Chat GPT for ASO optimization
We couldn't help but check out the way Chat GPT can be used for ASO optimization.
What is Chat GPT?
It is a language model developed by OpenAI. It was trained on a variety of texts to generate texts and answer questions in natural language. This allows Chat GPT to communicate with the user and provide information on various topics based on their training. The model can be used in various applications such as chatbots, virtual assistants, etc.
We decided to try to go through the basic steps of ASO text optimization using this tool. Let's see what happened.
Text ASO optimization
We will try to set tasks in the most free form, which will include the main steps for the formation of application text metadata. And if necessary, we will connect mobile analytics tools.
Step 1 - Collecting the semantic core
We asked politely to make the semantic core of the mental health application, indicating its main functions - sounds for sleep, white noise, positive thinking and affirmations.
The result was more than satisfactory, or we just don't expect much from the AI yet, who knows. It is worth recognizing that in analytics we can still rely on the competitive field as a source of semantics, but here the sources of inspiration for Chat GPT remain unknown to us.
But seriously, we refined the request - we requested a semantic core for 100 keywords and got the result.
As a result, we got a semantic core for 100 queries, which we will definitely add to the analytics to check for relevance and traffic. But first, let's ask our assistant - is it possible to analyze the proposed semantics.
Step 2 - Keyword Analysis
The next step, we set the task to evaluate the traffic rate of the suggested keywords.
The basis for the analysis and evaluation of keywords is two of their characteristics - the presence and amount of traffic, as well as relevance. Chat GPT will obviously not help us with the first, since it does not have access to the traffic database.
Step 3 - Form the name of the application.
Here we spent several iterations, since a lot depends on the wording of the request.
The first request is to create 10 titles for the mobile application with the specified keywords. Limit - 30 characters.
The second request - we just asked to generate a new response without changing the original request.
The third request - clarified that it is not allowed to repeat keywords inside the application name
Step 4 - Form a short app description
Request for a short description (5 options) for a mobile application using keywords generated earlier. Limit - 80 characters.
For a short description of a Google Play application, it looks pretty good, and at least it can be used as a starting point for working on a short description.
Step 5 - Generate an app description
Request for a text description of a mental health mobile application using keywords. Volume - 2500 characters. Avoid overspam - use the keyword no more than once per 250 characters.
It looks very good; it remains only to check whether this tool understands the concept - the frequency and density of the occurrence of keywords. Which we did by rephrasing the query a bit:
At first glance we found the Relaxation key - 4 times. Perhaps the query should be built with more stringent restrictions.
Chat GPT vs ASOMobile
Let's now check these metadata in professional tools; namely, we will analyze the semantic core, evaluate the relevance of keywords, and check the metadata.
- Keyword Monitor - to evaluate the semantic core for traffic and check keywords for relevance
Conclusion: about half of the proposed keywords have a 0 traffic indicator. And not all suggested keywords are relevant to the application (here, of course, it is worth mentioning that relevance could be tried to be set by a complete description of the application).
- Text Analyzer - will help evaluate the text of the description, check it for spam, and for the presence of the search queries we need.
So the description of 1730 characters, the system indicates a small overspam of a part of the key phrase - self. But let's look at what keywords the analyst found in the description text:
Looks great - a lot of keywords and phrases are used in the text.
- ASOCreator - a tool for working with metadata
At first glance, the metadata looks great, but successful text optimization is based on a well-formed and carefully analyzed semantic core of the application. And we will remind you that the analyzed initial version of the semantic core is worthless (from the point of view of ASO optimization, of course).
Chat GPT is a great source of inspiration for working with naming, creating short or long texts, searching for ideas, and much more. But the lack of an analytical apparatus for analyzing semantics, calculating traffic, and other important characteristics of keywords does not make it a “universal solution to all problems”, much less a replacement for professional mobile application analysts.
To use or not - it's up to you. And we ask you not to take this article as a serious study and guide to action; we keep up with trends and innovations and cannot ignore the emergence of such cool tools on the market as Chat GPT.