7X Organic Downland In Two Months [ Real ASO Case ] 2022

ASO for google play takes time, so should you ignore it for that reason? You decide after you read it all story from the real case study.

I explain how to grow 7X organic downloads for the tools category in Google play the US by using wise tactics which guide me to choose the right keywords.

When i start working with an app, for a specific country and language, I need deeply to understand the app as well as its keywords in metadata, current listing position, target country and language, UA campaign, and more…

A few months ago, one of my clients want me to do ASO for the US market but have not budgeted for UA. So our main focus is to gain best position for target keywords and increase downland as much as possible because we know what keywords our users use for finding in the listing.

There is an important point here, one of main app store optimization factors is downland velocity, which means that more downland in a short time of period more change you rank fast and stand. This is another topic soon we will discover that. But it is beneficial to launch ASO and UA together.

The client has an app for templates for posts, posters for social media. If you know this category is competitive enough to increase downland and beat the target.

As you know there are some big players on the listing, having cool features, offers, so that we will do our best to outrank them.

The main goal for my customer is to get users from Google play in US when app was launched 2021 march. As all we know that US is one of the hardest country to rank but country’s population is huge and user are likely to pay more than any other country. So that’s wise attempt but need to hard work.

Unfortunately, many use manually mothed analyzing keywords, competitors, app result pages which takes a huge amount of time. So you will uncover how i easily overcome those manually work with great software which i have made it thanks to my software background. This tool helps me to choose the right keyword, save time and competitor analyses, and more. This tool is free for the beta test. You can ask for an early request from here.

Before Our Implementation: On Meta Data App

Before we start to work on app metadata, should be analyses data. So you can find the former situation below:

Google Play – US Market – English Language Store Listing) :

Title: 30/50 characters, but not optimized for the main keyword and related keyword

Short Desc: 77/80 characters, proper length but not optimized for google play store

Long Desc: 1000/4000 characters, less keyword usage of main, and related keywords.

How can Mobileuply is increase your downland?

We ruled a benchmark study where we can find keywords, competitors and market.

i will explain these in detail where you can learn how to approach ASO for google play.

Are you ready?

Finding Keywords For App

I collect keywords that represent the app as its feature and problems which solve. I do not believe you need more than 50 keywords as long as you will apply

Well in this first keyword research, you will not need to have more than 10 keywords to find competitors. That is what we have a basis for further analysis. Please note that these keywords are just first to attempt to get the right direction and more keywords.

As instance, if your app is to help people who want to track their daily calories. So you might come up with keywords such as calorie tracker or calorie counter, daily calorie app… when you have them, just save them further analyze.

Competitor Analyze

When you have a bunch of keywords for your app, then it is time to analyze each keyword on store to see which apps Google Algorithm promotes. That is crucial part of this all ASO process because we will make sure that we choose the right keywords by looking at competitors. If market search engine result page are full of hard competitor which has a million downlands and reviews while you might do not want to compete with them at that time. Sometimes this is not case, even if some apps are few review and downlands as compared to main competitor, they have rank some specific keywords. That’s why i created store search engine result page analyzer. It shows important app’s data such as title, subtitle, long description… you can try for free. See of one example of that.

ASO Tools Competitor Analyse

So it is better to see big picture as what your app will potentially compete with. When you define your keywords as representer and related to your app. You can analyze each keywords with SSERP ( store search engine result page ) then prioritize keywords for app based on result page.

Finally, you choose one or two main keywords.

Keyword Expansion

Compared to other ASO keyword research, i have different approach as we remove unrelated keywords at beginning of this process. Keywords, that you think is most relevant and high volume, has result page on google play when typed. Now it is wise to shop here, dig more.

Behind the logic of my approach that find any keywords that related your main keywords. This relationship could be long tail, modifier, synonym… you can do by looking at the result page of each keyword in google play however this is taken so much time. That’s why i created a related keyword analyzer that searches and analyzes given keywords whether related to the main keyword or not. As example. In our case, thanks to our NLP software could find super related keywords to main keywords that allow us to use app meta data.

Through the keyword research, we got concentrated keywords that would be better to use in-app store listing.

Then i upload new version of our app with new keywords.

So for the US market, English store listing:

Title: 46/50 characters

Short Desc: 72/80 characters

Long Desc: 3600 / 4000 characters

I optimized these fields with related each other and main keywords, then create content with powerful main and related keywords that allow search engine understand app store listing context.

So what happened after implementation?

Google has great algorithm to understand app meta data, however, it needs time to check important signals for app. More these are discovered, more apps have more change to make the right move to rank fast and high. After applying the abovementioned keywords strategy, app get more visibility, as it helps more click and downland. Every action on the play store was taken by the user is recorded by Google. Having the right information in-app metadata attracts Google spider more and more then ultimately keyword and app’s association become more obvious and stronger. Then Google promote our app higher and higher listing.

Then we boosted the organic visitor at google play up to %200 and organic downland 7x.

What’s importance of keyword research?

Main outcome of having great keyword research ( as well as competitor benchmark ) was that we can rank high and fast. Moreover, a straightforward result of ASO is to get keywords to reach at least the top 5 and at the top 10 thanks to right keywords strategy with our software.

One main impact i want to emphases that even if app is new, had great momentum to be associated with new keywords and rank for them. This case study also shows us that new apps can compete with big guys in play store when you optimize right points. Please result…

ASO-Before-After

This was based over the fact that google play can know reaction of user for each keyword. This is best feedback for app to compare apps performance based on user experience. So I believe google can promote an app not just based on number also some of velocity attribution in certain time of period.

There is huge different ranking factor between google play and app store, that’s why we have used power of long description for google play along with short descriptions and titles.

I believe long description has less weight as compared to title but if you can’t properly edit long description, it is hard to rank in google play. This is how this app ranks fast and better.

Please note that ı have been successfully ranked for some keywords so far. So I will test it more with more competitor keywords and another variable. If you are curious how they will perform, do not forget to follow me in twitter.

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