It is vital to be aware of one of the App Store’s significant features for efficient mobile marketing. At the time of its first release in each country, an app can get a boost on the keywords in its title via auto-suggestions. The RadASO team is about to tell you how Apple helps new apps gain more visibility and why it is so important to handle the first metadata correctly and add the most relevant and popular search terms to the app’s title.
What Are Аuto-suggestions?
Аuto-suggestions at the App Store are suggestions provided by the store when users type a few letters or a word in the search line.
It is very common for a user not to finish typing the search phrase and just tap on the search prompt offered by a store. That’s why these auto-suggestions are a valid method of attracting more traffic.
How Does Apple Form Auto-suggestions
The company won’t disclose this info. However, the RadASO team makes an educated guess that the algorithm works based on the following aspects.
User’s Search History
The platform may note the user’s search history to suggest apps that match their interests and preferences. If a user often searches for a particular type of app, the auto-suggestions will prioritize similar apps above all else.
Popular Search Terms
Algorithms often consider other users’ search requests. If particular search terms or apps are popular, they are more likely to appear in the auto-suggestions.
Search Terms From the Apps’ Textual Metadata
The algorithm indicates search terms used for similar apps and adds those to the auto-suggestions.
Follow RadASO в LinkedIn, to stay updated on ASO and UA news.
How To Configure the Auto-suggestions For Your First-Timer’s Boost?
While you work in your textual metadata, add to the title the most popular and traffic-generating search terms for each country. A search suggestion may appear for every term in the title. That’s your golden opportunity to gain more views on relevant search terms in a competitive niche.
The brand name can be easily added to the title on one of the post-boost releases when the time comes to improve brand awareness.
According to the RadASO team’s observations, the auto-suggestions will continue appearing for the next five days for each search term in the app’s title.
Practical Examples Of Apps’ First Release Boost
For its first release, this app had “shopper: grocery shopping list.” in the Title. The boost started immediately after the release, and suggestions (prompts) for other search terms appeared. The boost lasted for five days.
For its first release, this app had “word duo – crossword & puzzle” in its title for the English (UK) localization. After the release, auto-suggestions appeared in the UK and other countries because English (UK) is active almost everywhere. The boost lasted for five days, the same as in the previous example.
So, the app’s boost during its first release is a chance for a new, totally unknown app to attract a significant amount of traffic via auto-suggestions gifted by Apple. The most important task here is to be smart about goal setting and use the maximum possible number of relevant and traffic-generating search terms in the app’s title.
For businesses looking to maximize their app's visibility and user acquisition through strategic mobile marketing efforts, partnering with a specialized mobile marketing company can provide tailored strategies and expertise to optimize app store optimization (ASO), user acquisition campaigns, and overall mobile marketing performance.
Related Articles
Agency vs. In-House vs. Freelance: What to Choose for Website Maintenance
I'll break down the pros and cons of each model according to your tasks, goals, and current stage of business development
Email Newsletters for Online Stores, From Idea to Launch
Email newsletters are one of the most useful tools on the way to sales success and recognition. Let's take a look at how they can generate revenue
GA4 Attribution Model Comparison Report: A Complete Guide
The report shows the performance of campaigns and channels for a given time period, comparing only attribution models.