ChatGPT, an AI chatbot, assists app developers with App Store Optimization (ASO) tasks such as metadata generation, app description writing, proofreading, and translation. AI crafts metadata, app descriptions, and ASO strategies, and handles marketing tasks like translation, competitor data analysis, and keyword list generation. It can also enhance app graphics using neural networks like Midjourney and DALL-E.
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In this article, we've crafted a step-by-step guide to provide a visual demonstration of its functionality in the ASO field. We'll delve into two distinct approaches for the App Store and Google Play, examining them separately.
Artificial intelligence and App Store optimization can produce superior results. As an example, we will use an app in the Fitness category.
This step is important and can improve your ASO results. Let's start by asking ChatGPT to imagine itself as an expert in ASO, using the cheat code "Let's imagine". It is worth adding that for an effective response. We also need to specify the exact name of the app and its corresponding Store ID or link to the App Store.
A few seconds later, we receive a brief analysis from ChatGPT, which provides basic information without specific details. This information is likely to be the most useful for those who are newcomers to the field of ASO.
The next crucial step is to gather information about competitors for further analysis and to identify our application's comparative advantages. To achieve this, we will provide AI with general information about our application, so it can prepare a list of competitors.
We've identified 10 competitors with ChatGPT's help. Generally, all apps that ChatGPT specified are considered competitors, occasionally including certain other ones that the algorithm deems relevant. This occurs because the neural network selects apps based on similar features, as explained by ChatGPT. In practice, this aids in initial competitor research and basic analysis or expanding your current list.
For a more precise answer, we have provided the competitors it suggested as examples. This avoids redundancy and helps pinpoint the niche more accurately.
The response was quick, and we received a list of 10 additional competitors with an app similar to ours.
With this information, we can revisit ChatGPT and request more detailed analytics for our app. This will provide us with analysis and recommendations for each of the app page components, including the Title, Subtitle, Description, Screenshots, Icon, Ratings, and Localization.
Conclusion. This ASO analysis is semi-professional. Nevertheless, it's crucial to acknowledge that neural network analysis may contain errors, necessitating data verification. For instance, ChatGPT erroneously indicated that the application has a good rating.
*Current rating in the USA
Although such errors are relatively uncommon, they can still occur. Therefore, it's essential to exercise caution and thoroughly review ChatGPT's responses.
A neural network typically gathers approximately 20 words as an initial keyword list, but it's important to note that this is not a strict limit. We can further expand it by making a request such as "collect semantic core" or "augment semantic core" to ChatGPT. In response, ChatGPT will initially propose around 20 words and then augment the list with an additional 20 words.
For the next query, we had ChatGPT generate a keyword list consisting of 500 words, which presented some challenges as we initially received a limited number of search terms. After analyzing and comparing the keyword list from four iterations, it became apparent that many keywords were repetitive. However, by consolidating them and removing duplicates, ChatGPT compiled a comprehensive keyword list containing a total of 133 unique keywords.
Conclusion. The neural network lacks crucial metrics for keyword list collection, including:
Please be cautious when using SAP keywords generated by ChatGPT, as they are random and often inaccurate.
Reminder: ChatGPT 3.5's knowledge is limited to data available until September 2021.
With the keyword list in place, we can instruct ChatGPT to generate metadata for our App Store application. However, to prevent overly long headers, we must manually specify character limits for the Title and Subtitle, such as 30 characters, when setting constraints in our query.
As in previous tasks, we can once again request additional Title and Subtitle variations, resulting in a greater number of choices.
Some of the Titles and Subtitles suggested by ChatGPT may not be suitable due to character limits or the partial use of brand names. There is always the option to edit the suggested choices or use them as inspiration or templates.
However, the situation differs with the Keywords field. When asking ChatGPT to provide Keywords to use in the application, it provides a vast list of keywords, some of which are duplicates from the Title and Subtitle fields. Unfortunately, there is no way to eliminate duplicates, and AI consistently includes at least one duplicated keyword.
ChatGPT itself emphasizes that it gives you suggestions from which you can select the keywords that best suit your needs.
Conclusion: When it comes to metadata collection, AI can be your helpful companion. It cannot operate as a stand-alone tool capable of handling tasks independently. Its strength lies in creating templates and inspiring ideas for your headlines.
Another useful functionality at your disposal is working with text-based descriptions of the app and compiling user reviews. This tool is highly versatile, as ChatGPT can create descriptions and responses to feedback tailored to your specific requests and requirements.
Let's begin by addressing the application's description. We have the option to request a general description, or we can specify certain app features for AI to highlight in the description.
Functionally, submitting multiple competitor description variants for analysis can enhance the quality of the output and better convey the app's essence.
In this case, we presented it with five descriptions from different competitors, each with unique app functionalities. ChatGPT extracted the most relevant keywords from these descriptions and integrated them into a unified text.
While app descriptions aren't indexed in the App Store, creating relevant and captivating text content that effectively outlines the app's main features is strongly recommended.
The next step is to create reviews. We can either provide a template or specify app features and keywords for ChatGPT to write the reviews.
As an example, we will use another app in the Fitness category. Implementing ChatGPT in Google Play metadata closely mirrors the process in the App Store. The collection and analysis of competitors and the keyword list are essentially the same.
We only need to instruct AI to generate a list of competitors and a list of keywords using the main app's name and ID.
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Google Play doesn't feature a dedicated keywords field; instead, it relies on the full description to gather keywords from. Therefore, our aim is to incorporate as many relevant keywords as possible and fit them naturally in the content of the app description.
Let's emphasize the two primary metrics crucial for our work:
Tips. To create a high-quality description, collect competitor examples from ChatGPT. Avoid including too many similar texts. The ideal number depends on your app's niche. For similar niches, around 5 texts should suffice; for diverse niches, use 10 or more to cover all variations.
It turns out that the finalized text should achieve high scores in both metrics. For Confidence, the target value is 0.8 or higher, and for Density (keyword frequency), our standard is a value of 2%. We will check the Confidence and Density values using Google Natural Language and WordCounter.
Let's proceed with text generation using ChatGPT. Here are the statistics for the original description:
The current text requires revision because it has a low Confidence score. On the other hand, Density has reached the optimal value for many keywords. However, this is influenced by the text's relatively small length, which in our case is only 500 characters.
Our initial step was to request a new description for the app.
The outcome was a significantly larger text that greatly improved all the existing metrics.
Similarly, we add new metrics here:
After the first text iteration, which did not include competitor examples or keyword selection, we prepared another description and increased search visibility.
Our work continues as we focus on enhancing individual metrics. Our first priority is to request ChatGPT to increase the Confidence value of the new text:
Here is the Confidence value of the new variant:
We've achieved remarkable results, but our text still has imperfections, particularly regarding keyword coverage. Fortunately, AI can help by increasing keyword density and frequency.
In our case, we utilize the keywords previously provided by ChatGPT but request an increase in their frequency. We select queries from the keyword list and incorporate them into the text.
Following this, we received a new text. The Confidence score dropped slightly to 0.95, still a high value for our set of criteria. Despite the text's increased length, keyword coverage improved.
Here is the result of the final text analysis:
Conclusion. ChatGPT excels in text generation, consistently delivering clear and predictable results with our 'advanced' requests.
More significant tasks where ChatGPT can streamline our work:
However, these tips represent just one part of the equation when it comes to utilizing AI effectively. The ChatGPT user community has uncovered various 'cheat codes' that can enhance its usability. While there are many more such tricks, we've highlighted the key ones:
step-by-step (to help point out the solution to your query)
with references (links) / examples (ChatGPT will add sources and examples to your query)
continue, keep going (continue to generate current query)
let's imagine (help to describe the abstract situation in terms of AI)
summarize it: link (ChatGPT will analyze given information and summarize it)
rephrase, try again (re-generate the current request)
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