Keyword Optimization: What It Is, Keyword Collection Methods, and Query Analysis

What Is Keyword Optimization in ASO?

Keyword Optimization is the process of identifying search queries that potential users use to find apps in a category and including those queries in the app page metadata. The goal is to maximize app visibility in search results for relevant queries.

Unlike SEO, where the semantic core can contain thousands of queries, ASO has a limited number of available keywords. In the App Store, the maximum size of the keywords field is 100 characters; in Google Play, the entire description text (up to 4000 characters) is indexed, but keyword density should not exceed the natural 2–3%.

According to the App Radar (2025) report, apps using a systematic approach to Keyword Optimization occupy on average 40% more positions in the top-10 search results compared to apps that fill metadata without prior research. The difference is especially noticeable in highly competitive categories such as Games and Social.

The Role of Keyword Optimization in ASO Strategy

Keyword Optimization is the foundation of ASO — without properly selected keywords, other ASO elements (icon, screenshots, description) work inefficiently. If the app does not appear for target queries, the page conversion rate does not matter — users simply do not find the product. According to Sensor Tower (2025), 70% of install growth after ASO optimization comes precisely from proper keyword selection.

Working with keywords is an ongoing process, not a one-time action. Search trends change, competitors update metadata, store algorithms improve. A quarterly review of the semantic core with new data is a standard practice for professional ASO specialists.

How App Store and Google Play Search Algorithms Work

Understanding search algorithms is a key condition for effective Keyword Optimization. The App Store and Google Play use fundamentally different approaches to indexing and ranking keywords, and a strategy that works in one store may be useless in another.

In the App Store, four fields are indexed: title, subtitle, the hidden keywords field, and seller name. The app description, icon, screenshots, and video are not involved in search ranking. The keywords field is not visible to users but is fully indexed by the algorithm. Words in the keywords field are separated by commas — spaces are ignored.

In Google Play, the title, short description (up to 80 characters), and full app description are indexed. There is no keywords field analogous to the App Store. Google's algorithm analyzes the entire description text, considering word frequency, position in text, and usage context. According to Google I/O 2024, Play Store search uses semantic query understanding based on the BERT model.

Tokenization and Stemming in Store Search

Tokenization is the process of splitting text into individual words (tokens) for indexing. The App Store automatically combines words from the title, subtitle, and keywords into a single index. This means that word combinations from different fields also participate in search. For example, if the title says "Photo Editor" and keywords contains "filter", the algorithm may show the app for the query "photo filter".

In Google Play, stemming allows finding apps by different word forms. When searching for "photo editing", the algorithm will find an app whose description mentions "edit photos". This reduces the need to include all possible word forms in the description and allows focusing on key terms in a natural context.

Keyword Difficulty: Assessing Competition for a Query

Keyword Difficulty (KD) is a metric that shows how difficult it is to enter the top-10 search results for a specific query. KD considers the number of apps optimized for that query, their ratings, number of reviews, and install growth rate. KD values range from 0 (very easy) to 100 (almost impossible).

For new apps, it is recommended to choose queries with KD below 40 — these can realistically reach the top-10 in 2–4 weeks. Queries with KD 40–60 require more serious effort and behavioral signals (rating, reviews). Queries with KD above 60 are only accessible to apps with a large number of installs and high ratings. Tools like Sensor Tower and App Radar automatically calculate KD for each query by store.

Methods for Collecting a Semantic Core for an App

Collecting a semantic core is the first and most important stage of Keyword Optimization. The quality of the collected queries determines how relevant the traffic the app will receive. A combination of several methods is used for maximum coverage of target queries.

The first method is analyzing autocomplete from app stores. When entering a query in the App Store or Google Play search bar, suggestions appear — these are real user search queries. Collecting autocomplete for a base query (e.g., "photo editor") yields 10–15 additional key phrases. It is recommended to collect autocomplete for 3–5 base queries to form an initial core.

The second method is competitor analysis. Using tools like ASOdesk or MobileAction, you can get a list of keywords that competitors rank for and their positions. This is an effective way to find queries already confirmed by traffic. Analyzing the top-10 apps in the target category is particularly useful.

import requests
import json

# Example of collecting App Store search suggestions
# via iTunes Search API
base_query = "photo editor"
url = (
    "https://itunes.apple.com/search"
    "?term=" + base_query.replace(" ", "+") +
    "&entity=software&limit=25"
)

response = requests.get(url)
data = response.json()

keywords = []
for app in data["results"]:
    name = app.get("trackName", "")
    if name:
        keywords.append(name.lower())

print(json.dumps({
    "base_query": base_query,
    "found_keywords": list(set(keywords))
}, ensure_ascii=False, indent=2))

This script uses the iTunes Search API to get a list of apps by a base query and extract keywords from their titles. The result helps determine which terms competitors most often use in titles — these words are highly likely to be relevant to the target query.

Trending and Seasonal Queries

Trending queries are keywords whose frequency increases during certain periods. For example, the query "fitness tracker" peaks in popularity in January (after New Year), while "photo editor for Christmas" peaks in December. Trend analysis allows timely updating of metadata for seasonal demand and gaining additional traffic during periods of high competition.

For tracking trends, use Google Trends (filter by "App Store" or "Google Play") and App Annie data on query frequency dynamics. It is recommended to add seasonal keywords to metadata 2–3 weeks before the season peak — algorithms need time for re-indexing and position updates.

Negative Keywords

In the context of ASO, negative keywords are queries for which the app should not be shown because they do not match the product's functionality. For example, a free note-taking app should not rank for the query "premium notes app" — users looking for a paid product will be disappointed and leave a negative review.

In the App Store, there is no direct mechanism for excluding keywords. Negative selection is implemented by excluding non-target words from the keywords field. In Google Play, where the description is indexed, it is enough not to mention non-target terms. However, BERT-based semantic search can link topics, so it is important to control the context of mentions.

Grouping and Prioritizing Key Queries

After collecting the initial list of queries (usually 100–300 phrases), it is necessary to group and prioritize them before including them in metadata. The main prioritization criteria are: query frequency, Keyword Difficulty, relevance to app functionality, and alignment with search intent.

Queries are divided into three groups: High Priority — high frequency, low/medium difficulty, high relevance; Medium Priority — medium frequency and difficulty; Long-tail — low frequency, low difficulty, high specificity. For a new app, the strategy is to start with Medium Priority and Long-tail queries, gradually moving to High Priority as behavioral signals accumulate.

According to ASOdesk (2025), the optimal ratio for the App Store is: 30% of keywords field characters for High Priority queries, 40% for Medium Priority, and 30% for Long-tail. For Google Play, where there is more space, the distribution can be 20/40/40 with a focus on Medium and Long-tail for natural keyword distribution in the description text.

• High-frequency queries (HF) — more than 10,000 queries per month. High competition, require strong behavioral signals.
• Medium-frequency queries (MF) — 1,000–10,000 queries per month. Optimal balance of traffic and competition for most apps.
• Low-frequency queries (LF) — fewer than 1,000 queries per month. Low competition, fast entry into top rankings, high conversion.
• Transactional queries — contain words like "buy", "download", "free". High conversion to install.
• Informational queries — "how to", "what is". Low conversion but expand reach.

Clustering Queries by Topics

Clustering is grouping queries into thematic clusters (app features, target audience, usage scenarios). For example, for a photo editor app, clusters could be: "filters and effects", "collages", "retouching", "crop and rotate". Each cluster corresponds to a specific set of features and helps structure metadata.

In the App Store, clustering determines the distribution of the 100-character keywords field across topics. In Google Play, it determines the description structure: each cluster gets a separate paragraph or section with natural keyword inclusion. This approach improves semantic relevance and helps algorithms more accurately determine the app's topic.

Keyword Placement Strategy in Metadata

Keyword placement in metadata should follow a specific strategy that considers the weight of each field in search algorithms. A common mistake is evenly distributing all keywords without considering field priority: the title has maximum weight, the subtitle has medium weight, and the keywords field has basic weight.

The title should include 1–3 of the most important keywords. They should be readable for the user and naturally fit into the brand name. Formula: [Brand] — [Main Keyword] [Additional Word]. For example: "SnapEdit — Photo Editor & Filter". Keep length limits in mind: 30 characters in the App Store, 50 in Google Play.

The subtitle (App Store) or short description (Google Play) should include additional keywords that extend the semantic coverage of the title. This field is also indexed and has high weight. Example: "Retouch, Collage, Crop & Effects". In Google Play, the short description (up to 80 characters) appears in search results under the title and should be conversion-oriented.

The App Store keywords field (100 characters) includes all remaining target queries. Important rules: words are separated by commas without spaces (each word is indexed separately), repeating words from the title and subtitle is not required (they are already in the index), and word combinations can be used to form compound queries.

Avoiding Keyword Cannibalization

Cannibalization is a situation where multiple pages of one app (e.g., the main page and the developer page) compete for the same query. In the context of ASO, cannibalization is possible when a developer has multiple apps in the same category. In this case, they may compete for the same keywords, reducing each other's effectiveness.

To prevent cannibalization, each app must have a unique semantic core with minimal overlap in key queries. If overlap is unavoidable (e.g., free and paid versions of the same app), it is recommended to use different keywords in the titles and explicitly differentiate positioning in the metadata.

Grammatical Forms and Synonyms

Modern app store algorithms — especially Google Play with BERT — understand synonyms and different grammatical forms of words. This means that including all possible forms of a word ("edit", "editing", "editor") in the description is not required. It is enough to use the most natural form in context.

For the App Store, the situation is different — stemming and synonym handling are weaker. It is recommended to include the most frequent form of a word in the title and keywords, as well as key synonyms if space allows. For example, if the app processes photos, the keywords field can include "photo,picture,image" to cover different search variants.

How to Track Keyword Rankings

Rank monitoring is a mandatory element of Keyword Optimization. Without regular position checks, it is impossible to determine which keywords are working and which need replacing. Monitoring data allows you to adjust your strategy and respond timely to changes in the market situation.

For tracking rankings, use specialized tools: Sensor Tower (daily position updates, 155 countries), App Radar (up to 10 countries, store integration), and ASOdesk (focus on the Russian-speaking market, RU/CIS regions). A free alternative is manual checking via App Store Connect Analytics and Google Play Console, but this is labor-intensive for more than 20 queries.

It is recommended to track rankings for 30–50 key queries weekly. Key queries are divided into three categories: Brand queries (brand name), Category queries (general category queries), and Competitor queries (competitor names). A drop in rankings for Category queries is the first signal that the ASO strategy needs updating.

• Daily monitoring — for the top-10 priority queries. Allows quick response to ranking drops.
• Weekly monitoring — for 30–50 queries. The main tracking mode, sufficient for most apps.
• Monthly audit — full review of the semantic core with analysis of competitor changes.
• Trend analysis — comparison of ranking dynamics over a month/quarter to identify sustainable trends.

Interpreting Monitoring Data

Rank growth for a query is not the only success metric. It is important to analyze whether rank growth leads to increased installs. Sometimes an app rises to the top-5 for a query, but the conversion rate for that query is low — users search for one thing but find another. In this case, the query should be replaced with a more relevant one.

The key performance indicator for Keyword Optimization is Organic Install Lift (percentage increase in organic installs). It is calculated as the ratio of installs from organic search after optimization to installs before optimization over a comparable period. The target is at least 50% growth within 2 months. If growth is lower, the strategy needs revision: perhaps the wrong keywords were chosen or the metadata is not sufficiently optimized.