Country Expansion Analysis

Overview

This analysis attempts to identify the most promising international markets for Buffer to target after the US.

We’ll examine the country-level funnel from website traffic through signups and paid conversions to find markets that combine sufficient scale with strong conversion performance. We’ll also look at the current makeup of MAU and paying customers and factor those into a composite score that we’ll use to rank opportunities.

What We Found

The UK stands out as a top expansion opportunity when we factor in both funnel performance and existing user base. The UK accounts for around 12% of Buffer’s paying customers and 12% of MAU outside the US, which represents a significant base from which to grow.

Canada and Australia rank second and third, and Germany and France round out the top five, with solid conversion rates for each of these countries.

The composite score balances five factors: traffic volume, signup rate, conversion rate, MAU proportion, and paying customer proportion. Countries with high traffic volume but low conversion rates, such as India and Bangladesh, score lower, but are still worth considering.

On the flip side, countries with higher conversion rates but less traffic, like smaller European markets, are also pushed down the list a bit.

US Baseline

Before diving into international markets, let’s establish the US baseline to use as a reference point. The data spans 25 months from February 2024 through February 2026. Conversions and conversion rate refer to paid conversions that occurred within 60 days of signing up.

funnel <- readRDS("country_funnel_data.rds")

us_data <- funnel %>%
  filter(country_iso_code == "US") %>%
  summarise(
    total_visitors = sum(visitors),
    total_signups = sum(signups),
    total_activations = sum(activations),
    total_conversions = sum(conversions)
  ) %>%
  mutate(
    signup_rate = total_signups / total_visitors,
    activation_rate = total_activations / total_signups,
    conversion_rate = total_conversions / total_signups
  )

cat("US Baseline Metrics:\n")

US Baseline Metrics:

cat("  Visitors:", comma(us_data$total_visitors), "\n")

  Visitors: 11,245,681 

cat("  Signups:", comma(us_data$total_signups), "\n")

  Signups: 408,945 

cat("  Conversions:", comma(us_data$total_conversions), "\n")

  Conversions: 24,182 

cat("  Signup Rate:", percent(us_data$signup_rate, accuracy = 0.1), "\n")

  Signup Rate: 3.6% 

cat("  Conversion Rate:", percent(us_data$conversion_rate, accuracy = 0.01), "\n")

  Conversion Rate: 5.91% 

The US has a 3.6% signup rate and 5.9% conversion rate. These metrics will serve as our benchmarks when evaluating other potential markets.

Data Collection

The data comes from the dbt_buffer.country_expansion_funnel table, which tracks the full funnel from website sessions through paid conversions by country and month. The data spans from February 2024 to February 2026. and includes 109 countries.

sql <- "
select
  month
  , country_iso_code
  , country_name
  , continent_code
  , continent_name
  , sessions
  , visitors
  , signups
  , activations
  , conversions
  , traffic_to_signup_rate
  , signup_to_activation_rate
  , signup_to_conversion_rate
  , activation_to_conversion_rate
  , mau_proportion
  , paying_customer_proportion
from dbt_buffer.country_expansion_funnel
order by month desc, visitors desc
"

funnel <- bq_query(sql = sql)
saveRDS(funnel, "country_funnel_data.rds")

Data Aggregation

We aggregate across all available months to get more reliable conversion rate estimates. We exclude the US because it’s already Buffer’s primary market. We also filter to markets with at least 1,000 visitors to make sure that the sample sizes are meaningful.

# get the most recent non-null mau and paying customer proportions for each country
latest_mau <- funnel %>%
  filter(country_iso_code != "US",
         country_iso_code != "Unknown",
         !is.na(mau_proportion)) %>%
  group_by(country_iso_code) %>%
  filter(month == max(month)) %>%
  ungroup() %>%
  select(country_iso_code, mau_proportion)

latest_paying <- funnel %>%
  filter(country_iso_code != "US",
         country_iso_code != "Unknown",
         !is.na(paying_customer_proportion)) %>%
  group_by(country_iso_code) %>%
  filter(month == max(month)) %>%
  ungroup() %>%
  select(country_iso_code, paying_customer_proportion)

latest_proportions <- latest_mau %>%
  full_join(latest_paying, by = "country_iso_code")

# aggregate funnel metrics across all months
country_totals <- funnel %>%
  filter(country_iso_code != "US",
         country_iso_code != "Unknown") %>%
  group_by(country_iso_code, country_name, continent_code, continent_name) %>%
  summarise(
    total_sessions = sum(sessions, na.rm = TRUE),
    total_visitors = sum(visitors, na.rm = TRUE),
    total_signups = sum(signups, na.rm = TRUE),
    total_activations = sum(activations, na.rm = TRUE),
    total_conversions = sum(conversions, na.rm = TRUE),
    n_months = n_distinct(month),
    .groups = "drop"
  ) %>%
  mutate(
    signup_rate = total_signups / total_visitors,
    activation_rate = ifelse(total_signups > 0, total_activations / total_signups, 0),
    conversion_rate = ifelse(total_signups > 0, total_conversions / total_signups, 0)
  ) %>%
  # join the latest proportions

  left_join(latest_proportions, by = "country_iso_code")

country_totals <- country_totals %>%
  filter(total_visitors >= 1000)

cat("Countries with 1,000+ visitors:", nrow(country_totals), "\n")

Countries with 1,000+ visitors: 98 

cat("Total visitors (excl. US):", comma(sum(country_totals$total_visitors)), "\n")

Total visitors (excl. US): 20,089,357 

cat("Total signups (excl. US):", comma(sum(country_totals$total_signups)), "\n")

Total signups (excl. US): 1,195,899 

cat("Total conversions (excl. US):", comma(sum(country_totals$total_conversions)), "\n")

Total conversions (excl. US): 28,810 

Ranking Countries by Opportunity

To find the best expansion markets, we balance five factors:

1. Traffic volume, which tells us if the market is big enough to be worth investing in.
2. Signup rate, which measures how often visitors from this country sign up for Buffer.
3. Conversion rate, which measures how often those signups become paying customers.
4. MAU proportion, which tells us what share of Buffer’s monthly active users are from this country.
5. Paying customer proportion, which tells us what share of Buffer’s paying customers are from this country.

The first three factors tell us about the funnel efficiency. The last two tell us about the current user/customer base. Countries with more existing users and paying customers may be easier to expand since there’s already word-of-mouth and market awareness.

We weight these factors as follows: 15% volume, 20% signup rate, 25% conversion rate, 15% MAU proportion, and 25% paying customer proportion. Each metric is scaled from 0-100 before combining. For volume, we use a logarithmic scale so huge markets like India don’t completely dominate.

# function to scale any metric from 0-100
normalize <- function(x) {
  (x - min(x, na.rm = TRUE)) / (max(x, na.rm = TRUE) - min(x, na.rm = TRUE)) * 100
}

country_scored <- country_totals %>%
  filter(!is.na(mau_proportion), !is.na(paying_customer_proportion)) %>%
  mutate(
    # use log scale for visitors so huge markets don't dominate
    log_visitors = log10(total_visitors),
    # scale each component 0-100
    volume_score = normalize(log_visitors),
    signup_score = normalize(signup_rate),
    conversion_score = normalize(conversion_rate),
    mau_score = normalize(mau_proportion),
    paying_score = normalize(paying_customer_proportion),
    # weighted average
    opportunity_score = (0.15 * volume_score) +
                        (0.20 * signup_score) +
                        (0.25 * conversion_score) +
                        (0.15 * mau_score) +
                        (0.25 * paying_score)
  ) %>%
  arrange(desc(opportunity_score))

Top Expansion Opportunities

The table below ranks countries by their composite opportunity score using data from 2025 onwards. Markets at the top combine meaningful traffic volume, strong conversion performance, and an existing user base.

# aggregate 2025+ data for the table
country_2025 <- funnel %>%
  filter(country_iso_code != "US",
         country_iso_code != "Unknown",
         month >= "2025-01-01") %>%
  group_by(country_iso_code, country_name, continent_name) %>%
  summarise(
    total_visitors = sum(visitors, na.rm = TRUE),
    total_signups = sum(signups, na.rm = TRUE),
    total_conversions = sum(conversions, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  mutate(
    signup_rate = total_signups / total_visitors,
    conversion_rate = ifelse(total_signups > 0, total_conversions / total_signups, 0)
  ) %>%
  left_join(latest_proportions, by = "country_iso_code") %>%
  filter(total_visitors >= 1000)

# score the 2025 data
country_2025_scored <- country_2025 %>%
  filter(!is.na(mau_proportion), !is.na(paying_customer_proportion)) %>%
  mutate(
    log_visitors = log10(total_visitors),
    volume_score = normalize(log_visitors),
    signup_score = normalize(signup_rate),
    conversion_score = normalize(conversion_rate),
    mau_score = normalize(mau_proportion),
    paying_score = normalize(paying_customer_proportion),
    opportunity_score = (0.15 * volume_score) +
                        (0.20 * signup_score) +
                        (0.25 * conversion_score) +
                        (0.15 * mau_score) +
                        (0.25 * paying_score)
  ) %>%
  arrange(desc(opportunity_score))

opportunity_table <- country_2025_scored %>%
  select(
    Country = country_name,
    Visitors = total_visitors,
    Signups = total_signups,
    `Signup Rate` = signup_rate,
    `Conv Rate` = conversion_rate,
    `MAU %` = mau_proportion,
    `Paying %` = paying_customer_proportion,
    Score = opportunity_score
  ) %>%
  head(100) %>%
  mutate(
    Visitors = comma(Visitors),
    Signups = comma(Signups),
    `Signup Rate` = percent(`Signup Rate`, accuracy = 0.1),
    `Conv Rate` = percent(`Conv Rate`, accuracy = 0.01),
    `MAU %` = percent(`MAU %`, accuracy = 0.1),
    `Paying %` = percent(`Paying %`, accuracy = 0.1),
    Score = round(Score, 1)
  )

kable(opportunity_table, align = "lrrrrrrr")

Country	Visitors	Signups	Signup Rate	Conv Rate	MAU %	Paying %	Score
United Kingdom	997,483	73,356	7.4%	3.91%	11.7%	12.5%	66.2
Canada	853,941	24,438	2.9%	6.23%	4.4%	5.7%	45.6
Australia	218,662	16,117	7.4%	7.42%	2.8%	4.4%	43.2
Germany	541,680	23,445	4.3%	4.32%	3.3%	3.0%	34.6
The Netherlands	375,225	12,441	3.3%	4.81%	2.1%	2.3%	31.3
Switzerland	54,890	3,816	7.0%	7.18%	0.7%	1.1%	30.3
Kuwait	2,365	150	6.3%	11.33%	0.1%	0.1%	30.0
France	344,361	19,963	5.8%	3.00%	2.6%	1.8%	28.3
New Zealand	34,862	2,486	7.1%	7.12%	0.4%	0.5%	27.9
Japan	292,273	10,953	3.7%	3.80%	3.1%	1.1%	27.7
Denmark	32,395	1,852	5.7%	7.45%	0.3%	0.4%	27.4
India	1,690,049	97,531	5.8%	0.54%	4.1%	0.5%	25.2
Philippines	200,920	22,468	11.2%	2.08%	2.7%	0.2%	24.9
Spain	274,626	17,199	6.3%	2.35%	2.6%	0.9%	24.6
Ireland	128,298	2,364	1.8%	5.46%	0.5%	0.6%	24.4
Austria	146,840	2,661	1.8%	5.41%	0.5%	0.4%	24.1
United Arab Emirates	58,791	6,097	10.4%	3.99%	0.5%	0.6%	24.1
Saudi Arabia	50,739	5,513	10.9%	4.12%	0.4%	0.5%	23.9
Sweden	59,821	2,548	4.3%	5.30%	0.4%	0.5%	23.3
Poland	100,453	6,147	6.1%	4.03%	0.7%	0.5%	23.0
Israel	27,543	2,839	10.3%	4.54%	0.2%	0.3%	22.8
Nicaragua	1,041	10	1.0%	10.00%	0.0%	0.0%	22.4
Costa Rica	9,392	630	6.7%	6.51%	0.1%	0.0%	22.3
Belgium	149,849	3,596	2.4%	4.14%	0.5%	0.7%	22.3
Moldova	2,073	796	38.4%	0.00%	0.4%	0.0%	21.9
Brazil	157,708	18,603	11.8%	1.39%	1.4%	0.4%	21.8
Nigeria	150,099	24,107	16.1%	0.64%	1.3%	0.2%	21.8
Finland	53,603	1,651	3.1%	5.09%	0.3%	0.4%	21.7
Italy	200,687	8,335	4.2%	2.72%	1.2%	0.6%	21.5
South Korea	52,989	6,731	12.7%	2.64%	0.5%	0.2%	21.4
South Africa	91,580	8,803	9.6%	2.41%	0.9%	0.5%	21.4
Thailand	68,526	4,355	6.4%	3.81%	0.5%	0.2%	21.0
Norway	27,031	1,396	5.2%	5.09%	0.1%	0.2%	21.0
Cyprus	6,775	510	7.5%	5.88%	0.1%	0.1%	20.8
Mexico	178,089	7,537	4.2%	2.88%	0.9%	0.4%	20.6
Portugal	49,387	4,368	8.8%	3.16%	0.6%	0.3%	20.5
Albania	11,772	519	4.4%	5.78%	0.0%	0.0%	19.9
Pakistan	187,490	23,488	12.5%	0.64%	1.1%	0.1%	19.9
Kenya	49,913	8,234	16.5%	0.95%	0.5%	0.1%	19.3
Panama	110,089	413	0.4%	4.36%	0.1%	0.0%	19.2
Bangladesh	96,071	15,945	16.6%	0.31%	0.5%	0.0%	19.1
Indonesia	258,254	14,143	5.5%	1.47%	0.9%	0.3%	18.8
Singapore	265,958	4,025	1.5%	2.46%	0.7%	0.4%	18.8
Romania	65,678	3,535	5.4%	3.20%	0.3%	0.2%	18.8
Slovakia	15,728	985	6.3%	4.47%	0.1%	0.1%	18.8
Morocco	44,585	7,248	16.3%	0.99%	0.3%	0.1%	18.7
Puerto Rico	7,847	495	6.3%	5.05%	0.1%	0.1%	18.6
Argentina	38,034	4,441	11.7%	2.00%	0.4%	0.1%	18.4
Colombia	37,097	3,678	9.9%	2.50%	0.3%	0.1%	18.4
Estonia	17,172	488	2.8%	4.92%	0.1%	0.1%	18.2
Croatia	14,599	685	4.7%	4.67%	0.1%	0.1%	18.1
Hong Kong	52,092	2,559	4.9%	3.09%	0.3%	0.3%	18.1
Greece	30,194	1,983	6.6%	3.28%	0.3%	0.1%	17.9
Taiwan	23,379	2,305	9.9%	2.69%	0.2%	0.1%	17.8
Hungary	38,927	1,401	3.6%	3.43%	0.2%	0.1%	17.0
Egypt	83,626	9,278	11.1%	0.75%	0.5%	0.1%	17.0
Lebanon	10,064	943	9.4%	3.29%	0.1%	0.0%	16.7
Serbia	26,912	970	3.6%	3.71%	0.1%	0.0%	16.7
Malaysia	174,612	4,254	2.4%	1.97%	0.4%	0.2%	16.6
Lithuania	20,676	844	4.1%	3.79%	0.1%	0.1%	16.6
Bulgaria	39,939	1,084	2.7%	3.41%	0.2%	0.1%	16.5
Ukraine	43,574	4,675	10.7%	1.16%	0.2%	0.1%	16.0
Ghana	20,827	2,923	14.0%	0.89%	0.2%	0.0%	15.5
Slovenia	11,928	539	4.5%	3.71%	0.1%	0.1%	15.5
Chile	29,472	1,910	6.5%	1.94%	0.2%	0.1%	14.7
Dominican Republic	8,813	779	8.8%	2.44%	0.1%	0.0%	14.3
Luxembourg	109,290	507	0.5%	1.97%	0.0%	0.1%	14.1
Cambodia	9,219	1,004	10.9%	1.79%	0.1%	0.0%	14.0
China	196,656	917	0.5%	1.31%	0.1%	0.2%	14.0
Peru	18,977	1,869	9.8%	1.23%	0.1%	0.0%	13.8
Venezuela	10,441	1,091	10.4%	1.65%	0.1%	0.0%	13.7
Jamaica	5,254	419	8.0%	2.86%	0.1%	0.0%	13.7
Latvia	19,226	563	2.9%	2.84%	0.1%	0.0%	13.7
Uganda	8,106	1,254	15.5%	0.56%	0.2%	0.0%	13.6
Algeria	22,185	2,374	10.7%	0.76%	0.1%	0.0%	13.4
Ecuador	15,327	1,152	7.5%	1.82%	0.1%	0.0%	13.4
Qatar	7,714	602	7.8%	2.33%	0.1%	0.0%	13.3
Sri Lanka	24,036	2,180	9.1%	0.87%	0.1%	0.0%	13.1
Tanzania	3,201	347	10.8%	2.31%	0.1%	0.0%	13.0
Nepal	26,793	2,439	9.1%	0.33%	0.1%	0.0%	12.1
Russia	209,101	2,191	1.0%	0.37%	0.0%	0.0%	11.9
Jordan	6,078	430	7.1%	2.09%	0.0%	0.0%	11.8
Tunisia	10,287	1,246	12.1%	0.40%	0.1%	0.0%	11.8
Georgia	10,905	595	5.5%	1.34%	0.0%	0.0%	10.5
Iraq	18,388	1,117	6.1%	0.36%	0.0%	0.0%	9.7
Azerbaijan	3,192	226	7.1%	0.44%	0.0%	0.0%	6.8
Palestine	1,224	41	3.3%	0.00%	0.0%	0.0%	1.9
Oman	1,392	35	2.5%	0.00%	0.0%	0.0%	1.8
Kazakhstan	1,173	36	3.1%	0.00%	0.0%	0.0%	1.7
Libya	1,546	19	1.2%	0.00%	0.0%	0.0%	1.3
Guatemala	1,180	20	1.7%	0.00%	0.1%	0.0%	1.1

The UK leads the pack with the best balance of volume and conversion quality, followed by Canada, Australia, Germany, and The Netherlands. None of the other countries make up at least 2% of Buffer’s paying customer base.

Large markets like Brazil, The Philippines, and India are clustered together, mostly because of their high traffic volume and relatively low conversion rates.

Top Countries by Signups

India generates the most signups outside the US, followed by the UK.

Top Countries by Paid Conversions

The UK leads in paid conversions, followed by Canada and Australia. These three English-speaking markets account for a large share of all international conversions. Note that conversions drop to zero in recent months due to the 60-day attribution window.

Germany should be noted as the top non-English speaking market in terms of paid conversions.

Top Countries by Signup Rate

We filter to countries with at least 5,000 total visitors to ensure reliable rate estimates.

African, South Asian, and Latin American markets have the highest signup rates — often 3-5x higher than the US rate of 3.6%.

Top Countries by Paid Conversion Rate

We filter to countries with at least 500 total signups to ensure meaningful conversion rate estimates.

The conversion rate picture is almost the inverse of signup rates. Switzerland, Australia, Denmark, New Zealand, and Canada lead. These are markets with higher GDP per capita and established SaaS adoption patterns.

MAU Proportion by Country

This shows what share of Buffer’s monthly active users come from each country. Countries with a larger existing user base may be easier to expand since there’s already word-of-mouth and market awareness.

The UK has the largest share of MAU outside the US, followed by India and Canada. Japan may be one to keep an eye on, as the proportion has been growing steadily.

Paying Customer Proportion by Country

This shows what share of Buffer’s paying customers come from each country.

The UK contributes the largest share of paying customers outside the US, followed by Canada, Australia, and Germany.

Regional Summary

regional_summary <- country_scored %>%
  group_by(continent_name) %>%
  summarise(
    Countries = n(),
    `Total Visitors` = sum(total_visitors),
    `Total Signups` = sum(total_signups),
    `Total Conversions` = sum(total_conversions),
    `Avg Signup Rate` = sum(total_signups) / sum(total_visitors),
    `Avg Conversion Rate` = ifelse(sum(total_signups) > 0,
                                    sum(total_conversions) / sum(total_signups), 0),
    .groups = "drop"
  ) %>%
  arrange(desc(`Total Visitors`)) %>%
  mutate(
    `Total Visitors` = comma(`Total Visitors`),
    `Total Signups` = comma(`Total Signups`),
    `Avg Signup Rate` = percent(`Avg Signup Rate`, accuracy = 0.1),
    `Avg Conversion Rate` = percent(`Avg Conversion Rate`, accuracy = 0.01)
  )

kable(regional_summary, align = "lrrrrrr")

continent_name	Countries	Total Visitors	Total Signups	Total Conversions	Avg Signup Rate	Avg Conversion Rate
Europe	33	8,040,308	397,818	14384	4.9%	3.62%
Asia	30	7,203,260	453,383	5704	6.3%	1.26%
North America	9	1,947,094	68,811	3329	3.5%	4.84%
Africa	11	1,073,714	131,938	1267	12.3%	0.96%
Oceania	2	619,161	34,614	2346	5.6%	6.78%
South America	7	593,121	69,880	929	11.8%	1.33%

Europe leads in both visitors (8.2M) and conversions (14.6K), with a 4.9% signup rate and 3.6% paid conversion rate.

Asia has similar traffic (7.7M visitors) but higher signup rates (6.3%) and lower conversion (1.3%).

Africa has a high signup rate at 12.3% but the lowest conversion rate at 0.95%. These markets have clear product interest but face barriers to paid plans.

Australia + New Zealand have a great combined conversion rate of 6.8%, which is higher than the US benchmark.