How to Do Keyword Research for App Store Optimization

Seed keywords are the starting point for all keyword research. They are the most obvious terms that describe what your app does. From these seeds, you will expand into dozens of variations, long-tail terms, and competitor-inspired ideas.

Source 1: Your app description.Read your own app description and highlight every noun and verb that describes a feature or use case. If your app is a recipe organizer, your seeds might include: "recipe," "cookbook," "meal plan," "grocery list," "cooking," "food planner." These are the words your potential users would use to describe what they are looking for.

Source 2: User language.Read your reviews, support emails, and social media mentions. Pay attention to the exact words users use when talking about your app. If users keep saying "meal prep" but your listing says "recipe management," you are missing a keyword that matches real user language. The best keywords come from how users think, not how developers think.

Source 3: App store autocomplete.Go to the App Store or Google Play and start typing your seed keywords. Record autocomplete suggestions as query-pattern evidence observed for that storefront, country, prefix, and date. They can expand research, but do not prove query volume, conversion, or that a term is relevant to your app. Type "recipe" and note each relevant suggestion: "recipe app," "recipe organizer," "recipe keeper," "recipe book." Then try "meal": "meal planner," "meal prep," "meal tracker." Validate every candidate with product fit and additional dated storefront evidence.

Source 4: Category browsing.Look at the app store category your app belongs to. Browse the top 50 apps and note recurring words in their titles and subtitles. These are competitor-copy patterns, not proof of what users search for or what caused a ranking. If every top recipe app includes "healthy" in their title, treat it as a messaging hypothesis to investigate against your own product, users, and storefront evidence.

Aim to generate 30 to 50 seed keywords from these sources. You will narrow this list down in the evaluation phase, so cast a wide net now. Include synonyms, abbreviations, and both singular and plural forms.

Not all keywords are worth targeting. The evaluation phase is where you separate high-potential keywords from time-wasters. Every keyword should be assessed on three dimensions: volume, difficulty, and relevance.

Demand estimates are provider-specific proxies for how often users may search for a term. They are useful for relative comparisons when the provider, storefront, country, and observation date are the same, but they are not official monthly search counts. Review them together with observed rank and relevance.

Difficulty (sometimes called competition) is also a third-party estimate. Providers use different data and methods, so a score of 80 in one tool is not directly comparable with 80 in another. Inspect the apps currently visible for the exact storefront and query, and ask what evidence and confidence support the score.

Relevanceis the most important and most overlooked factor. A keyword must accurately describe what your app does. Ranking for an irrelevant keyword might get impressions, but users will not download an app that does not match their search intent. Irrelevant targeting can create a poor product-page experience and weak conversion, regardless of how the store's undisclosed ranking system treats those observations.

Build a shortlist by considering relevance, observed rank, demand estimate, competitive context, freshness, and data confidence side by side. Do not collapse those dimensions into a universal formula: a single score can hide missing data and make incomparable providers or storefronts look equivalent.

Long-tail keywords are longer, more specific search terms. They may express clearer intent and may face different competition than broad terms, but lower volume, easier ranking, and higher conversion are hypotheses to validate for each storefront—not guaranteed properties.

Consider the difference between "fitness app" and "7 minute workout timer." The first term has massive volume but is dominated by apps with millions of downloads. The second has a fraction of the search volume and expresses a more specific need that a focused app may serve well. If your app is a quick workout timer, compare the observed results, demand estimate, and product-page conversion for both terms before deciding where to focus.

How to find long-tail keywords:Take your seed keywords and add modifiers. Common modifiers include "free," "best," "simple," "offline," use cases ("for beginners," "for kids," "for couples"), and specific features ("with timer," "with reminders," "no ads"). Each combination creates a long-tail term. Then validate these using autocomplete suggestions and search volume data from an ASO tool.

Portfolio thinking:A set of relevant, specific terms can diversify discovery beyond one broad query. Compare each term's observed rank, estimated demand, and conversion contribution where attribution is available. Do not assume that one ranking builds a hidden authority score or guarantees easier rankings for other terms later.

If you are new to ASO and want a plain-language explanation of how all of this fits together, our ASO for non-marketers guide covers the fundamentals without jargon.

Your competitors have already done keyword research for you. By analyzing which keywords they rank for, you can discover terms you missed and find gaps where they are weak and you can be strong.

Step 1: Identify your real competitors. Do not just pick the biggest apps in your category. Your real competitors are the apps that rank for the same keywords you are targeting. Search your primary keywords and note which apps consistently appear. These are the apps fighting for the same users, and they are the most relevant to study.

Step 2: Analyze their metadata.Look at their titles, subtitles, and descriptions. Repeated terms are useful observations, but they do not prove demand, field weight, or performance. Likewise, an unmentioned feature such as "grocery delivery integration" is a possible differentiation hypothesis—not a gap you automatically own.

Step 3: Find keyword gaps.A keyword gap is a relevant term where a competitor has an observed rank and you do not. A gap is a research candidate, not proof of demand or an automatic opportunity. Check the provider's demand estimate, storefront, observation date, result-page competition, and fit with your product before testing it.

Step 4: Monitor changes. When a competitor updates metadata, record what changed and when. The edit does not reveal its cause or whether it worked; it may be related to a feature, campaign, policy review, localization, or experiment. Use the change to form a hypothesis and compare subsequent observations rather than copying it. Tools like Lite ASO can automate this monitoring.

Not all keywords serve the same purpose. Organizing your keywords by user intent helps you compare relevance and decide which candidates support clear, useful metadata.

Core feature keywordsdescribe your app's primary function. For a budgeting app, these might be "budget tracker," "expense manager," "money tracker." Consider them for prominent user-facing fields when they are supported by data, policy-compliant, and still leave room to communicate your brand and value proposition.

Use case keywordsdescribe specific scenarios. "Track monthly expenses," "split bills with friends," "save for vacation." These can be candidates for Apple's private keyword field or natural Google Play copy when the data supports them. Validate their actual demand and competition in the target storefront.

Alternative and synonym keywordsare different words for the same thing. "Spending tracker," "finance app," "money manager." These may reveal vocabulary your audience uses. Compare them with observed store data and choose only the relevant terms that fit the platform's fields.

Brand and competitor keywordsinclude your own brand name and competitor brand names. Competitor names and trademarks require particular care: do not insert them merely to capture traffic. Follow each store's metadata and intellectual-property policies and obtain legal review for any comparative claim.

Create a simple spreadsheet with columns for keyword, intent category, estimated volume, difficulty, and priority. This becomes your keyword map and the basis for every metadata update. Refer to it when writing your title, subtitle, keyword field, and description to ensure every high-priority term is covered somewhere in your listing.

Here is a step-by-step workflow you can follow every time you do keyword research, whether it is your first time or your monthly refresh.

Step 1: Generate seed keywords (15 minutes). List every word and phrase that describes your app's features, use cases, and category. Pull from your app description, user reviews, and competitor titles. Aim for 30 to 50 raw terms. Do not filter yet. Just brainstorm.

Step 2: Expand with autocomplete (15 minutes). Enter each seed keyword into the App Store or Google Play search bar and record every autocomplete suggestion. Also try typing just the first two or three letters of each seed to see broader suggestions. This typically doubles your keyword list to 60 to 100 terms.

Step 3: Get volume and difficulty data (10 minutes). Use a keyword research tool to look up estimated search volume and difficulty for each term on your expanded list. Lite ASO's keyword explorer provides both metrics along with AI-powered suggestions for related terms you might have missed. Export the data to a spreadsheet.

Step 4: Score and prioritize (15 minutes). For each candidate, record product relevance, observed rank, provider-specific demand and difficulty estimates, storefront, freshness, and confidence. Compare the dimensions directly and document why a candidate advances. Avoid a universal arithmetic score that hides missing or incomparable data.

Step 5: Map keywords to metadata fields (10 minutes). Draft several policy-compliant variants that express the app's value naturally. Respect each platform's field purpose and limits; do not force every candidate into the listing or assume a fixed field weight. Apple's private keyword field is limited by UTF-8 bytes, while Google's descriptions are user-facing copy. For detailed guidance on the best ASO tools for 2026 to help with this process, we have a full comparison guide.

Step 6: Set up tracking (5 minutes). Add your priority keywords to a rank tracking tool. Set up weekly or daily tracking depending on your tool's capabilities. Create a baseline snapshot of your current rankings before making any metadata changes.

Step 7: Publish and measure. Submit the approved metadata through the relevant console and record when it actually becomes visible in each storefront. Preserve the prior version and baseline, then collect enough observations for your traffic before evaluating rank and conversion changes. There is no official universal two-week evaluation rule.

The time required depends on the number of storefronts, locales, candidates, and evidence gaps. Repeat the workflow when data freshness or product scope changes, spending less time on stable inputs and more on comparable outcome analysis.

You can do basic keyword research manually using just the app store search bar, but dedicated tools make the process faster, more data-driven, and repeatable. Here is what to look for in a keyword research tool and how Lite ASO's features map to the workflow above.

Autocomplete suggestions.A good tool captures app store autocomplete suggestions programmatically, saving you from typing hundreds of queries manually. Lite ASO's autocomplete feature pulls real suggestions from both Apple App Store and Google Play, organized with evidence-labelled relevance and modeled demand estimates. This can reduce repetitive collection work, but completion time still depends on the number of seeds, locales, and provider availability.

Keyword explorer.This is the core of any ASO keyword tool. It provides search volume estimates, difficulty scores, and related keyword suggestions for any term. Lite ASO's keyword explorer also shows which apps currently rank in the top ten for each keyword, so you can assess the competitive landscape at a glance. You can check out the full set of capabilities on the features page.

AI-powered suggestions.Modern ASO tools use artificial intelligence to generate keyword ideas you would not think of on your own. Lite ASO's AI suggestion engine analyzes your app's category, current metadata, and competitor landscape to recommend keywords with high potential. It is especially useful for finding long-tail variations and cross-category terms that manual brainstorming often misses.

Rank tracking.Without tracking, you cannot measure the impact of your keyword research. A rank tracker shows your app's position for each target keyword over time, alerting you to observations worth investigating. Rank tracking alone does not reveal which term caused downloads or conversion; combine it with appropriately attributed acquisition and experiment data.

Competitor keyword monitoring. The best keyword research tools track your competitors automatically. When a rival changes their metadata, adds new keywords, or experiences a significant ranking shift, you get notified. This competitive intelligence turns keyword research from a periodic task into a continuous information advantage.