
An app intelligence and ASO platform with keyword rankings, competitor research and download estimates.
Sensor Tower is a research tool, not a growth case. Founders use its estimates to size markets and spot rivals buying traffic: Migraine Buddy showed about $70K a month from 30K iOS downloads, versus roughly $50K claimed. Lesson: use it to find narrow painkiller niches and judge competition by the top three results, treating its figures as estimates.
From Josh (Blue Throne), Sensor Tower estimates
Use Sensor Tower estimates to confirm a painkiller niche earns real money. Steve Young used Sensor Tower to estimate that Migraine Buddy makes about $70K a month from 30K iOS downloads. That showed a narrow problem app can pay off even when the incumbent is weak.
The same app is described as making about $50,000 a month despite weak onboarding and a weak paywall. The idea is to look for incumbents with these gaps in painkiller niches, where users convert well enough to take a hard paywall.
Check Sensor Tower to see whether a keyword's top rivals buy traffic. When judging keyword competition, look at the top three results: their review counts and how many put the keyword in the title. Then check Sensor Tower or the Meta ad library to see whether they run paid.
If few apps target the keyword in the title and the number two or three app has only 100+ reviews, you can probably beat them. Steve outranked apps with far more ratings and revenue through better title and subtitle optimization. Quote: "I don't care what any ASO tool says about the difficulty."
Size a market by cutting Sensor Tower into categories and reading the top apps. Josh (Blue Throne) cuts Sensor Tower into logical categories and looks at the top 30 apps, which are usually the lion's share of the market. He uses this to narrow a category until you can credibly claim number one.
Flow narrowed health to women's health and became the first women's health unicorn, raising $200M+ from General Atlantic at a $1B+ valuation. Runna targeted Couch to 5K and was acquired by Strava. The category must stay believable, with real competitors and its own ecosystem. The revenue figures in that episode are Sensor Tower estimates.
Strong for iOS, with accurate traffic and difficulty data, batch keyword rankings, keyword spy, a translation tool and a Today featuring tracker. Weaker on Google Play difficulty, with an 80-keyword cap per app on his plan. He has used it since about 2014-15.
Use Sensor Tower estimates to confirm a painkiller niche earns real money. Steve Young used Sensor Tower to estimate that Migraine Buddy makes about $70K a month from 30K iOS downloads. That showed a narrow problem app can pay off even when the incumbent is weak.
The same app is described as making about $50,000 a month despite weak onboarding and a weak paywall. The idea is to look for incumbents with these gaps in painkiller niches, where users convert well enough to take a hard paywall.
Check Sensor Tower to see whether a keyword's top rivals buy traffic. When judging keyword competition, look at the top three results: their review counts and how many put the keyword in the title. Then check Sensor Tower or the Meta ad library to see whether they run paid.
If few apps target the keyword in the title and the number two or three app has only 100+ reviews, you can probably beat them. Steve outranked apps with far more ratings and revenue through better title and subtitle optimization. Quote: "I don't care what any ASO tool says about the difficulty."
Used to cross-check AppMagic's numbers. It showed $600,000 in revenue on 100,000 downloads for Endel, about a 6 to 1 ratio.
Size a market by cutting Sensor Tower into categories and reading the top apps. Josh (Blue Throne) cuts Sensor Tower into logical categories and looks at the top 30 apps, which are usually the lion's share of the market. He uses this to narrow a category until you can credibly claim number one.
Flow narrowed health to women's health and became the first women's health unicorn, raising $200M+ from General Atlantic at a $1B+ valuation. Runna targeted Couch to 5K and was acquired by Strava. The category must stay believable, with real competitors and its own ecosystem. The revenue figures in that episode are Sensor Tower estimates.
Used to estimate rival downloads and revenue, such as Migraine Buddy at about $70K a month from 30K iOS downloads, and to check whether an app buys traffic.
Josh sizes markets by cutting Sensor Tower into categories and looking at top apps, and the source's revenue figures are Sensor Tower estimates.
Estimates Mad Muscles at over $200,000 a month, but only sees app store revenue and misses web checkout sales.
Estimates Photo Revive AI at about $500K a month, matching AppFigures. Used as one of three tools to cross-check revenue estimates.
Shows Notepad has no ad-supported channels, so its traffic is purely organic. It was cross-checked against AppMagic to judge estimate accuracy.
It shows about 700,000 downloads and $300,000 revenue in a month for the simple remote app, plus a $2.99 weekly price that hints at price tests. Most people open it first, though it matches AppFigures on top countries and scale.
Its estimates put Cal AI's revenue above $800,000 a month and downloads near 300,000, which the speaker compared with Similarweb's visit counts.
Its free tier showed the top Instagram story app, InSave, making about $50K a month. Chatbot apps below ChatGPT all showed $50K or more, yet that market is a red ocean.
Shows Calm earning over $5 million a month, with Android a much smaller share than iOS, and exposes its pricing history. The figures are rough estimates.
It puts Genie at about $300K a month and its closest rival near $100K. Its top in-app purchases view shows which price points earn most for a competitor.
Used to check release dates, ad activity and keyword rankings for competitor apps. It showed no advertising for YNAB.
Used to research the top apps ranking for the Wordle keyword, checking launch dates, downloads, revenue, in-app purchases and name or price changes. Its 90-day view showed the paid app's ranks climbing at the start of January.
It may show fairly clear data on a competitor's organic versus paid traffic, but it is better to verify it yourself by checking their ad campaigns.
It shows the scanner app advertising on YouTube and six more networks, and gives ballpark revenue and download estimates. It also reveals when rivals run no paid marketing.
Used to check a competitor's iOS revenue before committing to an idea. It reports less revenue than an app really earns, and is one of two tools needed to validate an idea.
Its free sign-up shows a competitor's iOS revenue, such as for the co-parenting app Talking Parents. The figures are not 100% accurate but come close to real revenue, giving a ballpark of market size.
Barnard has checked its estimates against his own apps and found they can be orders of magnitude off. It is useful only as a ballpark for competitors.
It is the source of the download and revenue estimates for Subway Surfers and its clones.
Used to confirm Grammarly's revenue near $300K a month, and its ad listings matched the Facebook Ad Library.
Used to check keyword traffic (above 5 is decent, 'celebrity voice changer' scored 6.5) and the revenue of the top five or six apps. Its estimates ran within 20 to 30 percent of his own apps' real numbers, and its ad intelligence shows competitor creatives on Instagram and YouTube.
It was one of three tools used to analyze 163 keywords across Scribd and its competitors.
Suggested alongside Mobile Action for pulling traffic and difficulty scores on misspellings. Combined typos can add up to low overall difficulty.
Mobile Action's search traffic score now matches Sensor Tower. Steve uses it alongside Mobile Action to cross-check keyword difficulty, since two tools agreeing on low competition is a strong signal.
Scores traffic from 0.5 to 10, so 5.7 equals 57, and difficulty under 3 counts as low. Steve finds it more accurate for Google Play traffic.
Its traffic scores guide keyword selection, and a score under 20 (under 2 on its scale as transcribed) is usually not worth targeting. Relevant keywords with low competition around 0.5 can still be used.
Used to check competition scores on a few shortlisted keywords, such as 'slide shake', rather than on the whole suggestion list.
Useful for checking real search volume on terms like SWOT analysis, and it offers a free trial for small budgets. Volume for the SWOT terms was low.
A Sensor Tower keyword difficulty under 3 counts as low competition. When it agrees with Mobile Action, the app can usually rank very high for that keyword.
Used to filter keywords: look for traffic above 1 and competition of 3.0 or lower when an app gets under 100 organic downloads a day.
Its traffic scores proved more accurate than Mobile Action's. One client reached the top five for six or seven keywords with no download gain because Sensor Tower showed the real traffic was low.
Steve pulls keyword traffic and difficulty data there, since it allows unlimited keywords across separate searches.
Its difficulty score rates almost every Google Play keyword as hard, usually 5, 6 or 9 and never under 3, even where client apps already rank. One keyword rated 6.4 was already ranking number 7. It was still one of the most accurate tools on iOS.
Flagged 70 low-difficulty keywords but only about 41% reached the top ten, and on the kids app just 6 of 21 (about 28%). Steve left it because its search score seemed off and now uses it only as a cross-check.
Uses its own algorithm for search scores instead of Apple's number, so they drift from Apple's data. It showed 2.2 where Apple showed 5, and 0.5 where Apple was about 10, so Steve is moving away from it for volume.
Its data shows most of Yelp's users are in the U.S. and none in Mexico, which supports using the Spanish Mexico localization for U.S. keywords.
One keyword scored 4.9 (hard) on Sensor Tower yet had a 93% chance of ranking on Mobile Action, and ranked number three. Another scored 2.8 and was right about being low competition, with the app ranking number five. It also publishes a guide to which localizations each country's store indexes.
Ranked second behind Mobile Action on data accuracy but has the better interface. It is used to double-check optimization data, and a comparison against Apple's real App Store data is planned.
Used with Mobile Action to check traffic and difficulty scores for shortlisted name keywords before choosing an app name.
Paired with Mobile Action it gives the best keyword data, and when both show low competition a keyword can rank first or second. It has no equivalent to keyword intelligence, but holds 80 keywords and resets faster by deleting an app.
Gives keywords a traffic score from 1 to 10 and a difficulty score, and is best paired with MobileAction. It was right on a keyword with 2.8 difficulty that ranked number five despite a 67% MobileAction chance, but rated another at 4.9 difficulty that ranked number three.
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