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Small tools. Useful answers.
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13,220 daily rank observations across 452 tracked keyword series: iOS ranks moved 9.48 positions a day on average, but the top 10 was unchanged 72.5% of days.
We examined 26,972 listing snapshots of 278 apps. Explore observed title, description and release-note changes, with the subtitle findings clearly withdrawn.
862 selected keyword checks show lower average model scores for two- and three-word phrases. Read the sample, proxy-score and collection limits before applying them.
Check app-name search matches, confirm availability in App Store Connect and investigate trademark rights before choosing your final title.
Understand Apple Ads popularity, autocomplete and ASO tool estimates. Learn what each source measures and how to compare relevant keywords without inventing search counts.
Compare eight options for replacing data.ai workflows after Sensor Tower's acquisition: market estimates, own-app analytics and listing optimization.
Check app keyword difficulty with a free tool. Understand top-ten scores, caching, platform differences and why popularity proxies are not search counts.
604 selected keyword checks averaged 61.7 on iOS and 44.1 on Google Play. Compare model-score distributions with clear sampling and collection limits.
Three ways to check exactly where your app ranks for any keyword on the App Store or Google Play — manual, free tools, and daily tracking — plus what a good rank actually is.
What developers actually recommend on Reddit in 2026: real prices, real complaints and an honest verdict on each tool — a shortlist, not a vote count.
A 123-app scorecard sample had a median model score of 74. See the distributions, review flags and corrected limits on language and localization findings.
Find keyword candidates in competitor listings and observed rankings. Build a useful gap list while separating visibility, demand and hidden metadata.
