App Store Keyword Research: Search Intent, Relevance, and How to Find the Right Words

Most guides on App Store keyword research come down to the same thing: find high-volume keywords, add them to your metadata, wait for installs. The logic is straightforward. The results usually aren't.

Here's a concrete example. A habit tracker app adds "habit tracker" to its name — daily search traffic of 1,831 users, sounds reasonable. The top results for that query are Habit Tracker, HabitKit, and Onrise, and the chances of a new app breaking into the top 10 are close to zero. Meanwhile, "routine planner" — with traffic of 436 — sits wide open: far less competitive, and a much closer match to what the app actually does. That's the difference between chasing big numbers and finding the right words.

Traffic matters. But it's one variable out of five, and if it's the only thing you look at, you end up with a list of keywords you can't rank for — and when users do find the app, they often don't install it, because they were searching for something slightly different.

This guide covers the full process: understanding what users actually want when they search in the App Store, building and filtering a keyword list, setting priorities, and deciding where each keyword goes in the metadata. No theory for its own sake.

How App Store Keyword Research Differs from Web SEO

This is worth understanding before anything else, because many teams carry over web SEO logic to mobile and run into unexpected results.

In web SEO, rankings depend on backlinks, behavioral signals, domain authority, and a dozen other factors. The App Store works differently: the algorithm indexes specific metadata fields — the app name, subtitle, and keyword field (iOS only). External links have no effect on rankings, and the iOS description isn't indexed at all — it exists only for users who have already opened the app page.

The practical implication: every character in the name and subtitle has to work for two audiences simultaneously — the App Store algorithm and the person reading the page. The keyword field, unlike the other two, exists purely for the algorithm and is never shown to users.

Competition in the App Store also works differently. It's driven not by site authority but by the install count and ratings of apps already sitting at the top. For popular queries like "photo editor," the top results are held by apps with tens of millions of installs, and no amount of metadata optimization alone will break through that — it takes either finding less competitive queries or growing the install base at the same time.

Keyword Metrics That Actually Matter

Before opening any tool, it's worth understanding what to look at and why.

A note on the Traffic metric: there's no publicly available data on organic search volume in the App Store. ASOMobile calculates it using its own methodology — it's an estimated number of daily searches that serves as a benchmark for comparing keywords, not an absolute figure.

Volume without relevance is traffic that doesn't convert. A good example from the habit tracker category: the keyword "habits" has high traffic, but when someone types it into the App Store, they could be looking for anything — the book Atomic Habits, self-development advice, or fitness apps. A habit tracker ends up somewhere in the third position in terms of relevance. The query "habit tracker daily" has lower volume, but whoever types it is almost certainly looking for exactly that kind of tool.

Relevance without volume means targeting queries nobody makes. That doesn't work either.

Keyword Difficulty is evaluated by the strength of apps already in the top search results: their install counts, ratings, and metadata quality. If the top results are held by apps with hundreds of thousands of reviews, breaking in organically is nearly impossible, regardless of how often the keyword appears in the metadata.

Conversion potential and search intent are closely related — we'll go deeper on both in the next section.

Search Intent in the App Store: Six Types

Intent is what a user actually wants when they type a query. Two keywords with identical search volume can yield very different results if the intent behind them doesn't align with what the app offers.

In the App Store, intent breaks down into six types:

Informational — the user doesn't know what they want yet and is browsing for ideas, not a specific product. Common queries: "apps for productivity," "self-improvement app." Conversion is low because they're just looking around. These queries are useful for a broad reach, but building an entire strategy around them is a mistake.

Problem-aware — the user knows the problem but not the solution. Examples: "how to build a daily habits app," "help build a daily routine." These are strong queries for apps that solve a specific pain point: the user is looking for a solution, not just browsing a topic.

Feature-led — the user wants a specific feature. Examples: "habit tracker with streaks," "habit tracker with reminders." High conversion potential because the query is precise — if the app has that feature and it's visible on the page, the install is likely.

Category-led — the user is looking for a type of app. Examples: "habit tracker app," "daily planner." Volume is typically high, and so is competition. Works well as the foundation of a keyword set, but not as the only strategy.

Brand-led — the user is searching for a specific app or company. Examples: "Streaks app," "Habitica." The warmest traffic, but only when it's our own brand. Using competitor brand names in metadata is a gray area — Apple can reject metadata for it.

Competitor-led — the user is looking for an alternative. Examples: "apps like Habitica," "Streaks alternative free." Highly targeted traffic: the user already knows the category and is actively comparing. If our app has a clear advantage, this is one of the best intent types for conversion.

The practical takeaway: before adding any keyword to the list, ask—what is the person typing this actually looking for? And will they be satisfied with finding our app?

Wrong choice vs right choice: habit tracker

The app helps users build daily habits through a daily checklist, streaks, and reminders. Keywords are chosen based on traffic volume alone.

Wrong choice: habits (high volume), productivity, self-improvement

What's wrong: all three queries are informational and too broad. Someone who types "habits" might be looking for the book Atomic Habits, a collection of tips, or anything else. "Productivity" covers task managers, timers, and team tools — a habit tracker ends up buried. All three keywords will generate impressions and almost zero installs because the intent doesn't match.

Right choice: habit tracker, habit tracker daily, habit tracker with streaks, morning routine app

Why it works: each of these is a feature-led or category-led query — the user is looking for a specific tool. "habit tracker" has high volume; the others are moderate, but competition is significantly lower, and the intent matches exactly what the app does.

All the key metrics for any query — traffic, difficulty, current ranking position — are visible directly in Keyword Monitor.

Keyword Monitor in ASOMobile — queries "habits," "habit tracker," and "habit tracker daily" in one table. "habits" has higher volume but incomparably higher Difficulty and a mismatched intent.

That's why intent analysis comes before looking at traffic volume, not after.

How to Build a Seed Keyword List

Keyword research starts not with a tool, but with a question: how do our users describe what our app does?

Several sources help answer it.

The app itself

The first step is to write out every word and phrase that describes the app — not marketing language, but functional descriptions: what it does, who it's for, and when it's used. A habit tracker is a habit tracker, a goals journal, a daily planner, a reminder app, a streak tracker. Each of these descriptions is a potential keyword or part of one.

User reviews

One of the best sources of natural language. How do users actually describe the app and what they do with it? Reviews often contain phrasing the marketing team would never come up with — and those are exactly the words people type into search.

App Store autocomplete suggestion.s

When a user starts typing in the App Store search bar, the store suggests completions. These aren't random — they're real queries people make most often, and scrolling through all the suggestions for a seed keyword produces a ready-made list of high-demand variations.

For a systematic approach to working with autocomplete suggestions, see our guide on using App Store autocomplete as a keyword source.

Category and adjacent categories

Look at which category the app sits in and how the top apps in that category describe themselves. Their names and subtitles are a concentrated source of relevant keywords — those developers have already done their own research.

Tools for expanding the list

Once the base list is ready, it needs to grow. Keyword Suggest in ASOMobile builds expansions from real App Store autocomplete data: take a seed keyword and see all its variations with traffic figures for each.

Keyword Finder goes further: it analyzes competitor keywords and surfaces queries they rank for — including ones we'd never find on our own.

Wrong choice vs right choice: the starting list for a habit tracker

The most common mistake at the collection stage is stopping at the obvious words.

Wrong starting list: habits, goals, productivity, health, self-improvement, daily, reminder

What's wrong: most of these keywords are too broad. "Productivity" covers task managers, note-taking apps, and timers; "health" spans fitness, nutrition, medicine, and a dozen other directions. Someone looking for a habit tracker is unlikely to type just "goals" — that word describes too many different things. The list looks complete, but in practice, it only covers informational demand, where conversion rates are close to zero.

Right list after expansion through autocomplete and Keyword Suggest:

• habit tracker — core category query, high traffic volume
• habit tracker daily — functional refinement, moderate competition
• habit tracker with streaks — specific feature, high conversion
• daily routine planner — adjacent query, different angle
• goal tracker app — category-led, moderate competition
• routine builder — niche, low competition
• morning routine app — long-tail, precise intent
• drink water reminder — narrow, but very high conversion potential

Keyword Suggest in ASOMobile — results for the query "habit " It is the list of expansions with traffic data for each, option to add a keyword to a list in one click. We can see how one seed keyword produces 15–20 real queries with measurable data.

After expansion, the list grows from 7 broad words to 40–60 specific queries with measurable characteristics. Next step: filtering and prioritization.

Competitor Keywords and Keyword Gaps

Our own keyword list is only half the work. The other half is understanding which queries competitors rank for — and finding the ones they cover that we don't. This is the keyword gap, and competitors almost always have queries that drive traffic for them, are relevant to our app, and are absent from our metadata. These are ready-made opportunities that don't need to be discovered from scratch.

How to analyze competitors

Start with 3–5 direct competitors — apps that solve the same problem for the same audience. Look at their metadata: what's in the name, what's in the subtitle. That's public information and already tells a lot.

Then go deeper with tools. Spy Keywords in ASOMobile shows which queries any app ranks for in search, revealing the full keyword picture — not just what's visible in the metadata.

What to look for first: queries with moderate volume where a competitor ranks in the top 5 and we don't appear at all; feature-led queries that accurately describe our app but are covered by competitors, not by us; competitor-led queries (apps like X) if our app is a genuine alternative.

Wrong choice vs right choice: competitor analysis for a habit tracker

We review competitors in the App Store manually and copy keywords from their names and subtitles.

Wrong approach: grabbed habits, goals, productivity, daily planner — everything visible on the pages of the top competitors.

What's wrong: those are exactly the keywords large apps already rank for in the top 1–3 positions, backed by millions of installs. Adding them to our metadata won't produce rankings — the algorithm will continue to reward apps with far more weight. And in the process, we're spending keyword field characters on queries where we'll never appear in a visible position.

Right approach: run Spy Keywords on 3–4 mid-tier competitors — not the top apps, but those sitting in the top 20 with fewer reviews — and find the queries they rank for where competition is lower.

This kind of analysis typically surfaces queries such as "habit tracker with reminders," "routine planner app," "streak counter," and "daily checklist app." All feature-led, precise intent, and the top results for these queries are apps without millions of reviews — meaning the positions are realistically achievable.

Spy Keywords in ASOMobile — keyword list for a mid-size habit tracker competitor.

Keyword Finder — shows the list to realistically achievable queries.

What not to do

Don't unthinkingly copy a competitor's keywords. Some of those keywords work because of that app's install volume and rating — not the keywords alone. A competitor ranking in the top 3 doesn't mean we'll get there with the same words in our metadata: the algorithm weighs the overall authority of each app.

How to Group Keywords Before Prioritizing

Once the list is collected and competitor gaps are filled, it typically contains 200–500 keywords in no particular order. Before deciding what goes where, it helps to group them — otherwise, prioritization turns into guesswork.

The most useful grouping is by intent and function:

• Core category queries — "habit tracker," "habit tracker app." High volume, high competition. Go in the name.
• Feature-led queries — "habit tracker with streaks," "habit tracker with reminders." Moderate volume, lower competition, high conversion. Got the subtitle and keyword field.
• Adjacent queries — "daily routine planner," "goal tracker app." Capture users browsing related categories. Go to the keyword field.
• Long-tail queries — "morning routine app for anxiety," "drink water reminder app." Low volume, very precise intent. Fill the remaining keyword field space.
• Informational queries — "habits app," "productivity tracker." High volume, low conversion. Hold for later or skip entirely.

Grouping makes the next step — deciding what goes in the name, what goes in the subtitle, and what goes in the keyword field — much faster. Each group naturally maps to a different part of the metadata based on its conversion potential and level of competition.

How to Prioritize Keywords

After the list is built — and it might contain 200–500 keywords — the question is what to work with right now.

A solid prioritization framework works like this. Priority: keywords with moderate volume and high relevance — not the most popular, but the ones where the app has a realistic shot at the top 10, and where quick results are most likely to come from. Second priority: high-competition category-led keywords — worth including, but with the understanding that results take time and depend on growing the app's overall install base. Third priority: long-tail — queries of 3–4 words with low volume but very high precision. "Water tracker for pregnancy" is small in volume, but anyone searching for it wants exactly what the app offers.

Wrong choice vs right choice: prioritizing a list of 80 keywords

After the collection, we have 80 keywords. The classic mistake is grabbing the highest-traffic words and putting them in the name.

Wrong prioritization: name set to HabitFlow — Habits & Goals & Productivity — three broad keywords with maximum competition, and the app doesn't rank above position 50 for any of them.

Right prioritization:

"Fitness habits" looks attractive in terms of volume, but the intent is informational, and the competition is dominated by apps with hundreds of thousands of reviews. Hold until the install base grows. "Habit Tracker with streaks" will realistically land in the top 15 within a month.

Keyword Check in ASOMobile — tool for checking each keyword for future metadata

Geographic check

Before finalizing the list, check how keywords perform in each target market — queries popular in the US may have near-zero traffic in Germany or Brazil, and vice versa. Worldwide Check in ASOMobile shows traffic for any query by country. How to use it is covered in the Worldwide Check overview.

A typical scenario: "habit tracker" performs well in the US, but in Germany, the more common searches are "Gewohnheiten App" or "Routine Tracker," and in France, "suivi habitudes." When localizations are set up, each market needs its own keyword research — words don't carry over automatically.

Worldwide Check — query "habit" with traffic data across 6–8 countries (US, Germany, France, Brazil, Japan, UK). Shows us clearly that the same query carries very different weight across markets.

For more on keyword strategy across both stores and markets, see App Store and Google Play Keyword Strategy.

How to Map Keywords to App Metadata

This is where the research turns into actual decisions.

What gets indexed in the App Store (iOS)

The App Store indexes three fields: the name (up to 30 characters) — the highest-weight signal for the algorithm; the subtitle (up to 30 characters) — second in priority; and the keyword field (up to 100 characters, not visible to users) — iOS only. The description is not factored into rankings; it exists to convert users who have already opened the app page.

How the keyword field works

The keyword field uses comma-separated values with no spaces: tracker,habits,goals,journal. A few rules that get broken often: don't repeat words already in the name or subtitle — the algorithm has already counted them, and repetition wastes characters; no spaces after commas; use individual words, not phrases — the algorithm combines words from different fields into multi-word queries on its own; don't include competitor brand names — Apple rejects metadata for this.

For a detailed breakdown of the iOS keyword field, see iOS Keyword Field: How to Choose Keywords for Your App.

How to distribute keywords across fields

The most important keywords go in the name: either the core category query or a feature-led query that precisely describes the app's main value. Both the algorithm and the user read the name, so it needs to make sense as plain text.

The subtitle holds the second-priority query and adds value for the user — a balance between algorithmic relevance and readability for real people.

The keyword field takes everything else: synonyms, long-tail queries, translations for localized markets, and spelling variants.

Wrong choice vs right choice: metadata for a habit tracker

Bad:

• Name: HabitFlow — Your Daily App
• Subtitle: Track habits and reach goals!
• Keyword field: habits,goals,tracker,daily,productivity,health,fitness,routine,self,improvement

What's wrong: the name includes the vague phrase "daily app," which has no search value. The keyword field duplicates words already in the name (habits, tracker), "self" alone doesn't index as a useful query, and spaces after commas waste characters.

Good:

• Name: HabitFlow: Habit Tracker Daily
• Subtitle: Streaks, Goals & Reminders
• Keyword field: routine,checklist,improvement,challenge,water,journal,list,task,calendar,productivity,health,todo

What changed: "habit tracker" and "daily" are now in the name — both are real search queries. The subtitle surfaces features through words people actually search for. The keyword field is cleaned of duplicates and filled with words that have proven volume, which the algorithm will combine with the name to form multi-word queries.

ASO Creator in ASOMobile - tool for metadata creating

How to Track Keyword Performance

After updating the metadata, the work isn't over. The App Store applies changes within a few days of publishing an update.

What to track: keyword rankings — for each priority keyword, it's important to know where the app appears in search, because breaking into the top 10 means a fundamentally different level of traffic compared to position 50. Ranking trends — the direction matters as much as the current position: is it climbing or falling, and a drop after a metadata update is a signal that the change didn't work. Search traffic — rising rankings should translate into more impressions and page visits. Conversion — if impressions are growing but installs aren't, the problem is either an intent mismatch or something on the app page itself: icon, screenshots, description.

Keyword Report in ASOMobile tracks all of this for every keyword in one place. How to read the report is covered in Tracking Keyword Dynamics with Keyword Report.

What monitoring looks like 4 weeks after an update

After the habit tracker metadata update from the example above, a typical picture:

• habit tracker daily: position moved from 34 to 11 — now in a visible range
• habit tracker with streaks: entered the top 20, wasn't indexed at all before
• routine planner: top 15 from the first week — low competition, fast result
• habit tracker: position 47, unchanged — high competition, needs more install volume

This is exactly why chasing a "habit tracker" from day one isn't the right strategy. While the app builds its weight, moderate-competition queries drive real traffic and installs, which eventually make it possible to compete for the main keywords too.

How often to update metadata. The standard cycle is every 1–2 months. Less frequent means missed opportunities; more frequent means not enough data to evaluate results. Exception: if rankings drop sharply, respond faster.

How ASOMobile Helps with App Store Keyword Research

Everything described above can be done manually — tracking autocomplete suggestions by hand, maintaining spreadsheets, and checking rankings periodically. It takes a lot of time and still doesn't give the full picture.

ASOMobile covers every stage of the process.

Keyword collection: App Keywords shows the current indexation of any app — a competitor or our own — making it a direct source of keyword ideas. Keyword Suggest builds expansions from real App Store and Google Play autocomplete data, showing all variations of a seed keyword with traffic figures for each. Keyword Finder analyzes competitor keywords and finds queries that work for them but are missing from our metadata.

Competitor analysis: Spy Keywords provides a complete view of which queries any app ranks for in search and makes it easy to compare our keyword set against several competitors to identify gaps.

Geographic coverage: Worldwide. Check shows traffic for any query by country — useful when working across multiple markets at once.

Rank monitoring: Keyword Monitor tracks app positions for any selected keywords in real time. Keyword Report builds a history for each keyword — useful for evaluating results after a metadata update.

Performance overview: ASO Dashboard gives a full picture of app health with clear visual summaries.

Metadata decisions: ASO Creator helps build metadata within character limits, checks for duplicates across fields, and shows how keywords are distributed.

Pre-Update Checklist

Before submitting an App Store update, run through this list:

• Every keyword in the priority set has been evaluated for volume, relevance, and competition
• The search intent is clear for each keyword — it matches what the app offers
• The highest-priority queries are in the name or subtitle
• The keyword field has no duplicates of words already in the name or subtitle
• No spaces after commas in the keyword field
• The keyword field is within 100 characters
• The name and subtitle read as natural text, not a list of keywords
• Competitor-led queries have been reviewed — no direct brand name references
• Geographic coverage has been checked: keywords are relevant for each target market
• Rank monitoring is set up to evaluate results after the update

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