This is where ChatGPT, an advanced language model developed by OpenAI, comes into play. Trained on a vast amount of internet text, ChatGPT can generate human-like text based on input and translate to a high standard. 

ChatGPT translation can handle turning English text into various other languages, while retaining the context and tone of the original text. We decided to integrate this powerful tool into our translation process, the steps of which can be found below.

Our Translation Process

Below is an example of how we used ChatGPT to translate an English file into German:

1. File Preparation

We started by preparing our English language file with all the text strings that need translation. This file was the foundation for ChatGPT to work on.

2. Feeding the File to ChatGPT

Next, we fed the English language file into ChatGPT. Inputting the data in a format that the AI model understands and yields the desired output was crucial. We used the English sentences as prompts and asked ChatGPT to translate them into German.

3. Translation

Within minutes, ChatGPT processed our file and provided a German translation. The speed and quality of the ChatGPT translation were impressive, handling complex sentences, industry jargon, and nuanced language effortlessly.

4. Review and Integration

Despite ChatGPT’s high accuracy, we ensured our applications met our quality standards. Our team reviewed the translated content for any necessary tweaks. Once satisfied, we integrated the translated files into our application, adhering to Atlassian’s best practices.

How to Measure the Impact of Localization

It’s important to track KPIs both before and after app localization efforts have been completed so you can compare how effective that localization has been. Some essential KPIs to measure include:

• Downloads and user growth rates across different markets. 
• Session lengths including daily and monthly active users (DAU/MAU). 
• User retention rates, churn rates and conversion rates. 
• Customer satisfaction and feedback. 
• Revenue growth and in-app purchases. 

Tracking these KPIs ensures you know the ROI of your app localization strategy.

The Outcome and Future Prospects

The result is a highly localized version of Checkout and Abacus for our German-speaking users, achieved with minimal manual effort and maximum accuracy. The efficiency of the translation process allowed us to quickly support multiple languages.

With ChatGPT, we have a scalable app localization solution that grows with our needs. As we expand our language support, we are confident in providing a truly localized experience for all our global users.

Using AI in app localization demonstrates the immense potential of artificial intelligence in bridging language barriers and enhancing user experiences worldwide. We’re thrilled about this integration and look forward to further leveraging AI to improve our applications.

As noted by Atlassian, AI is fundamentally changing work for the better, augmenting our capabilities and allowing us to achieve feats like deploying multiple languages swiftly and efficiently. Atlassian Intelligence brings the power and magic of AI into Atlassian’s Cloud products.

Stay tuned for more updates as we continue this exciting journey of innovation and localization and contact us to learn more about our services and products. 

FAQs

Why is app localization necessary?

App localization is necessary as it ensures your application resonates with users globally by adapting content, design, and functionality to culture and language.

How much does app localization cost?

The cost varies depending on the number of languages, volume of content, and whether you use manual translation or automation. 

What is the process of app localization?

The process includes preparing your text files, using tools like ChatGPT for translation, reviewing results for quality, integrating them into your app, and testing before releasing.