ASO for iOS applications - a practical case

In this article, we will analyze in detail and step by step how you can do text optimization ASO for an iOS application - what tools you need to use and what will happen as a result.

First, let's define the terms (for details, we recommend that you look at our ASO Dictionary:

The App Store is the official app store for iPhone, iPad, and Apple Watch.

ASO (App Store Optimization) is the process of improving the visibility of an app in stores. Includes work with the text and visual parts of the application. ASO optimization helps to attract more visitors to the application page and turn them into users.

ASO Tools are tools to help you find relevant keywords, prioritize search queries and compose powerful metadata, and then analyze keyword rankings.

ASOMobile is a mobile app analytics platform.

Text ASO is the process of optimizing app metadata to improve its visibility in search.

Indexing is the visibility of an application for keywords on Google Play or the App Store. When an app is indexed by a keyword, it means that the user will be able to find it by entering this keyword in the search.

App visibility is how much a market or store displays you in search results. The number of indexed requests is taken into account, as well as how high the application is displayed in search results.

Visual ASO is the process of optimizing the visual elements of an application page: icon, screenshots, videos.

Now, when we speak the same language with you, we can start our optimization.

At the end of the article, you can find a 40% discount coupon for the first month of using ASOMobile - a tool for creating ASO and researching competitors in the mobile app market.

Textual optimization of mobile app

Optimization of the application, especially its text part, is a rather laborious process that includes many steps:

1. Collecting the semantic core.
2. Keyword prioritization
3. Compiling metadata.

The data required for all these stages (especially for the first) is quite extensive, has a different origin and is difficult to process with a manual approach. That is why all ASO application optimization specialists use auxiliary, and we would even call them the main tools for analysis - ASO tools and mobile app analytics.

We begin our work with market analysis, since it is in this environment that the demand for a mobile application of one kind or another is formed - for us this will be the main source of keywords for the future project. We will be guided by logic, common sense and the example of competitors.

An example of text optimization of our case will be a classic application for maintaining mental health and anti-stress techniques.

Competitors can be considered in two main directions:

• at the time of development and creation of the MVP of the product - you make a cross-section of the leading players in the niche, make a collective portrait of the product, make a list of the main functions and features of applications of this type.
• at the time of the start of ASO optimization, when you get a ready-made product (apk) for the purpose of subsequent optimization in stores.

But both involve a review and analysis of the existing application market in the context of a niche. We have devoted a separate article to this issue. We recommend reading it here.

But how easy it is to understand who is the leader of the niche and who to focus on?

Method 1 - search results in the app store.

An example of a desktop search for Meditation:

The search results in the mobile version look this way:

Further, in a few iterations, you will be able to understand which Top applications are based on the functionality you are interested in and relevant keywords.

Method 2 - use mobile app analytics. We will consider this possibility at the time of collecting the semantic core, since competitors will be one of the most important sources of semantics.

Collection of the semantic core.

The semantic core is an array of keywords and phrases that best describe the application and all relevant associations associated with it.

So at this stage we will collect a pool of requests that are related to our application. Since the application for our case is new, text optimization starts from scratch. Tip, if the application is under development, choose the most similar competitor application as the basis for optimization.

So the input data is applications for iOS, for the US market.

For the basis of optimization, we take one of the most popular and similar applications on the market - Calm

All ASO text optimization will be done using ASOMobile tools.

Step-by-step guide for building a semantic core based on ASOMobile

1. Add an application to ASOMobile analytics and select a geo where the semantic core will be collected.

At the moment of adding the app, the analytics will suggest relevant keywords - this means that at this moment the formation of semantics occurs automatically. If (usually this applies to ASO specialists) you have already optimized a similar application and it is in the system, you can import the existing semantic core into the current project.

In our case, we will use the system auto suggestions and start forming a query pool for the future semantic core.

Keyword Monitor - a tool for working with the semantic core

The next step is the Keyword Monitor tool - our base for working with the semantic core of the app, where we will add keywords from other tools.

The main purpose of Keyword Monitor is to select and track search queries and it is the main tool of the ASO specialist. Here we not only form a list of relevant keywords but also subsequently monitor indexing and analyze the trends of our application after the release in the store.

App Keywords

The next and main source of keywords for the semantic core is App Keywords. It will show us:

• search queries for which the app is indexed
• suggestions and queries related to the topic
• competitor indexing analysis
• additional keywords

*The system selects similar queries based on the keywords that are added to the Keyword Monitor. That is why we do not recommend excluding auto-suggestions at the time of adding the application.

What you should focus on when working with this tool:

First, pay attention to the tab where we start from. Indexing.

You can immediately see how many search queries this application has already been indexed (and put it in your target figure to evaluate the effectiveness of your optimization). Our competitor application, which is also the text ASO database, is currently indexed by 776 queries.

Next, we will be interested in Traffic indicators, what Position the application is in for certain keywords, as well as the popularity of key queries using the Search Ads indicator. We add to the semantic core ✅ all relevant queries for our application, taking into account these indicators. Pay your attention to how search queries are formed - after all, this is what makes us understand how users search and what search results they receive as a result.

If any search query brings us into doubt (relevant or not, perhaps this is some feature of competitors that we do not have, but we would like to add, etc.). We make sure to check this key for compliance:

The search results for the keyword “sleeper” represent the following Top Apps - note that you can always see the current position of the selected app in the search results for the keyword):

For the search term “breathe”, the Calm app is in 8th place in the search results.

This is how we check the search results (if necessary, go to the store to clarify the relevance), we select the appropriate search queries and add them to the current semantic core, which is formed in Keyword Monitor.

The Competitors tab will allow us to add search terms to our semantics by checking the indexing of our app competitors. Adding competitors to analytics occurs in two ways - manual and automatic.

When adding an application to the ASOMobile platform, we not only used the suggested keywords, but also automatically added competitors - suggested competitors.

The rest of the pool of competitive applications was formed in the course of checking the relevance of the keyword and viewing the search results for the keys we were interested in.

In the competitors tab, we will focus primarily on the Competitors Indexing indicator, which, when sorted, will give us a color scale indicator to understand how many competitors are indexed for this query in the TOP-50. And of course, we do not miss the traffic indicator. We add the search queries we are interested in to the current semantic core (Keyword Monitor).

In the Suggestions tab we get a list of keywords, which are store suggestions for search queries that are added to Keyword Monitor. That is why, for a wider pool of requests, it is necessary to already form an approximate semantics. Thus, the system will be able to help you with search suggestions.

Here we will most likely use the Hide added search queries option, since the semantic core is already quite large and this will help us not to miss keywords that have not yet been added.

And the final step of working with App Keywords will be the Similar tab - this is a list of similar search queries, which the system generates based on the keywords added to Keyword Monitor.

We took advantage of all available tabs - Similar, Tips and Competitors, thus filling our semantic core with relevant key queries.

Keyword Finder

Going further, with the help of the Keyword Finder tool, we can find many relevant search queries for any application and quickly complete the semantic core. We are interested in the All keywords tab, which contains the most popular queries and relevant keywords for our application.

Keyword Finder analyzes the metadata of the current (added) application, competitor keywords, selects suggestions and similar search queries to give you a database of relevant keywords. The selection method is completely different from App Keywords, so you can find even more keywords here. With the help of this tool, we have significantly expanded our semantic core.

Keyword Suggest

It is time to go to Keyword Suggest through the store search suggestions, because this is one of the most reliable sources - that is what users see when they use the search in the app store. We are interested in the most relevant search queries for our application - such as sleep, sounds, white noise, relax, etc.

Search suggestions for the given word will be placed in alphabetical order, including numbers at the end.

Keyword Select

Using Keyword Select, a tool for matching similar search queries, we can get even more keywords. We will use the same keywords for selection as we use to search for store suggestions.

 

Suggest Checker

This is a very handy tool and it is better to use it in the last stages of collecting the semantic core. Why? Because Suggest Checker allows you to download a list of keywords (in our case, the already formed core from Keyword Monitor) and check their location and position in the tips.

For what purpose, after all, we have already used search hints as a source of semantics? Here the situation is exactly the opposite - Suggest Checker will check our pool of key queries for being in the tips of the store (and even show which letter in the account, when entering a search query, the user will see the key we are interested in). Since users, for the most part, will be guided by pop-up search suggestions rather than entering their query manually, it is difficult to find better confirmation of the relevance of a search query.

We immediately see that some queries are not in the store suggestions - this indicates that such a formulation of the search query by the user is unlikely.

Organic Downloads - new!

With the help of the Organic Downloads tool, you can find out which keywords your app or your competitors' apps are getting installations from (select the most successful apps in your niche). This will help you form a strategy for promoting your application.

Thus, we check all our search queries from the pool of collected semantics and proceed to the final part of working with the semantic core of the application, returning to Keyword Monitor

Analysis of the app semantic core 

So, let's proceed to the direct analysis and formation of the app semantic core.

Keyword Monitor is a tool where subsequent work with the semantic core of the application will take place.

• check hints for key queries;

• check for keyword traffic;

• check the search results for the relevance of the search query.

We delete irrelevant requests and requests without traffic. In our case, the core got requests that relate to the current application, but it is important that they correspond to the future app. We have cleaned up the core of requests regarding sleep tracking (it is not part of the functionality). You can argue that the topic is the same and the user may be interested in our application when shown in the search results, the main thing here is to follow the relevance rule.

A practical example - a user is looking for sleep tracking, when entering a search query, they get to our application (since we included this query in the metadata) and our successful visual optimization led to a conversion, i.e. to installation. As a result, the user does not find what they are looking for and deletes the applications, and in a bad scenario, they also give a bad rating and write a negative review.

By analogy with the tracker, we delete all keywords that are not related to the functionality of our application. These are requests related to “timer, waking up, sound machine” and others. inappropriate for our application. We remove and form the final version of the semantic core for further analysis.

The final version of the semantics is 88 key relevant search queries, taking into account branded ones. We will dwell on them in more detail - although in the practice of ASO for iOS applications there are cases of their use when generating metadata. What is a Brand Inquiry? It is logical that this is someone's name or brand. In our semantic core, we observe the following brand keys - calm, headspace, meditopia and others. How to check if the key is branded or not - the easiest way, look at the search results.

To use or not, that is the question.

Brand requests in the visible part of the metadata will definitely cause any penalties from the side of the store, at least you will simply not be able to release your application. As for the hidden part of the metadata, namely the keywords field, the decision is entirely up to you. The experience of many ASO specialists will help you, but we will do text optimization according to the canons and therefore we will not use branded keys.

Features of semantic core analysis for App Store applications

For iOS applications, the most important indicators of the semantic core are:

Traffic - the number of users who enter this search query into the market search bar (per day).

Position - the position of the application for each request with the dynamics of change over the last day and the date of updating the information by key.

Applications - the number of apps for this search query.

Search Ads - the popularity of a search query in Apple Search Ads. Values range from 5 (unpopular) to 100 (very popular).

Let's group the semantic core according to the frequency of requests (at this stage, all search queries are relevant for us):

• green zone - high-frequency keywords;
• orange zone - mid-frequency keywords;
• yellow zone - low-frequency keywords.

This division is rather conditional and each app and semantic core will have its own traffic indicators (or rather, its maximum and values close to it), you divide it not into three equal parts by quantity, but by the traffic indicator.

Why do we need that?

• to fill metadata in
• for subsequent indexing tracking
• for analytics and attracting non-organic traffic (for example, for Apple Search ads).

Thus, we have completed a rather responsible, and one can not deny, the most time-consuming stage of text optimization - the formation of a semantic core.

Forming app metadata

We are moving to the ASO Creator - a tool for generating metadata.

With ASO Creator, you can quickly create metadata using as many keywords as possible.

But before analytics helps us with metadata, let's recall what we are talking about if text optimization occurs for iOS applications.

For convenience, here is a short text ASO checklist for the App Store:

✅ Title - 30 characters:

 - create a simple and concise name (combine brand name and keywords)

 - use relevant keywords with a high traffic rate;

 - localize the name for each country;

✅ Subtitle - 30 characters:

 - is used in addition to the title and is its logical continuation;

 - use relevant keywords;

 - do not duplicate words with title and keywords;

✅ Keywords field - 100 characters:

 - all keywords are written separately, separated by commas, without spaces;

 - do not use phrases;

 - check the correct combination of keywords in key queries;

 - remember about additional locales (almost all countries have them);

 - check the combination title+subtitle+keywords;

In Title and Subtitle, we try to use the most relevant and high-frequency search queries, in our case, these are keywords from the green group.

ASO Creator will check that the metadata fields are filled in correctly:

• the number of symbols used and restrictions on their number;
• used keywords from the collected semantic core;
• duplicate keywords in metadata fields;

The tool allows us to quickly make optimization, avoiding the main ASO optimization mistakes for the App Store, so actually the ASO Creator will check the following:

• Do not duplicate words in metadata fields. If we accidentally repeat a keyword (or part of it), the system will definitely highlight duplicate words for us.
• It will independently generate keywords, taking into account their repetitions in the core itself and the traffic indicator.
• Use additional locales (up to 3 inclusive).

But! We can independently edit the keywords depending on the needs.

To evaluate the metadata coverage of the semantic core, pay attention to the scale at the top and the highlighting of key phrases in the semantic core.

Life hacks when filling in metadata for iOS:

• Do not use “free keywords” - see the list below.
• For English, there is no need to make both singular and plural at the same time. The store will index both options. That is why we will not write both sound and sounds, choosing one or the other. But in Russian or any other language, unfortunately, this does not work. Any cases, plurals, declensions App Store perceives as completely different words. Therefore, if we need to index the word in different forms and add them in all necessary forms to the metadata.
• For the USA, Mexico is an additional locale. That is why we will cover our semantic core even more thanks to the expansion of the field of keywords.

After evaluating the automatic completion of keywords, we made some adjustments (removed branded keywords, leaving room for other relevant keywords).

Fill in the second (additional) locale

Kindly note that we left the title and subtitle unchanged (which is not necessary, you can try different combinations), the system immediately notified us that these keywords had already been used.

After reviewing the semantic core to assess the correctness of filling in the metadata, we see that most of the search queries are mentioned, and those that are not - usually contain "free" keys. Let's just talk about them.

Remember, about the whole pool of words that are indexed automatically on the App Store, so we do not waste characters on them.

a, about, above, after, again, against, all, am, an, and, any, app, are, aren't, as, at, be, because, been, before, being, below, between, both, but, by, can't, cannot, could, couldn't, did, didn't, do, does, doesn't, doing, don't, down, during, each, few, for, from, further, had, hadn't, has, hasn't, have, haven't, having, he, he'd, he'll, he's, her, here, here's, hers, herself, him, himself, his, how, how's, i, i'd, i'll, i'm, i've, if, in, into, is, isn't, it, it's, its, itself, let's, me, more, most, mustn't, my, myself, no, nor, not, of, off, on, once, only, or, other, ought, our, ours, ourselves, out, over, own, same, shan't, she, she'd, she'll, she's, should, shouldn't, so, some, such, than, that, that's, the, their, theirs, them, themselves, then, there, there's, these, they, they'd, they'll, they're, they've, this, those, through, to, too, under, until, up, very, was, wasn't, we, we'd, we'll, we're, we've, were, weren't, what, what's, when, when's, where, where's, which, while, who, who's, whom, why, why's, with, won't, would, wouldn't, you, you'd, you'll, you're, you've, your, yours, yourself, yourselves.

You can learn more about additional locales and languages in our ASOMobile analytics, App Store localizations and how to apply them in our article APP STORE LOCALIZATION, OR HOW TO MAXIMIZE APP INDEXATION FOR A LARGE POOL OF KEY REQUESTS ON THE APP STORE.

The result of text optimization for the App Store

As a result of text ASO optimization: we have a ready-made semantic core of the app (Keyword Monitor), app metadata, Title, Subtitle and Keywords for 2 locales (ASO Creator).

To understand the next steps, we suggest reading the following articles:

1. VISUAL ASO APP OPTIMIZATION AND ITS IMPACT ON CONVERSION
2. HOW TO CREATE AN IOS APP ICON?
3. 13 TIPS ON HOW TO CREATE AND OPTIMIZE APP STORE SCREENSHOTS
4. HOW TO PUBLISH AN IOS APP ON THE APP STORE?
5. HOW TO MEASURE THE SUCCESS OF ASO OPTIMIZATION - KEY POINTS AND MET RICS

After uploading the data to App Store Connect, we will track the indexing of the application according to the semantic core used.

Do not forget that optimization is not a one-time action, but a process. Therefore, to achieve your goals, you can return to semantics more than once or twice, revise priority keywords and update metadata. Keep in mind that updating the metadata of an already released app is only possible at the same time as updating the app.

To track the “health” and trends of your application, we highly recommend returning to analytics, where you can quite clearly get comprehensive information on:

• the number of indexed keywords.
• visibility in search of your app in dynamics
• changes in indexing, for which particular search queries positions have changed and in which direction
• what position in the category your application occupies
• downloading your application
• rating and reviews.

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