For developers, how to select effective keywords and develop a good keyword coverage plan is the most important foundation of ASO optimization, so how to select the "best" keywords for the app has become a skill that developers need to master.

How to pick the best keywords

At present, there is one most important and unmissable measure for the "best" keywords under the search algorithm and ranking rules of App Store: high search index and low competition.

Since "search" has become the main way for users to download apps, the main purpose of setting keywords for apps is just to improve the discoverability of apps.

The keywords with high search index and low competition can greatly increase the chance of the app being exposed to the users' eyes, thus increasing the chance of the app being downloaded.

We all know that there is a index of the data in the App Store, and each keyword corresponds to an index.

So what is the index? The index represents the hotness of the keyword, representing how many people are searching for the word. The more people search for the same word, the higher the hotness index of the keyword.

The index is a data used to show the search volume of keywords according to the App Store search rules. According to the index setting rules, the search volume of keywords below 4650 hotness is less than 1 a day.

As the search hotness value and the number of searches are one-to-one correspondence, then if there is a hotness value corresponding to the number of words, accounting for the largest proportion of all words, then the hotness, corresponding to the number of searches is 1.

According to the actual experience, the daily search words, heat value should be at least greater than 100. because the heat value of less than 100 words, basically some app name without any comments, or some very long words.

According to the statistics of the heat value of all search terms, there are about 210,000 keywords with a heat value greater than 100. Among them, the most words with a heat value of 4605, there are 58,000, the proportion of about 28%; and the second is the heat value of 4606 words, only 9,000, accounting for 4%.
4605 heat value of the word far more than the number of words corresponding to other heat, so it can be assumed that the value of 4605 heat corresponding to the number of searches for 1.

Of course, the data of 28% also shows that the proportion of long-tail words in Appstore is more than other platforms.

 

Estimated search traffic according to the number of downloads

Apple's statistical system, the core data is mainly the app content page display volume and the real download volume, because the display volume source is not given, this data value is small, we mainly use the download volume data.

The rough idea is that the app downloads are known, then roughly 65% of the downloads are brought by the search, and most of the downloads of an app are brought by the word with the highest hotness and the first ranking, using this process, we can roughly estimate the word with the highest hotness and the corresponding search volume.

50% to 100% of App downloads come from search, the official data is 65%. But considering that most of the App is not on the top150 list, it can only be obtained through search, and the proportion of downloads from the list of App, especially the head ones, will obviously be larger.

The conversion rate from search to download is calculated according to 2:1 to 4:1, that is, 2 to 4 times of search can produce one download.

This ratio comes from some Android market statistics, because the app store does not have first-hand data, temporarily can only be calculated according to this ratio, where the conversion rate of industry words will be lower, brand words will be higher.

search volume all corresponds to the highest app hotness, ranking first a search term. If there are more than one hot words, evenly divided search volume.

We based on the above assumptions, combined with some of our actual rush ranking experience.

We use the data fitting method to get the correspondence between the hotness value and the search volume. Some typical heat degree value corresponding to the search volume is shown in the following table.