What Is ASO? The Part Localisation Plays in App Store Optimisation

When an app is published it enters two separate races. The first is whether users like it. The second is whether users ever find it at all. The second race is called app store optimisation, and the industry knows it by the abbreviation ASO. The short answer to the question of what ASO is: arranging the information on the store page for visibility.

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Jiménez-Crespo treats this process as a natural part of app localisation. He writes that the aim is to reach the widest possible audience, and that this calls for the localisation of the elements that determine ranking, such as the app name, the keywords and the description (2024, p. 229).

In this article we look at which signals store optimisation runs on, where localisation touches those signals, and which decisions need to be taken at the outset when you open up to the Turkish market.

What is ASO, and where does it part company with web search optimisation?

The book likens store optimisation to the search engine optimisation carried out for web ranking (Jiménez-Crespo 2024, p. 229). The resemblance is real: in both cases the user types a query, the system returns a list of results, and the top places on that list make a large difference.

The differences begin with the amount of space you have. On a website you can arrange dozens of pages, headings and blocks of text. In the store there are only a handful of fields: the name, the short description, the long description, the keyword box and the images. There is no way of creating more room by producing more text.

The second difference lies in the signals. On the web side, links and depth of content carry the most weight. In the store it is behavioural data that decides. According to the book, ranking depends on the number of downloads, on ratings and reviews, and on updates (2024, p. 229).

The third difference is the intent of the audience. A web search is usually looking for information; a store search is looking directly for an action. The user is already ready to download and is only deciding which app to download. Store texts should therefore not be informative but should make the decision easier.

The signals that feed the ranking

Dividing the signals into two groups makes the work easier. The first group is the text you write yourself; the second is the behaviour of users. Localisation affects the first group directly and the second indirectly. ASO work done without seeing that distinction amounts to no more than swapping words.

Signal	Source	Effect of localisation
App name	Publisher	Direct
Keywords	Publisher	Direct
Description	Publisher	Direct
Number of downloads	User	Indirect
Ratings and reviews	User	Indirect
Update frequency	Publisher	Indirect

Indirect effects should not be underestimated. An app with no Turkish interface is downloaded less, receives lower ratings and attracts negative reviews. Each of those three outcomes pulls the ranking down. A shortfall in localisation therefore damages not only the user experience but discoverability as well.

The reverse is also true. Good localisation can lift the ranking over time even if not a single keyword on the store page is changed. That effect moves slowly and cannot be traced back to any one campaign.

It is therefore misleading to treat store optimisation as a marketing job on its own. Editing the text may raise visibility in the short term, but lasting improvement depends on the product genuinely working well in the target language. Results come when the two strands feed each other.

The three main elements that get localised

The book lists the app name, the keywords and the description as the elements that determine ranking (2024, p. 229). The three do not carry equal weight. The name is the strongest signal and the narrowest space; the description is the widest space and the weakest signal. The keyword box sits between the two.

That imbalance sets the order of work. The name is decided first, the keywords are chosen next, and the description is written last. Work in the reverse order and the terms used in the description will not match the terms in the name.

The length limits on these fields, and the differences between stores, are a separate subject. We touched on that detail in the article on store texts in our knowledge centre. What matters for ASO is accepting from the start that the limits narrow the creative space.

Having the three elements repeat one another is another common mistake. Putting a word from the name back into the first sentence of the description means spending limited space twice over. When each field answers a different question, the total coverage grows.

Why keywords are not translated but rebuilt

A keyword list is made up of the phrases users in the source market actually type. Those phrases depend on culture, habit and keyboard layout. Translating the list word for word can produce phrases that nobody in the target market searches for. The result is a list that is grammatically correct and functionally dead.

The right approach is to build the list from scratch in the target language. The source list gives no more than a starting idea. After that you look at the names of competing apps in the target market, at the phrases that appear in user reviews, and at the store’s own search suggestions.

This work calls for a different competence from classic translation skill. Here the translator is not producing text but carrying out terminology research. Setting that step aside as a separate line item when planning the project works out cheaper than corrections made later.

When the list is being prepared, there is no need to fixate on single words. Users most often type phrases of two or three words. Such phrases are searched for less often, but the competition for them is also lower. For a new app, narrow and precise phrases can be more productive than broad and popular words.

Ratings and user reviews

Reviews are texts the publisher does not write but which affect the ranking. The book states that store ranking is related to ratings and reviews (Jiménez-Crespo 2024, p. 229). These texts are at the same time a free language resource: they show which words users use for the app’s functions.

Reading Turkish reviews regularly feeds the keyword list. The user does not write the term you use in the interface but their own. If the two diverge, either the interface term or the store text needs to be reviewed.

Replies to reviews should also be written in the target language. Answering a Turkish complaint in English reinforces the impression that the app has not really been localised. That impression is reflected in the average rating and comes back indirectly as ranking.

Preparing ready-made templates for review replies speeds the process up. The tone needs adjusting as the templates are translated; a phrase that sounds warm in the source language may come across as too familiar in Turkish. Adapting a handful of templates properly once gives more consistent results than rewriting every reply.

Update rhythm and store visibility

The book counts updates among the factors that determine ranking (2024, p. 229). That takes app localisation out of the category of one-off work. Every release brings new strings, new release notes and, more often than not, new screenshots.

This rhythm changes the way translation is procured as well. Instead of one bulk translation a year, you have to work in small, frequent packages. The unit cost of small packages is higher; the visibility cost of late releases is higher still.

The practical solution is to tie the translation calendar to the release calendar. When the development team sets a release date, it should also set aside days for translation and linguistic quality control. Otherwise the Turkish version will always arrive one release behind.

Frequent updates are also an opportunity to refresh the store texts. There is no need to change the whole description with every release; updating the first paragraph to reflect the new feature is usually enough. These small edits accumulate over time and keep the page alive.

How do you measure the result?

The soundest measure of store optimisation is the number of downloads. The book describes how companies can localise only the store description in a given market and then watch the change in download numbers (Jiménez-Crespo 2024, p. 230). It is a low-cost experiment.

For the experiment to work, you have to change one variable at a time. If you alter the description and publish a new release in the same week, you will not be able to tell which of the two produced the result. Putting changes in sequence is what makes measurement possible.

The second figure to measure is the rate at which page views turn into downloads. If views rise but downloads do not, the problem lies not in the keywords but in the persuasive text. Reading the two figures together points the budget in the right direction.

The third figure is the uninstall rate. If downloads are rising while the number of users who delete the app soon afterwards is rising too, the store text may be promising something that is not actually there. In that case what needs fixing is not the ranking but the management of expectations.

The Türkiye context

Türkiye is one of the markets in which mobile use outweighs desktop use. That makes store visibility more critical than website visibility. For many services, the user’s first contact is not the website but a search result in the app store.

On the language side, two habits live side by side. Users search for everyday functions in Turkish but write technical features in English. The keyword list has to reflect that duality. Lists built only in Turkish, or only in English, miss half the searches.

The third point is review culture. Turkish reviews are often direct and detailed; a single wrong term in the interface will be raised again and again in the reviews. Awkward as that feedback may look, it is in fact a free linguistic quality report.

Finally, the variety of devices has to be taken into account. A wide range of devices with different screen sizes is in use in the Turkish market. You need to check that store images and headings look right not only on the newest models but on mid-range devices too.

What does this mean for you?

If you are opening your app up to the Turkish market, plan store optimisation as a heading separate from translation. The sequence below is a reasonable starting point for teams working to a limited budget.

1. Record your existing Turkish download and view figures as the baseline for measurement.
2. Build the keyword list through target market research rather than translation.
3. Take the decision on the app name together with the brand, marketing and translation sides.
4. Have the description rewritten so that the first three lines carry the strongest benefit.
5. Publish changes one after another rather than all at once, and measure the effect of each separately.
6. Read the Turkish reviews once a month and update your term list.

Some of these steps can be handled by your in-house team. If you would like support with producing the text and researching terminology, you can use our quotation form, and you can look at our pricing page for scope and rates.

Frequently asked questions

What is ASO, in short?

App store optimisation is the process of improving the information on a store page in terms of ranking and visibility. It resembles search engine optimisation for websites, but its signals are different.

What is the basic difference between ASO and web SEO?

In web search, links and page content carry the most weight. In the store it is the number of downloads, the ratings, the reviews and the updates that decide. The number of editable fields in the store is also limited.

Can keywords be translated directly?

Generally no. The phrases users actually type in the target language may not correspond to those in the source language. That is why the list is not translated but rebuilt for the target market.

Do user reviews affect ASO?

Yes. The book states that ranking depends on the number of downloads, on ratings and reviews, and on updates. The language and content of reviews are therefore worth monitoring.

References

• Jiménez-Crespo, M. A. (2024). Localization in Translation. Routledge, pp. 229–230.
• Strzelecki, A. (2020). App store optimisation studies, source cited in the book.
• Google Play Console Help, store listing and localisation documentation (2024).