We examined 123 public scorecards from the AppDrift ASO Score checker: 94 Google Play apps and 29 App Store apps. The scores describe one tool's listing checks, not store-assigned grades or measured download performance.
Correction — September 11, 2026: this article previously claimed that 108 apps had single-language store listings and that 94% failed localization. Those interpretations are withdrawn. The inspected collector sets Android language count to zero and reads the iOS app-language list; neither measures translated store metadata. The original component outputs and charts remain below as a historical record, not valid localization-coverage findings. Overall scores include that component and should not be treated as complete listing-quality measurements.
How we got this data
The 123 scorecards were created between May and July 2026. The tool combined checks on retrieved public fields into a 0–100 score. We preserve the original outputs rather than recalculate history using a new model. Some source fields were missing or unsuitable for their intended checks: the median overall output was 74, the highest was 81, and no score reached the model's A+ band.
The sample is self-selected: developers chose to run these checks, so it cannot represent the whole store. We also excluded subtitle findings and iOS keyword-field findings because the collector did not obtain those fields. The effect of this selection on the scores is unknown.
What do real ASO scores look like in 2026?
Scores cluster tightly. Nearly six in ten apps land in the 70s, and the entire sample fits between 40 and 81:
By the historical model's letter grades, 7 apps received A, 72 B, 32 C and 12 D. These are score outputs, not independently verified optimization levels. A score ceiling in this selected sample is not a ceiling for fully optimized listings.
Withdrawn interpretation: language fields did not measure localization
The saved model outputs recorded 116 of 123 scorecards with a D for the component labeled “localization,” a median component score of 10, and an earlier count of 108 interpreted as single-language listings. That last interpretation is withdrawn; the count has not been validated against matched storefront text. The title and description component medians were 100 and 95. The chart retains its original labels and values, including “Localization,” only to document what the model output looked like.
Chart reading: “Localization” above names an old proxy component. The 2% A-or-better share does not measure how many apps translated their listings. App-interface languages and store-listing localizations are different fields. A coverage study would need matched listing text and locale observations, a translation definition and a documented missing-data policy.
Before choosing a market to localize, check customer interest, app availability and your ability to review and support the language. This dataset did not compare the ranking return from translation with the return from an English title change.
What title and description checks recorded
In the original scorecards, median title usage was 90% of the character allowance and 81% of apps received A or better for the title component. Description length had a median of 2,418 characters and 90% received A or better. These are model checks and length summaries; they do not establish that the copy was persuasive or that more characters would improve performance.
A high title or description component grade means the listing passed the model's checks. It does not establish persuasive copy, keyword fit or a competitive advantage. Use the keyword research guide to assess those questions against the current app and storefront.
One nuance from the character data: while the median title is nearly full, 16% of apps still use less than half their title characters. If you're in that group, review whether useful, relevant wording is missing; using every available character is not a goal by itself — the exact limits per field and store are in our character limits reference.
The model flagged repeated keywords in 62 descriptions
62 of 123 apps were flagged for repeating a keyword too often in the description. Read those sentences before changing them: some repetition is natural, and this flag does not establish a ranking penalty. Remove wording that makes the description awkward or redundant. If you use AI metadata generation to prepare another draft, give that draft the same editorial review.
Does your app category matter?
The table shows median scores for categories with at least four apps in the sample:
| Category | Apps in sample | Median ASO score |
|---|---|---|
| Education | 14 | 78 |
| Finance | 23 | 76 |
| Health & Fitness | 12 | 75.5 |
| Productivity | 10 | 74 |
| Personalization | 6 | 74 |
| Tools | 8 | 73.5 |
| Lifestyle | 10 | 71 |
Seven points separate the category medians shown. Small, selected category groups and a composite score with the limitations above cannot establish how much variance is caused by category or execution. Use these as descriptive rows, not a benchmark for expected growth.
What to do with these numbers
1. Review the underlying fields. Use the ASO Score checker to identify items to inspect, then compare them with the actual storefront. A missing field or a low component grade needs verification before it becomes an editing task.
2. Verify localization directly. Open the intended store and language, inspect the actual title, description and screenshots, and record which content is translated. The withdrawn 108-listing interpretation cannot select a market or establish demand.
3. Choose one task the findings support. That might be clarifying a title, removing awkward repetition or reviewing a supported market. Record the change and assess actual store results; this scorecard dataset does not measure the effect of those actions.
Frequently asked questions
What does the median ASO score of 74 mean?
It is the median historical output of this model in a selected 123-app sample. It is not a store grade, an industry benchmark or evidence of download performance. The overall outputs include a localization component whose coverage interpretation is withdrawn.
Did this study find that 108 apps had only one localized listing?
No. That interpretation is withdrawn. The collector used app-language fields and a zero placeholder on Android, which do not establish translated store-listing coverage.
How was this data collected?
The sample contains 123 public scorecards generated between May and July 2026: 94 Android apps and 29 iOS apps. It is self-selected. Subtitle and iOS keyword-field findings were excluded, and the language-field interpretation has since been withdrawn.
Does a higher score guarantee more downloads?
No. The model flags fields for review. This dataset contains no experiment establishing a score-to-download relationship, and the historical composite includes a limited language proxy.
Methodology note: sample of 123 public scorecards (self-selected by the developers who ran them), scored May–July 2026. Percentages rounded to whole numbers. We'll re-run this analysis as the scorecard pool grows.
