How to Research App Store Competitors in Under 60 Seconds
Most app store competitor research is a chore. You open SensorTower in one tab, the App Store in another, paste app names into a spreadsheet, and spend 30 minutes building a picture that is out of date before you finish. If you are an indie developer working alone, that kind of friction is enough to skip the app store competitor research entirely — which is exactly how people end up targeting keywords they have no realistic chance of ranking for.
This tutorial shows you a faster approach: running App Store competitor research directly inside Claude using App Store Operator. App Store Operator is an MCP server — a local tool that connects to Claude as an extension — that pulls App Store metadata and SensorTower estimates into your workflow in a single query. It is free, requires no subscription, and no API key to manage. You will have a full competitive snapshot — estimated downloads, estimated revenue, rating data, publisher country, and top markets — in under 60 seconds, without leaving Claude.
The worked example throughout uses the keyword "meditation" in the US App Store, a competitive category with clear patterns worth examining.
What You Get From a Competitor Research Query
Before walking through the setup, it helps to understand what the data looks like and why each field matters for iOS competitive analysis.
App Store Operator returns the following for each top-ranking app in a keyword search:
Estimated monthly downloads. The single most important number. It tells you whether a keyword is actually generating traffic or just looks impressive. A keyword where the top app does 3,000 downloads per month is a different opportunity than one where the top app does 300,000. Most indie developers underestimate how much download volume varies across keywords at the same apparent "difficulty" level.
Estimated monthly revenue. This tells you whether the category is monetising. Some keywords attract a lot of downloads but generate almost no revenue — often because users expect the apps to be free or because the apps in that category have not figured out a sustainable monetisation model. Revenue estimates help you avoid building into a market where no one pays.
Rating count and rating score. Rating count is a rough proxy for download history — apps with 50,000 ratings got there through years of accumulated installs. Apps with 200 ratings are relatively new or serving a niche. Rating score matters because a dominant app with a 3.4 average is vulnerable; users are telling you they want something better.
Publisher country. Tells you who you are competing with. Individual developers in smaller markets compete differently from companies with dedicated ASO teams. This is useful context when you are trying to understand whether a market leader has the resources to respond to a new entrant.
Top markets. Shows which countries are driving the most downloads for each app. If a keyword is dominated by apps whose primary market is Japan or Germany, and your app is targeting English-speaking users, you may have more room than the headline competition suggests.
Together, these data points let you answer the core question: is this keyword worth entering, and if so, where is the opening?
Setting Up App Store Operator
App Store Operator is free with no subscription required. It is an MCP server, which means it runs locally on your machine and connects to Claude as a tool extension. The install is one command:
npx -y app-store-operator@latest
Run that in your terminal. It downloads and installs the server, then makes it available inside Claude Desktop and Claude Code automatically. There is no configuration file to edit and no API key to manage. The server fetches App Store data through the iTunes Search API and SensorTower estimates through a headless browser, caching results for 24 hours so repeated queries on the same keyword are fast.
Once the command completes, open Claude. App Store Operator is ready.
How to Research App Store Competitors
With App Store Operator running, you can query any keyword in any country store. The query format is conversational — you are asking Claude to do research, not running a command with flags.
For the meditation keyword in the US store, your query looks like this:
Research the top competitors for the keyword "meditation" in the US App Store.
Claude passes this to App Store Operator, which searches the App Store for that keyword, identifies the top-ranking apps, pulls their metadata, fetches SensorTower estimates for each, and returns a structured report. The whole round-trip takes under 60 seconds.
The response you get back includes a breakdown per app: name, developer, rating count, rating score, estimated monthly downloads, estimated monthly revenue, publisher country, and which markets are driving the most installs.
How to Interpret Competitor Research Data
Here is what the meditation keyword data reveals, and how to read it for a real iOS competitive analysis.
When you search "meditation" in the US App Store, the top results include well-established apps with years of download history. A typical result set looks like this:
- Position 1: ~420,000 lifetime ratings, est. 175,000 monthly downloads, est. $2.1M monthly revenue. Publisher: US company.
- Position 2: ~185,000 lifetime ratings, est. 88,000 monthly downloads, est. $1.3M monthly revenue. Publisher: US company.
- Position 3: ~140,000 lifetime ratings, est. 52,000 monthly downloads, est. $290,000 monthly revenue. Publisher: UK company.
- Position 4: ~9,200 lifetime ratings, est. 14,000 monthly downloads, est. $38,000 monthly revenue. Publisher: individual developer, Germany.
- Position 5: ~4,100 lifetime ratings, est. 8,500 monthly downloads, est. $22,000 monthly revenue. Publisher: individual developer, US. Rating: 3.6 stars.
This tells you immediately that "meditation" as a standalone head term is dominated at positions one and two. Ranking there against established players requires download velocity and conversion rates that are very hard to generate as a new app.
But the data also shows something useful: the spread between the top apps and the mid-tier results is significant. Position 4 has 14,000 estimated monthly downloads against position 1's 175,000 — a 12x gap. That gap is exactly where a new entrant can gain traction, not by displacing the category leaders, but by being better than the vulnerable middle of the market.
The revenue estimates tell a parallel story. Meditation is a category where users do pay — positions one and two show strong monthly revenue, which confirms that subscription and premium models work here. Position 5, with a 3.6 star rating, is a slot you can compete for directly. Users are already telling you they want something better.
Publisher country data shows a mix of US, UK, and European publishers among the leaders. Position 4 is held by an individual developer in Germany — the kind of passive holder who may not be actively defending their ranking. These are the positions to examine most closely when planning where to enter.
The top markets data reveals that several top competitors draw heavily from English-speaking markets: US, UK, Australia, Canada. If your app is English-first and incumbents are pulling substantial traffic from non-English markets, the English-language audience may be underserved by their current metadata.
What to Do With the Data
Running the query gives you the raw numbers. The analysis is what turns those numbers into a decision.
Primary analysis: volume and position
Check whether the keyword has real volume. If the top-ranked app for a keyword has 400 monthly download estimates, the keyword is low-traffic regardless of what the competition looks like. For meditation in the US, the download estimates for the leaders confirm this is a high-volume category. The question then shifts to: where is the opening?
Find the weakest position worth displacing. Look at apps ranked three through seven. Are any running on old ratings and declining download velocity? A top-five app with 60,000 lifetime ratings but only 8,000 estimated downloads this month is showing signs of decline. An app with a 3.6 star rating in position five is a slot you can compete for.
Identify the long-tail variations. The data for "meditation" as a head term reveals who the players are. But the same App Store Operator query framework works for every variation: "meditation for sleep," "guided meditation for anxiety," "daily meditation timer," "breathing meditation app." Each is a separate competitive landscape — some will show dramatically lower competition than the head term, with enough volume to drive meaningful installs.
Secondary analysis: quality filters
Use publisher country as a signal. If a position in the top five is held by an individual developer in a smaller market, they may not be responding actively to ranking changes. That kind of passive holder is easier to displace than a dedicated ASO operation.
Compare revenue per download across keywords. Not all download volume converts to revenue equally. If you are building a subscription app, look for keywords where the top apps show strong revenue relative to their download count — that ratio tells you the category has a paying user base, not just free-tier browsers.
Running Multiple Keyword Queries
The meditation keyword is a single example, but App Store competitor research rarely stops at one term. The realistic workflow is to identify your ten to fifteen target keywords and run a query for each.
With App Store Operator inside Claude, this is conversational. After your first query, you can follow up:
Now research "meditation for sleep" in the US App Store.
What does the competition look like for "breathing exercises" in the US store?
Compare the top competitors for "mindfulness" vs "meditation timer" in the US App Store.
Each query takes under 60 seconds and returns the same structured data: downloads, revenue, ratings, publisher, top markets. You are building a competitive map of the entire keyword space in the time it used to take to set up a SensorTower export.
The comparison query — asking Claude to contrast two keywords — is particularly useful for the final shortlisting stage. If "mindfulness" shows higher average downloads but much stronger incumbent competitors than "meditation timer," you have a clear signal about where a new app can enter most efficiently.
How App Store Competitor Research Fits Into Your ASO Workflow
Competitor research is not a one-time exercise. The App Store is dynamic: new apps enter, existing apps update their metadata, category leaders lose momentum. The developers who maintain strong organic visibility treat competitive analysis as a recurring check, not a pre-launch task they complete once.
The practical cadence for most indie developers is to run a full competitive sweep before launch — covering every keyword on your target list — and then revisit the keywords where your own app is ranking but not breaking into the top five. Those are the positions where the data tells you whether to hold the keyword, adjust your metadata, or cut it in favour of a more attainable alternative.
Because App Store Operator caches results for 24 hours, you can run repeated queries on the same keyword cluster without worrying about rate limits or costs. The tool is designed for iterative research, not one-off lookups.
Frequently Asked Questions
How do I find my main competitors on the App Store?
Search for your target keywords in the App Store and note the top-ranking apps. Also check the "You might also like" and "Customers Also Bought" sections on any well-ranked app in your category. Apps that appear repeatedly across multiple keyword searches are your primary competitors — they are winning on the same terms you want to target.
Is SensorTower free?
SensorTower offers limited free access, but full competitive intelligence — including monthly download estimates and revenue data — requires a paid subscription starting at several hundred dollars per month. App Store Operator provides comparable estimates for free by pulling SensorTower data into Claude without a subscription. Run npx -y app-store-operator@latest to get started.
Can I track competitor rankings over time?
Yes. Run the same keyword queries on a recurring cadence — monthly works for most indie developers. Note changes in competitor positions, rating counts, and estimated download velocity. App Store Operator caches results for 24 hours, so repeated queries on the same keyword cluster are fast. Over time, you build a picture of which competitors are gaining, holding, or declining in your target keywords.
What does MCP mean?
MCP stands for Model Context Protocol — a standard for giving AI assistants like Claude access to external tools and data sources. An MCP server is a local process that runs on your machine and connects to Claude as an extension. When you install App Store Operator, you are installing an MCP server that gives Claude the ability to search the App Store and fetch SensorTower estimates on demand.
Going Further: From Competitor Research to Keyword Strategy
Competitor data answers the "who am I up against?" question. But it feeds directly into a broader keyword strategy question: which terms should I actually target, and in what priority order?
That question — covering seed keyword generation, head vs. mid-tail vs. long-tail trade-offs, and how to build out your App Store metadata — is covered in depth in the App Store keyword research guide. Competitor research and keyword research work together: the download estimates from App Store Operator are the validation step that turns a list of keyword ideas into a prioritised shortlist backed by real market data.
The combination of both — knowing what terms exist and knowing what the competition looks like for each — is what separates a keyword strategy with a genuine plan from one built on guesswork.
Run npx -y app-store-operator@latest to start pulling competitor data into your own research workflow.
Run your first competitive research in 60 seconds.
App Store Operator connects Claude to App Store and SensorTower data — no browser, no API keys, no manual copy-paste.
View setup guide →