Should I trust an app that just reached the top 20?

ASO RESEARCH · EXPERIMENT 4 · 27 OCTOBER 2026

Before choosing an app to study, check how long it has been near the top.

A high app-chart position today does not tell you whether that app will still be there next week. In our test, a week of chart history made a big difference.

The ASO question

You are researching successful apps. You open the chart, find an app near the top and start studying its title, screenshots and description.

But did you find an app that stays popular, or one that appeared near the top only recently?

Our theory: new top-20 arrivals are more likely to fall out of the top 20 over the following week than established apps at similar starting positions.

Why this matters for ASO

Your competitor shortlist shapes what you study. If you choose apps from a single chart snapshot, you may spend time examining a temporary chart appearance.

The practical implication is to check an app’s recent chart history before treating it as a benchmark for sustained visibility. This is an implication for ASO research, not proof that the more persistent apps have better screenshots, keywords or conversion rates.

What counts as a new arrival?

We compared two groups:

• New chart arrivals: apps in the top 20 today that were outside the top 20 on all seven previous days.
• Established chart apps: apps in the top 20 today that were also there on at least five of the previous seven days, including yesterday.

“New” means new to that chart’s top 20 after a week outside it. It does not mean a newly launched app. A familiar app such as DoorDash can qualify.

Each newcomer was paired with an established app in the same store, country and date, starting no more than three chart positions apart. For example, a newcomer at #13 might be compared with an established app at #12.

The main result: what happened a week later?

These are the results for the later event dates, October 8 to October 19, 2026. Each pair contains one newcomer and one established app.

Apple: 45 of 69 new arrivals were outside the top 20 a week later. Only 13 of their 69 established comparison apps were outside it.

Google Play: 42 of 67 new arrivals were outside the top 20 a week later, compared with 18 of their 67 established comparison apps.

These are positions on day seven. An app could leave the top 20 during the week and then return, so “outside after seven days” is different from “left at any point”.

Did it happen in the earlier dates too?

Yes. Using the same rules on September 26 to October 7 produced the same direction and a large difference in both stores.

Before calculating outcomes, we required a gap of at least 15 percentage points in both date periods and both stores, with at least 30 pairs in each group and a further stability check. The study met those requirements.

How quickly did the apps leave?

In the later-date sample, the share outside the top 20 at each checkpoint was:

On Apple, new arrivals appeared in the top 20 on an average of 2.5 of the next seven daily charts. Their established comparison apps appeared on 6.0. On Google Play, the figures were 3.4 days and 5.4 days.

The Apple newcomer exit rate was slightly lower on day seven than on day three because some apps returned. These checkpoints do not describe a one-way, permanent disappearance.

Real app examples, including exceptions

The following are US chart pairs from the later period. For each store, we selected the earliest pair where the newcomer left and the established app stayed, plus the earliest pair where the newcomer remained. These illustrate the findings; they are not the basis for the overall percentages.

KICK and PDF Reader illustrate the pattern. DoorDash and McDonald’s show the exception: a new chart arrival can stay in the top 20 or climb higher. The result helps describe a group; it does not predict every individual app.

App names are historical names from the archived metadata available before the starting chart capture. “Outside top 100” means absent from a complete top-100 chart. It does not mean removed from the store.

Could this just be because newcomers started lower?

Newcomers did start slightly lower on average. In the main later-date comparison, the average starting ranks were #12.86 versus #12.12 on Apple, and #15.30 versus #14.37 on Google Play.

We therefore repeated the matching with a stricter rule: the apps could start only one chart position apart. The difference remained large: 62% versus 16% on Apple, and 67% versus 26% on Google Play.

We also used a stricter definition of “established” and checked the result after counting each newcomer app ID only once across countries.

The stricter matches still do not create identical apps. Category, advertising, brand strength and starting position may differ. The evidence supports an association between prior chart persistence and later persistence, not a causal explanation.

What can we publish from this?

The ASO lesson: one day’s rank is a limited basis for choosing an app to study as an example of sustained chart visibility. Looking back over the previous week gives useful context.

This does not tell us to ignore new arrivals. It tells us to distinguish an emerging app from an established chart performer before drawing lessons from its listing.

What we did not prove

We did not test keyword rankings, store-page conversion or private download counts. We did not establish why apps rose or fell. A brief chart appearance might reflect advertising, seasonality or another event, but those explanations were not measured.

We used broad free-app charts, not a list of direct competitors within one category. The earlier fitness-app example explained the idea; this was not a fitness-category study. Apple’s broad apps chart includes games, while Google Play’s apps chart excludes games, so we kept the stores separate.

Finally, this was a short historical study. The later Apple sample covers seven markets, and the later Google Play sample covers nine, with uneven numbers of pairs. It is not a representative estimate for every app or country.

Research context and sources

Chart survival is an existing research topic. Lee and Raghu’s 2014 study, Determinants of Mobile Apps’ Success: Evidence from the App Store Market, examined survival in Apple’s top-grossing 300 chart. Our contribution here is a specific, reproducible comparison of new and established top-20 free-chart apps at nearby starting ranks. We do not claim to have discovered chart persistence for the first time.

Source data: AppOrbit, pinned commit 9621042dde5ad42193bc84c8b54e5bf9b2d4e9eb. The saved evidence includes chart rows, snapshot metadata, historical app names, protocol, code, matched pairs, results and verification. AppOrbit’s database rights are licensed under ODbL 1.0; its license does not grant rights to third-party app-store content. The original license and data-format documentation are included.

For the distinction from search rankings, see Apple’s discoverability documentation, which describes text relevance and customer behavior among search-ranking factors. That documentation supplies context, not evidence for our measured chart-survival result.

This is experiment 4 in an exploratory ASO series. The earlier unsupported results remain part of the record: experiment 1 did not support the proposed reactive-editing pattern; experiment 3 found little extra forecasting value from recent rating growth. Experiment 2 measured differences between countries’ app charts.

The evidence ZIP is built into this HTML file. Save the report and open it in a browser to use the download button. The report uses no external fonts, scripts or images.