For whom is this article intended?
This article will provide useful information for all developers who have run app search optimization. Companies who own many apps, or who use private agencies to provides services for search engine optimization, will also benefit greatly from the information contained herein, as will anyone involved in the marketing and sales of mobile apps who would like to understand what App Store Search Optimization is and how to use it. The author is positive that all readers will want to review their achievements in the field of search engine optimization for their applications.
What is ASO?
App Store Search Optimization (hereinafter ASO) is a set of techniques that allow you to achieve organic growth of your product primarily through the search settings of your mobile application. ASO is now gaining popularity among developers of mobile applications. More and more companies are trying to improve search engine optimization for their mobile applications for every build release, for their purposes it is crucial for search requests to be of the highest accuracy. In this article we’ll examine data regarding the frequency of the search request.
The problem regarding lack of information
In today’s market, there is a general lack of information about how popular certain search requests are as app stores (App Store and Google Play) do not share this information. Perhaps part of the problem will be solved for the App Store with the advent of Search Ads from Apple. The service however, is not available for all countries, and also carries other functional loads, and is therefore not a tool for optimization.
Where does the bad data come from?
Such requests for information may be popular on the web, but unfortunately are not when it comes to mobile app stores. There are many reasons for this, for example, the average user is unaware that he/she can search for applications by their specific functionality. As an example, not everyone understands that it is possible to find applications for flower delivery or car washes. These markets are just beginning to develop, however, and in the future the situation will be quite different.
Unfortunately, many ASO-services do not calculate the frequency of search requests by using existing app store infrastructure, and instead, only use search request data from the Web (the author’s research was conducted on more than 100 000 search requests that were not published in accordance with fair competition practices).
How does the app store search work?
Let’s see how the search works in the App Store. It all starts from the home screen where trending searches for the region are displayed.
When a user starts typing a search request, it immediately issues a list of suggestions.
Suggestions are already ranked by descending popularity, i.e. how often users entered the keywords or clicked on the suggestions. The suggestions to the main requests come primarily from the names of applications — either a single word, phrase, or a full name.
If the request is not included with the related searches, it is either not popular or “banned” from the app store. For example, porn and pornhub are not included as suggestions in the US, despite being a popular request.
A comparison of two ASO services
To confirm that the data regarding the frequency of search requests from the web (reduced to a scale of 0 to 10) and the actual number of searches have nothing to do with App Store apps, the search suggestions and the data of one popular ASO service vary widely compared to the data from the ASOdesk.com service — which uses only data from app stores, and takes “banned” requests into account. Scale ASOdesk shows the number of people (Traffic Score) by search requests and is not limited to an upper value.
Germany. Keyword: dating seiten
Famous ASO service data:
Comparison of the frequency of keyword, “dating seiten” on two ASO services: ASOdesk.com, and real positions of the search suggestions on the app store in Germany region. According to the data, it is clear that this search term can not be a “good” indicator of frequency because the request does not appear in the search suggestions of both.
USA. Keyword: acquaintance
Famous ASO service data:
Comparison of the frequency of keyword, “acquaintance” on two ASO services: ASOdesk.com, and real positions of the search suggestions on the app store in the US region. According to the data, it is clear that this search term can not be a “good” indicator of frequency because the request does not appear in the search suggestions of both.
UK. Search term: hookup & hookups
The following example is interesting as it shows not only a comparison of the two ASO services for a single search term, but also their position relative to each other in the App Store search suggestions.
Keywords ”hookup” and “hookups” have a comparable search frequency according to a popular ASO service, the second request performing better than the first. If we look at the App Store suggestions, we can see that the request “hookup” appears much earlier than the ”hookups” search term. Given the fact that the suggestions in the App Store are formed on the basis of popularity, this tells us that the keyword ”hookup” is a more popular search term than ”hookups” — exactly what we see on the ASOdesk.com service.
Famous ASO service data:
Icing on the cake
ASOdesk made a special trash USA iOS search requests chart and compared Sensor Tower, Mobile Action and ASOdesk. Below you can see the requests and their traffic according to the system. Enjoy!
Thus, we can conclude that the web-based data drawn from one of the most popular competing ASO services, can cause you to make some serious errors when setting priorities for the ASO of your app. Losing sight of the most frequent search requests — as an indicator of popularity on the App Store — will cause you to fail to reach your targeted user. Therefore, if you want to have the most efficient ASO for your app, in which there is no error of prioritization in selected search requests, you must first determine the source of the search term data.
ASOdesk.com is based only on the mobile data that creates the search suggestions in the first place. This ensures that you get relevant data regarding the mobile search situation and make the right decisions to attract more customers to your app every day.