THE VERDICT · SUPPORTED IN THIS SAMPLE
Most big jumps did not have an equally big match elsewhere.
In the countries and three day windows I could compare, about three out of four selected jumps did not coincide with another rise of at least 20 places in another eligible country.
That does not mean the app grew in only one country. Smaller rises elsewhere were common, and checking different windows found more matches.
See what I found ↓
MY QUESTION
One country looks great. What about the others?
Imagine an app shoots up a chart in the US. Before calling it a worldwide success story or copying its listing, I want to know what happened to the same app in other markets.
I looked for an app rising at least 20 chart places over three days.
I compared the same app identity in the same store during closely timed chart windows.
I checked whether any eligible country also rose at least 20 places.
I required at least two other eligible countries, with enough room to rise. These were selected chart events, not every app in every market.
MY THEORY VS THE RESULT
The matching jump was usually missing.
My theory
Most big climbs would not coincide with a rise of at least 20 places in another eligible country.
What I found
That pattern appeared in both stores and in the later period used to check it. It does not establish purely local growth.
one country → other eligible countries
Later checking period. Smaller rises elsewhere were possible and other time windows found more matches.
See the original result chart
Later checking period. “Elsewhere” means only the other eligible countries I observed.
These percentages count events with no matching rise of at least 20 places. They do not count all growth or every available country.
But smaller rises were common.
In that same later period, at least one other eligible country rose by 10 or more places in 55.3% of App Store events and 62.7% of Google Play events. Those smaller rises matter. I cannot turn “no equally big jump” into “no movement anywhere else.”
REAL APPS IN MY SAMPLE
What did the country charts actually show?
These examples are from the historical data in 2026. They show different patterns, not proof of why any app moved or claims of a client relationship.
APP STORE · UNITED STATES, UK, GERMANY
Freecash
It rose from 45 to 16 in the US. In the same window it rose from 62 to 51 in the UK and fell from 72 to 82 in Germany.
APP STORE · UNITED STATES, UK, JAPAN
Shop
It rose from 44 to 24 in the US. It also rose from 52 to 12 in Japan, a matching large rise, while moving from 27 to 13 in the UK.
GOOGLE PLAY · UNITED STATES, UK, GERMANY
Microsoft Authenticator
It rose from 89 to 36 in the US and from 40 to 19 in Germany. In the UK it rose from 27 to 8, a smaller rise of 19 places.
These cases illustrate the result; they were not used on their own to calculate the percentages.
TIMING CHANGES THE PICTURE
What if another country moved a few days earlier or later?
I also widened the timing window. More matching jumps appeared, but this check had complete coverage for only 12 App Store and 19 Google Play events. Searching more windows also creates more chances to find a match. It is a useful question for further research, not proof that success spread between countries.
MY ASO TAKEAWAY
Check the market before copying the story.
My ASO tip: When an app climbs in one country, check its chart history in the specific market you care about before calling it a global winner.
My ASO strategy: Compare the same app across markets and dates, then inspect local search results and listing changes separately. Keep track of what you observed and what you are only guessing.
My ASO warning: Chart position is relative to other apps. It is not a download count, a keyword ranking or evidence that a title or screenshot caused a rise.
WHAT I COULD NOT PROVE
Here is where the evidence stops.
- Why apps moved: I did not measure advertising, product changes, seasonality or local events.
- What happened everywhere: I observed a limited number of eligible countries per event, not every market.
- Search or downloads: These were free chart positions, not keyword rankings or private download numbers.
- Purely local growth: Smaller rises and differently timed rises appeared elsewhere, so the data cannot establish that activity was confined to one country.
- Independent events: The same app can appear in multiple retained events, and the two periods can share app identities.
SOURCES & METHODS
Want to check my work?
I used AppOrbit’s archived charts. I reran the supplied verifier to reconstruct selected events and comparison rows from those charts. It checks calculations against the archive; it does not independently verify the collector or prove why apps moved.
Regional differences in app popularity were already researched by others, including Wang and colleagues. I am not claiming to have discovered that broader idea first.
The source evidence and complete technical report are kept locally while third party redistribution rights are reviewed.
Read the original report and technical details
ASO RESEARCH · EXPERIMENT 5 · 27 OCTOBER 2026
A big app-chart jump in one country needs a wider check.
About three in four sharp climbs had no matching 20-place rise in the other eligible countries during the same three days. But that does not mean the app was growing only in one country.
The narrow prediction passed. Most sharp climbs lacked another equally large rise in the countries we could compare at the same time. Smaller rises were common, and allowing different timing found more large rises. The broader claim that these were purely local growth events is not established.The ASO question
You see an app jump from #70 to #15 in a country’s chart. It looks like a success story. You might study its app title, screenshots or positioning and wonder whether the same approach could work in another market.
Before drawing that conclusion, it helps to ask: was the same app climbing elsewhere too?
Our theory was that most large chart jumps would not coincide with an equally large climb in another observed country. We tested that precise prediction. We did not test whether the app’s listing caused the rise.
What we measured
- A sharp climb meant moving up at least 20 chart positions over three days.
- We compared the same app ID in the same store across countries.
- The app had to appear in at least two other countries’ starting charts.
- In those other countries, it had to start at #21 or lower, leaving room for a 20-place rise.
- Each comparison used complete top-100 free-app charts and closely aligned collection times.
For example, an app starting at #5 cannot climb 20 places. Counting that country as a failed match would make the theory look stronger unfairly, so we excluded it from this comparison.
When the same app had qualifying jumps in several countries on the same date, we counted one event and used its largest eligible rise as the starting point. We also removed overlapping three-day windows for the same app.
The main result
For events ending October 8 to October 26, 2026:
| Store | Sharp-climb events | Distinct app IDs | Events with no matching 20-place rise elsewhere | Share |
|---|---|---|---|---|
| Apple App Store | 103 | 49 | 77/103 | 74.8% |
| Google Play | 59 | 35 | 44/59 | 74.6% |
Apple: 77 of 103 sharp climbs had no matching-size rise in another eligible country during the same three-day window.
Google Play: 44 of 59 sharp climbs had no matching-size rise in another eligible country.
“Elsewhere” means the eligible countries we observed, not every country where the app was available. We compared an average of 2.67 other countries per Apple event and 2.49 per Google Play event.
The pattern also appeared in the earlier dates
We applied the same rules to events ending September 22 to October 7, then checked the later dates separately.
| Store | Earlier events | Earlier share with no matching rise | Later events | Later share with no matching rise |
|---|---|---|---|---|
| Apple App Store | 91 | 71.4% | 103 | 74.8% |
| Google Play | 51 | 82.4% | 59 | 74.6% |
Before calculating results, we required the share to exceed 50% in both stores and both periods, with at least 30 events per store-period and additional stability checks. The results met that rule.
But “no matching jump” is not “no growth elsewhere”
This distinction changes how the finding should be used in ASO. In the later period, smaller rises in other countries were common:
| Store | Another country rose 20+ places | Another country rose 10+ places | Another country rose at least 1 place |
|---|---|---|---|
| Apple App Store | 25.2% | 55.3% | 75.7% |
| Google Play | 25.4% | 62.7% | 89.8% |
Although only about 25% had another 20-place rise, 55% of Apple events and 63% of Google Play events had another rise of at least 10 places.
That means the data supports “the big jump usually was not matched at the same size and time”. It does not support “the app only improved in one country”.
Real app examples
We selected both kinds of result: a large rise without a matching rise, and a large rise with one. These are illustrative cases from the later period, preferring US or UK starting markets and then the earliest available date. They were not selected to calculate the percentages.
Freecash - Get Paid Real Money · Apple App Store
Three-day window: 2026-10-06 to 2026-10-09 UTC dates.
| Country | Comparison | Starting rank | Ending rank | Movement |
|---|---|---|---|---|
| United States | Main jump | #45 | #16 | Up 29 places |
| Germany | Other observed country | #72 | #82 | Down 10 places |
| United Kingdom | Other observed country | #62 | #51 | Up 11 places |
Shop: Track, Pay, Discover · Apple App Store
Three-day window: 2026-10-11 to 2026-10-14 UTC dates.
| Country | Comparison | Starting rank | Ending rank | Movement |
|---|---|---|---|---|
| United States | Main jump | #44 | #24 | Up 20 places |
| United Kingdom | Other observed country | #27 | #13 | Up 14 places |
| Japan | Other observed country | #52 | #12 | Up 40 places |
Shop: Track, Pay, Discover · Google Play
Three-day window: 2026-10-05 to 2026-10-08 UTC dates.
| Country | Comparison | Starting rank | Ending rank | Movement |
|---|---|---|---|---|
| United States | Main jump | #66 | #41 | Up 25 places |
| Germany | Other observed country | #58 | #76 | Down 18 places |
| United Kingdom | Other observed country | #34 | #21 | Up 13 places |
Microsoft Authenticator · Google Play
Three-day window: 2026-10-05 to 2026-10-08 UTC dates.
| Country | Comparison | Starting rank | Ending rank | Movement |
|---|---|---|---|---|
| United States | Main jump | #89 | #36 | Up 53 places |
| Germany | Other observed country | #40 | #19 | Up 21 places |
| United Kingdom | Other observed country | #27 | #8 | Up 19 places |
Freecash’s US rise was accompanied by a smaller UK rise and a fall in Germany. Microsoft Authenticator’s US rise was accompanied by a qualifying rise in Germany and a slightly smaller rise in the UK. Both patterns exist in the data.
Were the other countries behaving differently from ordinary chart movement?
A 20-place rise is a high bar, so finding no matching rise might be common anyway. We checked that instead of treating the 75% figure as proof of unusual local behavior.
For every other-country comparison, we calculated how often other apps starting within five chart places rose at least 20 places in that same country and date window. We then compared that reference rate with the focal app’s rate, giving each event equal weight.
| Store | Same app: other-country jump rate | Nearby-ranked apps: reference jump rate | Difference |
|---|---|---|---|
| Apple App Store | 10.9% | 10.4% | +0.5 percentage points |
| Google Play | 12.9% | 6.1% | +6.8 percentage points |
On Apple, the rates were almost the same. We found no clear extra tendency for the same app to have another large rise compared with that ordinary-movement reference.
On Google Play, the same app had a large rise elsewhere more often than the reference apps: 12.9% versus 6.1%. There was evidence of shared movement across countries, even though most individual events still had no second 20-place rise.
This table measures the average fraction of other countries with a rise. The main table measures whether an event had any such country. These are different denominators and should not be compared as though they were the same percentage.
What if countries move a few days apart?
We also looked for a 20-place rise in three-day windows ending up to three days earlier or later. This required more complete chart history, leaving only 12 later Apple events and 19 later Google Play events.
Within those same small subsets, the share with no matching rise changed as follows:
| Store | Events with full timing coverage | No matching rise in original window | No matching rise after checking earlier/later windows |
|---|---|---|---|
| Apple App Store | 12 | 83.3% | 41.7% |
| Google Play | 19 | 89.5% | 52.6% |
More matches appeared when we widened the timing check. This weakens any claim that the activity was strictly confined to one country.
The timing check is exploratory and small. Searching more windows also creates more opportunities to find a rise by chance. It does not establish that success spread from one country to another.
What this means for ASO research
A sharp rise in one country is not enough evidence to call an app a worldwide ASO success. The same app may rise less elsewhere, rise at a different time, or fall in another market.
The useful research step is to check the app’s country-by-country trajectory before using its listing as a model for another market. This study provides context for choosing and interpreting case studies. It does not show that copying a listing works, or identify which changes caused the chart movement.
Publishable finding: “Across 304 selected sharp-climb events in free-app charts, a majority lacked another observed 20-position rise over the same three-day window. The pattern repeated on later dates in both stores. Smaller rises and differently timed rises were common, so the result should not be interpreted as purely local growth.”Open the methods, sample checks and verification
Source data and selection
The archive covers September 19 to October 26, 2026. We selected the latest complete, valid top-100 free-app chart for each store-country-UTC date, producing 671 daily charts. This UTC grouping differs from experiment 4’s local-calendar grouping and therefore has a different chart count.
Each three-day window needed endpoint captures 60 to 84 hours apart. For another country to qualify, its starting and ending captures each had to fall within 12 hours of the corresponding main-country capture. A six-hour check was also run.
We identified 3,954 store-app-ending-date combinations with at least one large rise. Of those, 3,416 lacked two eligible other-country comparisons. Removing 234 overlapping windows from the remaining 538 left 304 retained events. This is a selected sample of apps already charting in multiple countries, not a census of all app jumps.
| Store | All retained events | Distinct app IDs | Other-country comparisons | Mean other countries per later event | Maximum events from one app |
|---|---|---|---|---|---|
| Apple App Store | 194 | 56 | 514 | 2.67 | 8 |
| Google Play | 110 | 43 | 281 | 2.49 | 7 |
The analysis follows identical store app IDs. Separate regional IDs are not merged into a brand, and Apple and Google Play IDs are not merged. Apple’s broad apps chart includes games; Google Play’s apps chart excludes them. The stores are analyzed separately.
The dataset contains Brazil, China, Germany, the United Kingdom, Indonesia, India, Japan, South Korea, the United States and Vietnam, with no Google Play coverage for China. France appeared only in the initial hypothetical example and is not in this dataset. Eligibility rules further restrict which countries appear in individual comparisons.
How events and comparisons were chosen
For each store-app-date, we selected the largest qualifying rise that had at least two eligible other countries. Ties were resolved alphabetically by country. Events were retained chronologically; a new event’s starting date had to be after that app’s previous retained ending date. A simultaneous multicountry rise was therefore not counted as a separate event for every qualifying country.
Each retained event has equal weight. Within an event, other countries have equal weight when calculating average jump rates. We did not give every country or app equal total weight across the study. Repeated app IDs are handled in one of the stability checks, and a once-per-app sensitivity is reported below.
We counted a rise only when both actual ranks were known. If an app was absent from a complete ending top-100 chart, it could not count as a 20-place rise. We did not invent its exact rank outside the chart. Missing charts were excluded, not treated as an app disappearing.
Alternative checks on the later period
| Store | Check | Events | No matching 20-place rise |
|---|---|---|---|
| Apple App Store | Main comparison | 103 | 74.8% |
| Apple App Store | Country captures within 6 hours | 64 | 76.6% |
| Apple App Store | Only first event per app across the study | 8 | 87.5% |
| Apple App Store | Other-country starting ranks 41 to 100 | 64 | 75.0% |
| Google Play | Main comparison | 59 | 74.6% |
| Google Play | Country captures within 6 hours | 27 | 81.5% |
| Google Play | Only first event per app across the study | 12 | 83.3% |
| Google Play | Other-country starting ranks 41 to 100 | 31 | 77.4% |
The first-event check keeps only an app’s first retained event across the whole study, then selects those in the later period. That leaves just 8 Apple and 12 Google Play events, so it is a small check. The six-hour comparison reselects qualifying main countries and removes overlapping windows again; it is not necessarily a subset of the main events.
The starting-rank-41-to-100 check keeps the main events but restricts other-country comparisons to apps with more room to rise, requiring at least two such comparisons. It still excludes apps already high in those other charts.
Later results by country of the main jump
These are descriptive slices. Small country counts are not reliable standalone estimates.
| Store | Country of main jump | Events | No matching rise in eligible other countries |
|---|---|---|---|
| Apple App Store | Brazil | 15 | 80.0% |
| Apple App Store | China | 2 | 100.0% |
| Apple App Store | Germany | 15 | 60.0% |
| Apple App Store | United Kingdom | 13 | 61.5% |
| Apple App Store | Indonesia | 3 | 100.0% |
| Apple App Store | India | 11 | 81.8% |
| Apple App Store | Japan | 15 | 93.3% |
| Apple App Store | South Korea | 15 | 80.0% |
| Apple App Store | United States | 10 | 60.0% |
| Apple App Store | Vietnam | 4 | 50.0% |
| Google Play | Brazil | 7 | 71.4% |
| Google Play | Germany | 10 | 70.0% |
| Google Play | United Kingdom | 8 | 87.5% |
| Google Play | Indonesia | 7 | 85.7% |
| Google Play | India | 0 | No qualifying events |
| Google Play | Japan | 3 | 66.7% |
| Google Play | South Korea | 6 | 66.7% |
| Google Play | United States | 13 | 61.5% |
| Google Play | Vietnam | 5 | 100.0% |
Uncertainty and stability
We ran two separate 5,000-draw resampling checks: one keeping all events for each app ID together, and another keeping blocks of seven observed event dates together. The middle 95% of the resulting later-period no-matching-rise estimates was:
| Store | Resampling method | Lower end | Upper end |
|---|---|---|---|
| Apple App Store | Resample app IDs | 65.6% | 83.8% |
| Apple App Store | Resample blocks of dates | 68.3% | 80.6% |
| Google Play | Resample app IDs | 63.8% | 84.9% |
| Google Play | Resample blocks of dates | 66.2% | 84.3% |
Both methods put the lower end above 50% in both stores. Neither captures every source of dependence across apps, countries and dates. The periods also share many app IDs. This was a later-date replication using fixed rules, not a newly collected independent experiment or a test of a fitted prediction model.
For the ordinary-movement comparison, the app-resampling range for the later Apple excess was approximately -3.7 to +5.2 percentage points. For Google Play it was +0.9 to +12.8 points. The date-block ranges also included zero for Apple and stayed above zero for Google Play. These ranges are conditional on the selected sample and the reference method.
The reference apps share country, dates and nearby starting ranks, but not necessarily category, brand strength or marketing exposure. The reference is descriptive. It is not a randomized or fully adjusted causal control.
Verification
A separate script reconstructed the main-country selections, eligible other countries and the non-overlapping event windows from the source charts. It checked all event and comparison rows in both the 12-hour and six-hour analyses, recalculated reference-app sets and jump counts, and rebuilt the shifted-window timing outcomes. It also recalculated the main early, later and full-period totals. All checks passed.
This verifies the calculations against the archived source. It does not independently validate the original chart collector. The local protocol was recorded before calculation, but was not externally preregistered, and the same dataset had already been used for other research questions.
Limits on interpretation
Chart places are relative positions, not download counts. A 20-place move in one country does not necessarily represent the same number of downloads as a 20-place move in another. A rise can also happen when competing apps fall.
We measured neither keyword rankings nor changes to listings. Advertising, local events, seasonal demand, product updates and other explanations remain unmeasured. The result cannot identify a cause or establish that a listing tactic transfers between markets.
Research context and evidence
Geographic differences in app popularity are already documented. Wang and colleagues’ 2024 study, Global Prosperity or Local Monopoly? Understanding the Geography of App Popularity, examined a year of iOS popularity data across 154 regions. Our contribution here is the narrower, reproducible comparison of short chart jumps in the same apps across countries.
Source: AppOrbit, pinned commit 9621042dde5ad42193bc84c8b54e5bf9b2d4e9eb. The evidence download includes the source chart rows, snapshot index, historical name data, written rules, analysis code, events, comparisons and verification. AppOrbit’s database rights use ODbL 1.0; that license does not cover third-party app-store content. The original license is included.
This is experiment 5 in an exploratory ASO series. Earlier unsupported findings remain in the record. The result here meets the written majority rule, while the additional checks restrict the claim we can responsibly make.
The ZIP is built into this HTML file. Open the saved report in a browser to use the download button.
Keep exploring
All ASO experiments · See why US research misses local leaders · Research competitors · Choose markets to prioritize