App Store Conversion Rate by Country: The Peer-Median Gap

Your App Store conversion rate varies by country, often sharply. The same listing that converts near 25% in the US App Store can convert far lower in storefronts where it isn't localized [5]. You can see your own rate per country in App Store Connect, but Apple's peer benchmarks compare you to a worldwide median, not a per-territory one [3]. That mismatch is where most indie devs misread their number.

The gap matters because your installs aren't spread evenly across the world. If your download volume leans toward high-traffic, lower-converting storefronts, your blended rate looks weak against a global benchmark even when each market is performing fine for what it is.

TL;DR:

• Your headline conversion rate is a territory-weighted blend. Segment it by Territory in App Store Connect to see the real per-country picture [1][2].
• Apple builds peer groups from category, business model, and download volume, not territory, so the median you compare against is a worldwide figure [3].
• An install base skewed toward high-volume storefronts like India, Brazil, and Indonesia can read as "below median" even when it converts normally for those markets [4].
• Fix the read: compare each territory to itself over time, weight by install volume, and localize the product page for the storefronts that actually drive your downloads.

For what counts as a good rate by category, the companion post on App Store conversion rate benchmarks covers the category dimension. This one is about the territory dimension, the part the benchmark tables leave out.

Table of contents

• Does App Store conversion rate vary by country?
• How do you see your conversion rate by country in App Store Connect?
• Why doesn't Apple's peer benchmark show conversion by country?
• How does a skewed install base distort your benchmark read?
• How do you read your per-territory conversion rate correctly?
• Which territories should you localize first?
• Reading the number by territory

Does App Store conversion rate vary by country?

Yes, and the spread is wide. The US App Store averaged around 25% page-view-to-install in the first half of 2024, but that figure is one storefront's number, not a global constant [5]. The same product page shown in a market where it isn't translated, priced in local currency, or matched to local expectations converts lower, because a big share of visitors bounce at the language barrier before the screenshots ever do their job.

Two forces move your conversion rate the most: the category you compete in, and the storefronts your installs come from. The category side is well documented and easy to look up. The territory side is the blind spot, because most published benchmarks report a single blended rate per category and never split it by country. So when you compare your app to "the 25% average," you're comparing a territory-weighted blend of your traffic against a territory-weighted blend of someone else's, and the two blends rarely match.

That's the core problem. A number that mixes a strong US storefront with a weak, unlocalized Indonesia storefront is an average of two very different realities. Averaging them tells you nothing actionable about either.

How do you see your conversion rate by country in App Store Connect?

Open App Analytics, select the Conversion Rate metric, and filter by Territory. Apple defines Conversion Rate as how often unique impressions turn into total downloads, and it lets you filter that metric by dimensions including territory and device [1]. Territory is defined as the App Store territory determined by the customer's billing address [2], so a "US" number reflects US-account customers, not just US IP addresses.

Two filters make the per-country view genuinely useful:

• Territory isolates one storefront so you can compare like with like. A conversion rate for Japan on its own is a real signal; the same number buried inside a worldwide blend is not [2].
• Source Type splits App Store Search, App Store Browse, and referrals. Apple notes you can see how conversion differs by source type [1]. Search traffic and Browse traffic convert differently, and the mix varies by country, so a territory's rate is really several rates stacked together.

You can combine these filters, so "Search traffic in Germany" is a single view rather than separate exports. Start there before you compare yourself to any external chart. Your own per-territory numbers are the only ones measured with your exact funnel definition. If you're unsure which stage of the funnel a weak territory is leaking at, the two-stage conversion funnel breakdown shows how to separate the tap-through stage from the page-conversion stage.

Why doesn't Apple's peer benchmark show conversion by country?

Because Apple's peer group benchmarks are built on three axes, and territory isn't one of them. Peer benchmarks compare your app to similar apps on metrics such as Conversion Rate, and Apple groups those peers by App Store category, business model, and download volume tier [3]. There is no territory grouping, which means the peer conversion-rate median you see is a worldwide comparison by construction, not a per-country one.

That's a reasonable privacy design. Slicing peer medians down to a single country and a single category would expose individual apps in thin markets. But it leaves a real analytical gap for you as the developer:

• Your own conversion rate segments cleanly by territory [2].
• The peer median you'd compare it against does not [3].

So you can know that your Brazil conversion rate is 14%, but Apple won't tell you what a comparable app converts at in Brazil specifically. You only get the worldwide peer median across all territories. Comparing a single-territory number to a worldwide median is an apples-to-oranges read, and it's the most common way indie devs misjudge whether their screenshots are underperforming or their storefront mix is just weighted toward tougher markets.

How does a skewed install base distort your benchmark read?

It pulls your blended rate toward whichever storefronts send the most traffic, which are often the lower-converting ones. Download volume is heavily concentrated by country. Across both app stores from November 2024 to November 2025, India led global installs with 19.1B downloads (17% of the world total), ahead of the US at 12.6B (11.2%), Brazil at 9.0B, and Indonesia at 6.1B [4]. An app with real traction in those markets carries a lot of high-volume, frequently-unlocalized traffic in its blend.

On iOS specifically the mix tilts differently, which is exactly why the blended number is deceptive. App Store downloads concentrate in a different set of leaders:

Source: AppTweak app downloads by country [4].

Read the two views together. Your overall install mix might look India-heavy, while your iOS install mix leans US, China, and Japan. Each of those storefronts has its own conversion reality, and your headline App Store rate is the volume-weighted average of all of them. If China and Japan convert below your US number because the listing isn't fully localized for them, your blended rate drops even though your US storefront is strong. Against a worldwide peer median, that blended rate looks like a screenshot problem. It's actually a localization-coverage problem hiding inside an average.

How do you read your per-territory conversion rate correctly?

Compare each territory to itself over time, not to a single worldwide benchmark. Your most reliable signal is the trend within one storefront: did Germany's conversion rate rise after you shipped localized screenshots, and did it hold. That answers the question a global median never can, which is whether a specific change worked in a specific market.

A practical reading routine:

• Rank your territories by install volume first. The storefronts driving most of your downloads set your blended rate. A 2-point move in your top territory outweighs a 20-point move in a market that sends 50 installs a month.
• Compare each top territory against its own history, not against the worldwide peer median. Use the peer median only as a rough sanity check on your overall app, understanding it's a global figure [3].
• Split each territory by source type. A low rate driven by Browse traffic (people who stumbled in) is a different problem than a low rate on Search traffic (people who searched a keyword and still didn't convert) [1].
• Isolate the language barrier. If a territory's rate is far below your US number and the listing there is English-only, you've likely found unlocalized-storefront drag rather than a weak creative.

This is where the territory view pays off. A single global number tells you that you're at 18%. The per-territory view tells you that you're at 27% in the US, 22% in the UK, and 9% across three unlocalized markets that happen to carry a third of your volume. Only the second version tells you what to fix.

Which territories should you localize first?

Localize the product page for the storefronts that carry the most of your own install volume, starting with the ones whose conversion rate lags your best territory. Don't localize alphabetically or by market size in the abstract. Pull your territory download report, sort by volume, and target the biggest storefronts where an unlocalized listing is dragging conversion down. That's where translated screenshots and captions move the most installs per hour of work.

Screenshots do the heavy lifting here, because they carry both the language and the cultural read of your listing. Translating the captions on your first three frames often lifts a lagging territory more than any other single change, since most visitors decide from the frames Apple shows in search before they scroll or read the description. For the order to work through markets and the per-region design rules, the 7-market localization priority guide and the broader App Store localization guide lay out which storefronts to prioritize and how the screenshots need to change for each. When you're ready to produce localized sets, you can iterate translated screenshot variants for each market in the screenshot builder rather than rebuilding every frame by hand.

Reading the number by territory

Your headline App Store conversion rate is an average of storefronts that behave nothing alike, and Apple's peer benchmark compares that average to a worldwide median it never breaks down by country [3]. The single most useful move is to stop reading the blended number as one signal. Segment by Territory, rank your storefronts by volume, and judge each one against its own trend [2].

Once you can see which specific markets are dragging your rate, the fix is usually localization, and the fastest lever is translated screenshots for the frames that appear in search. To calibrate what a healthy rate even looks like before you start, cross-check the dated conversion benchmarks by category so you know which published numbers are current and which are recycled, then hold your own per-territory results to that bar market by market.