadence within the campaign. Reply rates landed in a narrow band from ~8% to ~12%:
Figure 2. The same offer across four Western markets lands in a narrow ~8–12% band — no pairwise gap is statistically distinguishable. Campaign B (AI creative tool). n ≈ 1,175 leads across four cohorts; none of six pairwise comparisons clears Bonferroni-adjusted p < 0.05; Wilson 95% CI. Same 0–80% scale as Figure 1.
| Country | Leads | Reply rate | 95% CI |
|---|---|---|---|
| Canada | ~100 | ~12% | 7 – 20% |
| Germany | ~50 | ~12% | 6 – 23% |
| United States | ~850 | ~8% | 6 – 10% |
| United Kingdom | ~175 | ~8% | 5 – 13% |
None of the six pairwise comparisons between these four countries cleared statistical significance after Bonferroni correction. You cannot reliably tell these markets apart from their reply rates.
So the country effect isn’t uniform across the world. Western developed markets behave like one pool: saturated, cold-email-weary, uniformly low response rates. The dispersion sits entirely on the emerging-markets side. This is probably the single most practical takeaway of the whole exercise. If you’re choosing where to spend outreach, the question is usually framed as “which platform in the US”. The data says the leverage is one step earlier: “is the US even the right market to outreach into right now”.
Platform is a multiplier on country, not a primary variable
We also looked at reply rates cut by creator country and the platform we were pitching for (TikTok, Instagram, YouTube). Under the 14-day cutoff the sample thins past country-only slicing, so we use the 7-day sensitivity cut here — directionally identical to the primary.
The richest cells, limited to country-by-platform cuts with at least 50 leads each:
Figure 3. Platform acts as a multiplier on country, not a primary variable. TikTok into emerging markets sets the ceiling (~70% in South Africa); Instagram / YouTube into Western markets set the floor (~13%). All country × platform cells in the sample with ≥ 50 leads; 7-day maturity sensitivity cut. Bar color denotes platform.
South Africa on TikTok is the top cell in the entire dataset at roughly 70%. The same South African creators approached about Instagram content drop to about 30% — a forty-point gap inside a single country cohort. TikTok is clearly the strongest channel into emerging-market creators. TikTok into US creators, meanwhile, gets you the same mediocre ~10% that YouTube does.
Put another way: platform looks like a multiplier on whatever country signal you already have. TikTok amplifies responsive markets and does little for saturated ones. A TikTok campaign into responsive countries outperforms everything else we ran; a TikTok campaign into Western markets sits alongside Instagram into those same markets.
Product category matters about half as much as country
One last cut worth surfacing. We grouped the campaigns in the sample into anonymous categories by product type, and looked at reply rate without weighting by volume:
| Product category | Reply rate |
|---|---|
| AI consumer assistant | ~21% |
| Consumer hardware | ~16% |
| AI creative tool | ~10% |
AI consumer assistants — products aimed at end users that creators can organically weave into a video — pull about 2× the reply rate of AI creative tools, which ask creators to adopt a piece of software into their own workflow. The hardware slice is small and we’d want a bigger sample before leaning on the exact number; the direction is consistent with a simple intuition that things creators can show to their audience are easier to pitch than things creators have to use themselves.
This effect is roughly half the size of the country effect. If the headline is “country spreads reply rates by five-ish times”, the sub-headline is “product category spreads them by about two times, in the same direction”.
What we think this means for brands
Three things change if you take the data above seriously:
The market question comes before the platform question. If the target market is the United States or any other Western developed market, every platform will give you roughly the same ~10% reply rate out of cold. The lever worth pulling is the pitch and the follow-up cadence, not TikTok-versus-YouTube. If the brand is willing to reach emerging-market creators, the variance opens up to several times and now platform, country, and copy all matter.
Emerging markets are under-outreached. A ~64% reply rate out of a cold sequence is what you’d expect from a warm-intro list, not from a one-to-many send. The fact that we see that number from one campaign in one country cohort suggests the market is genuinely underserved by the established creator-outreach playbook, which is largely written for US audiences on tools built in the US.
Product shape matters. Things a creator can demonstrate — “watch me use this AI study assistant to solve a problem on camera” — sell. Things a creator has to learn to use themselves sell worse. This isn’t a platform or country effect; it’s a category effect that compounds with both.
We’ll publish a follow-up next quarter with fresh numbers, a direct-comparison sensitivity between different outreach copy lengths, sending cadences, and sequence depths, and — in response to feedback on this piece — a formal logistic regression with country and platform as factors so we can replace the descriptive spread ratio here with a proper variance-explained number. If there’s a specific cut you’d like to see, our contact page is open. The end-to-end outreach work behind these numbers — sourcing, pitching, negotiation, briefing — is what we offer brands as our creator outreach service.
Methodology
Data source. A selected subset of our team’s cold-outreach campaigns from a recent quarter. This is not our full send volume — it is a curated sample assembled for this analysis, with immature leads, incomplete campaigns, and ambiguously-tagged records removed. We do not publish client names, exact campaign codes, or exact send dates; brand-level data is grouped into anonymous category codes, and the two featured campaigns are referred to as Campaign A and Campaign B.
Sample-size reporting. Lead counts in the charts and tables above are rounded to the nearest 25 (small cohorts) or 50 (large cohorts). Reply rates are rounded to the nearest whole percent for display, but all significance tests, confidence intervals, and spread comparisons below are computed on the unrounded underlying counts.
Maturity cutoff. We drop any lead whose last contact timestamp is within fourteen days of the pull date, so every kept lead had a fair reply window. We also ran a seven-day sensitivity analysis. The headline finding holds under both cutoffs — in the flagship Campaign A, all three pairwise country comparisons remain Bonferroni-significant under the tighter cut, and eight of ten remain significant under the looser one.
Country attribution. Country tags come from the standard creator-profile enrichment data our team uses when importing leads. For this analysis we restricted country-level claims to leads whose tag resolved unambiguously to a specific country. Leads tagged only with a content language — such as “English” or “Japanese” — are excluded from country-level tables, because a language is not a country.
Reply definition. A lead counts as having replied only if at least one of their received responses was not classified as an auto-responder. Out-of-office messages, unsubscribes, and system-flagged threads are filtered out. Human “not interested” replies count as real replies — a person who writes back to decline is still a person who engaged with the message.
Statistical tests. Reply-rate confidence intervals are Wilson score 95% intervals. Pairwise country comparisons within a single campaign use a two-proportion z-test. Within each same-campaign comparison group we apply a Bonferroni correction for the number of country pairs tested.
Quantifying the country-versus-platform gap. The “several times” framing is a descriptive spread comparison, not a model fit. Within a single campaign we take the range of reply rates across country cohorts (largest country-level rate minus smallest) and the range across platform cohorts. Across the campaigns we examined, the country range was roughly five times the platform range. We have not fit a formal logistic regression with country and platform as factors — doing so would yield a cleaner variance-explained (pseudo-R²) number, a country-only vs platform-only AIC comparison, and a proper odds-ratio estimate per factor. We’ll publish that in the follow-up and will revise the framing here if the model tells a different story than the descriptive spread does.
Open rates are not published. Open tracking was enabled on only a small fraction of the sample, which means any open-rate claim would rest on biased partial coverage. We excluded opens from the analysis entirely.
What we cannot claim. This is one agency’s outreach, over a limited window, on a selected subset of campaigns we chose for comparison integrity. We can’t claim these rates generalize to every brand or every outreach team. We also can’t claim that country is a causal variable — even within a single campaign that holds the email and sending machinery constant, the creators inside each country cohort differ from each other along dimensions we did not control for (vertical, audience size, rate expectations, commercial maturity, platform habits, language match with the pitch, local-holiday timing, lead-source mix). What we can say is that in the controlled campaign setup, the country tag was the most visible moving variable and tracked closely with reply rate, and that the cohort-level country effect we measured is large, statistically rigorous at the slice level, and consistent across both the 14-day primary and 7-day sensitivity cuts.
