App Store Keyword Research for Indie Apps: A Practical Guide

App store keyword research is the process of finding the phrases people use to look for an app, checking what each phrase means in a specific storefront, and choosing only the terms your product and listing can satisfy. For an indie app, the practical method is to build a broad candidate list, inspect the current results on Apple’s App Store or Google Play, compare relevance and competition, then measure search discovery after publishing rather than trusting an unsupported volume estimate (Apple App Store search and Google Play search guidance, checked 18 Oct 2026).

Disclosure: ZeroRaised publishes this guide. The recent tool discussed below belongs to GetAppNiche; ZeroRaised has no stated affiliation with it and has not independently validated its ranking data.

TL;DR: app store keyword research for indie apps#

This is a research workflow, not a promise of rank. Apple says its results use text relevance plus user behavior such as downloads, ratings, and reviews; Google says its ranking also combines ratings, reviews, downloads, and other factors whose weights are proprietary (Apple App Store search and Google Play search guidance, checked 18 Oct 2026). Metadata can establish relevance, but neither store describes it as the only input.

Recent hook, 13 Oct 2026: a free rank-checking tool reached Show HN#

Dated note: On 13 Oct 2026, Hacker News user skiffer91 submitted “Show HN: See which apps rank for any keyword on the App Store and Google Play.” The linked GetAppNiche page says its free tool returns a current top-ten snapshot for one keyword on either store, with a country selector and no account requirement. Those are the maker’s claims, not independent findings or a ZeroRaised endorsement (Show HN item 50072093 and GetAppNiche keyword tool, checked 18 Oct 2026).

The launch is useful because it exposes a modest but important research object: the actual result set. When its methodology is unavailable, a difficulty score hides how it was constructed. A result page lets a maker inspect whether the leading apps solve the same job, use the phrase in visible metadata, and look established enough to make the target difficult. It still does not reveal the stores’ complete algorithms or prove how many people search the phrase; Apple and Google both describe multiple ranking factors without publishing a complete weighting formula (Apple App Store search and Google Play search guidance, checked 18 Oct 2026). Treat the tool output as a snapshot to verify in the target storefront.

App store keyword research starts with a search brief#

Before collecting phrases, write one line that fixes the scope:

Find terms used by [person] who wants [job or result] on [store], in [language], in [country or region], that [app name] can satisfy in its current release.

This is a ZeroRaised editorial method. It prevents five different research problems from collapsing into one spreadsheet. An iPhone result in the United States is not evidence of an Android result in Germany. A phrase used by someone who wants a simple timer may carry a different intent from a phrase used by someone who wants a project planner. A feature on the roadmap does not make the current app relevant.

Store and location belong in the brief because both platforms expose market-specific surfaces. Apple localizes product-page metadata and says people can search with localized keywords in the countries or regions where that language is supported. Google recommends localizing the store listing and also lets eligible developers create custom listings for country, search-keyword, and other audience segments (Apple localization and Google Play custom store listings, checked 18 Oct 2026).

Use a worksheet with one row per phrase and one storefront. These columns are enough to start:

Do not score anything yet. The first pass is about defining the question and preserving evidence. A neat score attached to the wrong market, store, or intent is still the wrong answer.

Build a candidate list from language you can trace#

Start with language close to the problem. Product interviews, support requests, cancellation notes, app reviews, onboarding answers, and search terms from an existing campaign can all supply candidates when you have permission to use the material. Record the phrase, not personal information. This is editorial research advice, not a claim that every phrase represents broad demand.

Then expand the list through four traceable sources:

1. Your product vocabulary. Write the core job, input, output, audience, constraint, and distinguishing mechanism. A habit app might yield “habit tracker,” “streak tracker,” “routine planner,” or a narrower phrase tied to a capability it really offers. These are candidate patterns, not reported search volumes.
2. Store language. Type a seed phrase into the intended storefront and record the suggestions that actually appear. Apple says search hints appear after someone enters text and can help people select or complete a search, while Google describes the text in a store listing as part of app discovery (Apple App Store search and Google Play search guidance, checked 18 Oct 2026).
3. Relevant result pages. Open the listings that solve the same job and note recurring category words, outcomes, and audience qualifiers. Do not copy another app’s brand or protected terms. Apple explicitly bars competing app names and unauthorized trademarked terms from keywords, and Google bars misleading metadata and unauthorized brand use (Apple App Store search and Google Play metadata policy, checked 18 Oct 2026).
4. First-party advertising evidence, if you already use it. Apple Ads provides keyword recommendations, defines search terms as the words and phrases people used when looking for that type of app, and reports Search Popularity on a 1-to-5 scale based on App Store searches (Apple Ads keyword guidance, reporting definitions, and keyword insights, checked 18 Oct 2026). Treat that as Apple Ads evidence for a particular market, not as an exact organic search count.

Organize the candidates by intent before judging them:

The examples above use the real category and named app shown in the GetAppNiche snapshot and the live HabitKit App Store listing and HabitKit Google Play listing, checked 18 Oct 2026. They illustrate phrase types, not recommended targets for another app.

Keep broad and narrow candidates at this stage. Apple itself describes a trade-off: popular functional or seasonal terms may bring more traffic but be highly competitive, while less common terms may bring less traffic and be less competitive (Apple App Store search, checked 18 Oct 2026). That guidance does not tell you which side wins for your product. The result set and your own performance evidence must do that work.

Read the result set before choosing a keyword#

Search every serious candidate in the intended store and market. Record what appears without turning the first result into a universal rule. Apple says its search order considers title, subtitle, keywords, primary category, downloads, ratings, reviews, and other behavior. Google says ratings, reviews, downloads, and other factors contribute, but keeps the weights proprietary (Apple App Store search and Google Play search guidance, checked 18 Oct 2026).

Read the page in this order:

1. Intent coherence. Do most results solve the same job, or does the phrase mix several categories? A mixed page can mean the wording is ambiguous. It does not automatically mean the query is easy.
2. Product fit. Could your current app satisfy the expectation created by the top results? Reject the term if reaching the result page would set up disappointment.
3. Visible positioning. Which apps use the phrase in the name, subtitle, or visible description? This shows how deliberately competitors present the idea, but it does not prove that the wording caused their rank.
4. Result strength. Note ratings, review volume, recognizable brands, listing quality, and whether smaller relevant apps appear. These are observable attributes, not a substitute for the stores’ undisclosed ranking weights.
5. Result diversity. Note whether one publisher, one product model, or one feature interpretation dominates. A diverse set may offer positioning room, but only if your app fits the searcher’s job.
6. Snapshot context. Save the store, market, language, device context where relevant, and date. Recheck before committing prominent metadata because a rank observation is time-bound.

The recent GetAppNiche page supplies a concrete named example. In its United States snapshot for “habit tracker,” it displayed HabitKit in second position on the App Store and fifth on Google Play. The product’s visible title was “Habit Tracker - HabitKit” on both store listings when checked. That is evidence that the two result sets differed at that moment, not evidence that title wording alone produced either position (GetAppNiche keyword tool, HabitKit on the App Store, and HabitKit on Google Play, checked 18 Oct 2026).

Use a result-review table that separates observation from inference:

The “safe inference” column is intentionally modest. A research sheet should preserve what you know and expose what you do not. If a tool provides a single difficulty number, keep the vendor, date, market, and methodology beside it. If the methodology is unavailable, use the number only as a prompt to inspect the results.

Choose keywords without inventing search volume#

A keyword does not need a fictional monthly-search number to be evaluated. Use four independent questions:

• Does the phrase match the product? Name the screen, behavior, or output that fulfills it.
• Does the phrase express a coherent intent? Describe what a searcher would expect after tapping a result.
• Is there credible demand evidence? Mark whether the evidence comes from store suggestions, Apple Ads Search Popularity, actual search terms from a campaign, first-party store performance, or a third-party estimate.
• Can the listing make a credible choice? Compare the current result set, the promise shown in your metadata, and the proof visible on the product page.

This is the ZeroRaised selection framework. It deliberately avoids adding those questions into one opaque score. A phrase with strong demand evidence but weak product fit should be rejected. A highly relevant phrase with uncertain demand can stay in an investigation queue. A low-competition term that no intended user appears to seek is not a strategic win.

Use evidence labels instead of fake precision:

Apple Ads Search Popularity is ordinal, from 1 to 5, rather than a published exact organic query count (Apple Ads reporting definitions, checked 18 Oct 2026). Do not translate it into a made-up number. Likewise, neither the App Store nor Google Play documentation cited here gives developers the complete organic-ranking formula (Apple App Store search and Google Play search guidance, checked 18 Oct 2026).

Apple App Store and Google Play metadata are different systems#

Do not paste one keyword list into both consoles. Apple provides a dedicated keyword field and states which page elements affect text relevance. Google asks for a useful title and descriptions and prohibits repetitive or unrelated keyword blocks (Apple App Store search and Google Play listing best practices, checked 18 Oct 2026).

Sources for current field limits and behavior: Apple app information, Apple platform-version information, Apple App Store search, Google Play listing best practices, and Google Play metadata policy, checked 18 Oct 2026.

Write the Apple metadata map#

Map the selected terms across the name, subtitle, keyword field, and category before entering anything in App Store Connect. Apple advises choosing specific words people may use, separating keyword terms with commas and no spaces, avoiding repeated words already present in the name, subtitle, or category, and excluding filler, overly broad generic terms, competing app names, and unauthorized protected terms (Apple App Store search, checked 18 Oct 2026).

A compact planning table can look like this:

Count bytes in the actual field, especially for localized text, rather than relying on a visual character count. Apple’s App Store Connect reference specifies a 100-byte limit for the keyword property (Apple platform-version information, checked 18 Oct 2026).

Do not hide a competitor’s name, a celebrity, pricing copy, or an irrelevant popular term in the keyword field. Apple identifies improper keyword use as a common reason for rejection and lists those categories among the prohibited uses (Apple App Store search, checked 18 Oct 2026). The safer decision is also the clearer one: target the user’s job, not someone else’s brand equity.

Apple now also lets an approved custom product page appear for assigned keywords instead of the default page. The keywords must come from the latest approved version, and the custom page becomes searchable only after approval and visibility are in place (Apple custom product page configuration, checked 18 Oct 2026). Use that only when the page’s screenshots, previews, promotional text, and deep link genuinely match the assigned intent.

Write the Google Play metadata map#

For Google Play, write for comprehension first. Google advises a unique, accessible, focused title; a description centered on what users receive; and a comprehensive listing. Its policy requires metadata to reflect the app and prohibits repetitive, irrelevant, excessive, or deceptive text (Google Play search guidance, listing best practices, and metadata policy, checked 18 Oct 2026).

Use the title for the product identity and clearest category fit. Use the short description to state the result and meaningful distinction. Use the full description to explain who the app serves, what the core workflow does, which relevant features support it, and any material access conditions. Place selected phrases only where they make the explanation more precise.

Google’s current limits are 30 characters for the title, 80 for the short description, and 4,000 for the full description (Google Play listing best practices and metadata policy, checked 18 Oct 2026). Repetition is not a research strategy. If the copy becomes awkward when a phrase appears again, the user-facing explanation has already given you a reason to stop.

Do not claim “#1,” “best,” a store award, or a promotional price simply to attract attention. Google’s metadata guidance rejects store-performance, ranking, pricing, promotional, and deceptive claims in the relevant listing elements (Google Play listing best practices and metadata policy, checked 18 Oct 2026).

Localize the research, not just the final words#

Translation is the last step only after the search intent has been researched in the target language and market. A literal translation can be grammatically correct while missing the term people use for the job. That is editorial advice, not a claim about every language pair.

Apple says localized metadata displays according to language and storefront support, localized keywords can make an app searchable where that localization is supported, and a fallback is used when no localization matches the user’s setting (Apple localization, checked 18 Oct 2026). Google says it may provide machine translations when a developer does not supply a listing, while recommending professional translation for better search results and worldwide discoverability (Google Play search guidance, checked 18 Oct 2026).

For each priority market, ask a fluent reviewer to describe the job without seeing the source-language keyword list. Collect native wording from intended users, suggestions, and relevant local listings; inspect the target storefront; reject phrases that point to another job; then review the final metadata beside the localized product and screenshots. This is ZeroRaised editorial methodology.

Google’s custom store listings can target countries or regions and, where the option is available, users who discover the app through specified search keywords (Google Play custom store listings, checked 18 Oct 2026). Apple’s custom product pages can likewise be assigned approved keywords (Apple custom product page configuration, checked 18 Oct 2026). These are intent-matching tools, not permission to make conflicting promises to different audiences.

Measure rank, discovery, listing response, and product use separately#

Keyword research creates a hypothesis about discovery. It does not prove that a person saw the app, wanted the product, installed it, or found value. Track the chain as separate layers:

Apple’s acquisition analytics includes unique impressions, unique product-page views, downloads, conversion rate, and source dimensions, including App Store search as a source type (Apple acquisition analytics, checked 18 Oct 2026). Use those definitions as Apple provides them. Do not turn an App Store search total into a report for one organic keyword unless Apple explicitly provides that breakdown in the view you are using.

Google changed its store-listing performance reporting in July 2026 to focus the main reports on intent signals. The current documentation describes unique clicks on Install, Open, or Pre-register, plus visitors and click-through rate in conversion analysis; completed acquisitions remain available in the Grow overview, Statistics, and downloadable reporting (Google Play store performance, checked 18 Oct 2026). Preserve the metric name and reporting location in your log so a pre-change baseline is not compared as though the definition stayed identical.

Use this change log for every meaningful metadata test:

This table is editorial methodology. Choose the observation window according to the app’s traffic, release cadence, review timing, and decision cost. Do not set a universal duration or declare a winner from a handful of observations.

Store-page experiments answer a related but different question. Apple product page optimization can test icons, screenshots, and app previews, with up to three treatments described in its current documentation. Google Play store listing experiments can test graphics and, for localized experiments, the short and full descriptions (Apple product page optimization and Google Play store listing experiments, checked 18 Oct 2026). A creative or description test does not isolate organic keyword rank unless the experimental design and available reporting actually support that conclusion.

Change one main research hypothesis at a time when practical. When several release elements must change together, record them and treat the keyword conclusion cautiously. This is ZeroRaised editorial advice.

Turn keyword research into a truthful launch#

App store keyword research helps the right store visitor find and understand the listing. It does not replace release testing, screenshots, pricing clarity, support, or distribution outside the store. Use the product launch checklist to prepare the release path and the bootstrapped app launch guide to sequence outreach around a working product.

Use the app store screenshot guide to turn the chosen search intent into an accurate visual sequence for the listing.

When the app is ready, eligible makers can submit a launch to ZeroRaised for free. ZeroRaised is for products built without institutional VC or accelerator equity; check the current submission form and terms rather than treating this article as an eligibility decision. The home board shows the current site experience, and maker perks collects offers that may be useful around launch.

Bring the evidence, not a promise of rank: the intended user, the app’s core job, the store links, a clear description, accurate screenshots, access and pricing conditions, and the release story. Keyword research should make that story easier to find. The product still has to keep it.

Sources#

• Show HN item 50072093, submitted 13 Oct 2026; checked 18 Oct 2026.
• GetAppNiche free keyword tool, checked 18 Oct 2026. Vendor claims are labeled as such in the article.
• Apple App Store search guidance, checked 18 Oct 2026.
• Apple App Store Connect app information, checked 18 Oct 2026.
• Apple App Store Connect platform-version information, checked 18 Oct 2026.
• Apple localization guidance, checked 18 Oct 2026.
• Apple acquisition analytics, checked 18 Oct 2026.
• Apple Ads keyword guidance and reporting definitions, checked 18 Oct 2026.
• Apple custom product page configuration and product page optimization, checked 18 Oct 2026.
• HabitKit on the App Store and HabitKit on Google Play, checked 18 Oct 2026.
• Google Play search guidance, checked 18 Oct 2026.
• Google Play listing best practices, checked 18 Oct 2026.
• Google Play metadata policy, checked 18 Oct 2026.
• Google Play store performance reports, checked 18 Oct 2026.
• Google Play categories and tags, custom store listings, and store listing experiments, checked 18 Oct 2026.