WakeApp: Why you need to expand the semantic core for ASO now

The WakeApp marketing company wrote for App2Top.ru a column in which she told how very soon the search suggestions will change the ASO promotion in the App Store.

Apple announced the improvement of search in the App Store with the help of relevant queries (Related Search Suggestions) back in March 2014. All users from the USA, after long testing, have these requests recently. Until the innovation appeared in the Russian App Store, we decided to make our own predictions about how relevant queries will affect user behavior in search and how this will affect search promotion strategies.

Apple is constantly looking for ways to improve the search in the App Store, the number of applications in which has long exceeded 1 million. Since the search accounts for about 70% of all downloads, the introduction of additional guidelines in the search is really necessary – it can be very difficult to navigate in a crowded store.

What are the hints of relevant queries that should be expected soon in the Russian App Store? This is a small additional block under the search bar, which displays keys similar in semantics, designed primarily to clarify what the user is interested in and help him find an application for a narrower (low-frequency) query. Queries scroll to the right – you can scroll through them and select the most interesting one. After clicking, you will be taken to the search page for this query. Enter hotel deals – you see California in the hint, and if you just wanted to find out about hotels in California, most likely click on this hint and go to another search page. It looks like this:

That is, in fact, the lion’s share of traffic with this approach will go to low–frequency queries, and to applications that are not in the first positions for high-frequency target queries, the user may simply not scroll through – now he will be quickly taken to other search pages by relevant hints.

We decided to test the output of relevant queries a little in order to understand by what criteria the algorithm selects hints. This information will be very useful to you when you expand the semantic core of your application.

1. Analyze the semantics of hints. We enter the dating apps query and get the hooking, gay chat, online chat, meeting new people subtasks. It turns out that we are offered to clarify, narrow down the request – maybe we are more interested in chat, or specifically meeting new people. Hints do not go beyond the scope of the subject:

2. Check whether relevant queries are related to search trends. We look for hints in the search – all these queries appear in a prominent place. Immediately we see that relevant offers remain under the search bar so that at any moment you can change your mind and click on them:

3. Check whether the suggestions take into account the search history. We follow the prompts a bit and enter the funny pics query again. We get a new set of hints related to the query that we clicked on for the first time. We conclude that the algorithm takes into account the search history:

So, by semantics, we found that the algorithm takes into account:

• query semantics (only queries on the topic or related topics are shown);
• search trends (only those queries that are often typed into the search bar by other users);
• search history (if you click on a mid-frequency query, next time you will be offered more low-frequency associated with it).

Undoubtedly, for users, this feature opens up new opportunities for orientation in a crowded app store, saves time on search and makes it easier to navigate through search pages. As for search promotion strategies, we recommend that you start expanding your semantic core to low-frequency queries right now and optimize the application page, if you haven’t already done so. Otherwise, the loss of traffic from the search after the introduction of relevant queries can greatly disappoint you.