Different types of app localization

When expanding into new markets, marketers must consider the depth of localization needed to meet their goals effectively. There are two main approaches to app localization: the MVP (Minimum Viable Product) approach, and full localization. 

The choice between these options depends largely on the company’s growth strategy, resources, and specific market objectives. Let’s look at the unique benefits and challenges of each approach.

MVP app localization

The MVP approach aims to quickly and efficiently adapt the app to a new market with minimal resources and time investment. The thinking behind such an approach is often to enter a new market as soon as possible, with the aim of adapting and optimizing the experience on the fly. 

Key characteristics of this approach include: 

• Targeted translation. Translating essential text elements such as menus, calls to action, and product pages. 
• Limited scope. Localizing only the core features and functionalities
• Minimal cultural adaptation. Relying on basic language translation rather than adjusting fully for cultural nuances. 
• Quick launch. The aim is to achieve a quick market entry to test the app’s viability in the new region.
• Post-launch optimization. User feedback and analytics is used to guide further localization efforts and prioritize subsequent improvements.

There are several benefits to the MVP approach, including the speed of deployment to new markets, and a more cost-effective initial investment which allows brands to test the market before making further improvements. 

However, this approach restricts the user experience, and can limit the appeal of the app. By presenting an MVP version, businesses run the risk of  deterring users and stalling expansion plans. 

One example of an MVP app is the first Ubercab app, which launched in San Francisco using a prototype app to test the concept. In this case it worked, and the concept of MVP fits well for tech startups. For brands with more established apps, there may be drawbacks to this approach. 

Full app localization

This approach is designed to launch an app which has been fully adapted to meet all the linguistic, cultural, technical, and legal requirements of the target market, with a more relevant and seamless user experience.

Key characteristics of this approach include: 

• Full translation. All elements are translated into the local language, including help documentation, support content, and marketing content. 
• Cultural adaptation. Visuals, cultural references, and user interactions are carefully adapted to align with local  preferences. This may include redesigning areas such as imagery and copy to be culturally appropriate.
• Legal compliance. Ensuring that the app complies with local regulations and standards, including data protection laws and content guidelines.
• Pre-launch testing. Thorough testing with local users to ensure that the app functions smoothly and meets local expectations.
• Full localization of features. All features and functionality are localized to ensure a consistent and complete user experience.

This approach is more time consuming, with key processes such as testing and full translation potentially delaying the launch of the app, while a greater up-front investment is needed before launch. 

However, this investment in time and resources can produce a better, more relevant experience for users, which should translate into increased satisfaction and engagement, as well as faster growth. 

While the app may still need to be optimized on the go, full localization provides a stronger platform for success, and gives apps a competitive advantage.

How marketers should approach app localization

Effectively localization requires a comprehensive approach that accounts for language, cultural nuances, technical and legal aspects, and a process of testing and optimization.  

These are some of the main app localization challenges for marketers:

Here are some key areas to consider, which will help marketers to address these challenges above. 

Market research 

• Understand the target customer. Gain an understanding of local culture, user behavior, preferences, and competitors.
• Gauge demand. Evaluate the demand for your app in the new market. Consider factors such as market size, growth potential, and local economic conditions.

Define goals and prioritize key localization elements 

• Set clear objectives. Define your goals, whether this is user acquisition, engagement, revenue growth or a combination of the three. 
• Prioritize. Decide which parts of the app should be localized first, based on potential user impact and strategic importance.

Translation and cultural adaptation

• Focus on quality. Implement quality assurance processes such as proofreading and user testing to ensure translations are accurate and contextually relevant.
• Use machine translation. Machine translation combined with automated language quality assessment tools can improve the accuracy of translation, and speed up time to market, as well as subsequent content updates.  

Cultural adaptation

• Adapt visual content. Images, icons, colors, and graphics should  align with local cultural norms and preferences. Avoid content, colours and images that could be culturally insensitive or have any potential for misunderstanding. 
• Tailor content and messaging. Transcreation of marketing messages, slogans, and promotional materials ensures cultural sensitivity and alignment with what resonates with local audiences. This process demands thorough research into the local market. 

Technical and legal considerations

Ensuring a seamless and compliant user experience goes beyond translation and adaptation – technical and legal considerations that can make or break your app’s success in a new market. From supporting local formats to meeting regulatory standards, these elements are critical to delivering a fully localized app: 

• Formatting and design.  Ensure that the app supports local date, time, and number formats, as well as currency symbols and units of measurement. Aspects such as checkout forms should be adapted to input fields that match local conventions, such as address formats and phone number patterns.
• Understand local regulations. Ensure compliance with local laws and regulations, including data protection laws, content restrictions, and advertising standards.
• Integrate with local services. Where appropriate, your app should be compatible with local payment gateways, and other third-party services that are popular in the target market.

Testing and optimization 

• Conduct user testing. Test the app and gather feedback from local users, which helps to quickly identify any localization issues and areas for improvement. Monitor user reviews, ratings, and other feedback to address any emerging issues.
• Track performance. Monitor key performance metrics  such as downloads, user engagement, retention rates, and revenue in new markets. 
• Optimize continuously. Based on feedback, testing, and performance data, the app should be optimized on a regular basis. 

In the next part of this article, we’ll look at what marketers need to know about app store localization, and how they can choose the most effective app localization tools. 

When we talk about artificial intelligence (AI) in localization, there’s a natural tendency to speak about it from an operational angle.

After all, the right use of AI and Large Language Models (LLMs) has been busily revolutionizing high-volume machine translation (MT) for some time. Companies can now handle and translate vast amounts of content quickly and efficiently.

However, there is another major impact that we’re perhaps guilty of dancing around – how exactly will AI impact jobs in our industry.

As more automation and machine-assisted processes become the norm, there’s a natural tendency to worry about people being replaced.

This then leads to other questions: If people are removed from the loop, can we trust AI to handle things? Will the translation quality meet our standards and those of our customers? Should we be worried about AI ‘hallucinations’ in our content?

And the questions don’t stop there.

As AI becomes more prominent, the roles of localization professionals will inevitably shift, but in the majority of cases, this might be the best thing that could happen.

In this article, I’d like to look at how those roles are adapting. More importantly, why the role of humans is so crucial if we’re going to ensure that these technologies are applied ethically, safeguarding both the integrity of translations, and maintaining cultural sensitivity across incredibly diverse and complex markets. Let’s dive in…

The power (and pitfalls) of AI in localization

Efficiency and volume – how AI and LLMs are revolutionizing high-volume translation tasks

Let’s start by thinking about how and why AI and LLMs are being applied currently. One of their main advantages over human translation is the ability to translate very large volumes of content efficiently.

Coupling speed with cost-effectiveness means companies can scale their translation efforts and stick within their budgets. AI also provides a fairly straightforward path to new, often less widely spoken, language pairs, meaning that internationalization is quicker and easier.

Despite these advantages, AI comes with several ethical challenges that need to be addressed so that the systems can be used responsibly:

• Bias in Training Data: Because LLMs utilize such large datasets, any biases in those sets can easily be amplified. In many cases, AI is looking for the most common, repeatable uses of certain terms, so it’s easy to perpetuate bias. This can skew translations, and provide outputs that marginalize or exclude certain groups. Considering the lengths businesses go to ensure cultural sensitivity and relevance, it’s easy to see how this could have a significant impact on brand reputation, and the bottom line. 
• Human Bias in AI Use: With the best will in the world, everyone carries conscious and unconscious biases, and it’s all too easy to introduce these into our AI processes. From inadvertently favoring a particular dataset to interpreting outputs based on our own beliefs or cultural background, none of us are immune. If outputs aren’t scrutinized properly, or are applied inappropriately, AI can easily be misapplied. 
• Bias For and Against AI: Perhaps most importantly here, there is also a notable bias for or against the use of AI in translation. At one end, company leadership may be clamoring for cost reduction, efficiency and scalability. At the other, localization managers might be concerned about their job and their team being sidelined by new technology, leading to slow adoption and a reluctance to apply AI capabilities in certain areas.

As with most business decisions, it’s all about balance. AI is not, and never has been, designed to fully replace human expertise; rather to augment it and allow localization professionals to focus on more strategic, high-impact tasks. 

By addressing these biases and ensuring that AI is used ethically, localization managers can play a vital role in guiding the responsible integration of AI into the industry.

Of course, this is easier said than done. Localization professionals will need to consider which of their existing skills can most readily be employed in AI-led translation processes. 

The ethical imperatives in AI-driven localization

Data quality and bias mitigation

As I mentioned earlier, the quality of training data for any LLM is paramount. High quality, and crucially, highly representative datasets are essential to combat bias. 

For instance, neural machine translation (NMT) systems often struggle with less widely spoken languages due to the limited availability of high-quality data. This can result in errors and mistranslations that misrepresent these languages and their cultural nuances. This has traditionally been particularly prevalent with many African languages. 

Consider for example, that Nigeria alone has more than 500 languages and dialects, each with a high level of cultural and geographical specificity (and if you want to dive deeper, you might also consider the bias I’m displaying by choosing Africa as a  hub of diverse languages).

Culturally sensitive translation is something that is particularly challenging for AI. Context or cultural applications of certain terms change much more frequently than most datasets, leading to translations and applications that might be technically perfect, but could also be inappropriate or even offensive.

As an example, consider a hypothetical situation where AI is being used as part of the content moderation process on a gaming platform.

In the UK, the word ‘Bloody” can be used as a mild curse word for emphasis, but if our user is playing a first-person shooter game, are they actually cursing, or commenting on the amount of pixelated blood that’s spraying around on-screen?

It’s a very minor, and very specific usage, but could well have a compound effect on auto-moderated in-game conversation, blocking content or players for perfectly innocent interactions.

The human element is crucial here, and equipping decision-makers with more culturally aware training processes can make a huge difference.

Culturally diverse businesses often outperform their contemporaries, so it makes sense to build this approach into our AI data as well.

Because we’re talking about a technological problem, we might want to apply a technological solution. In the past, this would have meant “better datasets”, But there is a fundamental difference between earlier neural models and the emerging way in which LLMs are being used.  

Neural models were trained from scratch on large data sets.  So controlling and manipulating these data sets was very important and consequential.  LLMs however,  are “pretrained” by the large organizations that create them, so the end user often has little or no control over what information was used for initial training.  

However, users do now have much richer methods of control over refining outputs, by interacting with them and adding nuanced instructions and prompts. This instruction could include formal or informal language, changing the tone of voice, avoiding certain words, and more.  

This level of control and interactivity is new, and wasn’t possible until quite recently, but again, it very much requires human experts to drive the changes and to evaluate and ensure the results are satisfactory.

Transparency and accountability

The types of AI systems at work in high-volume machine translation can often seem a mystery if you aren’t an expert. If managers and users can’t understand why their AI system is making the choices it makes, they aren’t going to feel comfortable using it – or passing those translations onto users.

Localization professionals are quite right to demand clear, exact explanations from LLM engines and vendors around which datasets and algorithms are being used, and why decisions are being made by these systems.

While training to help understand what is happening ‘under the hood’ can help here, there also needs to be a focus on who trains AI systems, and in particular, how we train them so that they can check and evaluate the output of other AI systems, as well as how users can regularly audit for changes in the levels of bias, and quality rate measurement to spot dips or peaks in accuracy.

If you’re dealing with legal, medical, or indeed any type of personal data, the risks are obviously much higher, and while this is chiefly seen as an engineering issue, any vendors should have clear guidelines around how personal data is anonymized and removed from AI memory. The legal costs of misuse or misapplication can be enormous. 

Cultural and contextual sensitivity

It’s hard to underline this enough. AI translation is efficient, but without very clear guidelines and refinements, it is not subtle. Without careful consideration of context, your translations are going to miss the mark in many ways – and not all of them will be expected. 

The most obvious example of this might be the literal translation of an idiomatic expression:

“It’s still up in the air”. 

Is your project literally on board an airliner right now, or do you simply mean you’re waiting for something to be resolved?

It’s easy to become overly concerned about these things, but on the flipside, getting it right offers huge opportunities to raise engagement and foster loyalty, and this means combining human oversight with large-scale AI powered translation.

(As a delightfully silly example, the French word for “choice” is “Choix”. In the French translation of the Harry Potter books, the famous Sorting Hat is referred to as a “Choixpeau” – a take on “Chapeau”,  the French word for hat. It’s often the smallest examples that showcase why and how a human’s insight and ability to interpret can make all the difference.) 

The evolving role of localization managers

From tactical to strategic

Localization managers are now strategic business assets. Certainly, there’s still the need to coordinate assets and make sure projects are delivered on time, but the bigger picture is all about balancing the gains offered by AI with the need for high quality, consistent and sensitive translations and transcreation.

Localization managers should ideally be looking at ways to use AI to create more efficient workflows and drive scale, but will also need to be sharing that knowledge by setting operational guidelines for its use.

In practice, this means that a specialist localization professional is in charge of keeping humans in the loop. 

In many cases, this means reconfiguring processes and ensuring that translation teams are able to review and optimize AI-generated content regularly.

It also means developing new methods to assess how effectively teams improve or correct flagged content. In this new era, monitoring dashboards and analysis tools become crucial.

Similar to the ways Engineering and Marketing teams work, the modern localization team needs a set of tools and processes that adopt the ‘kaizen’ way of working – a method that consistently looks for ways to optimize both processes and outputs. 

Localization managers as educators

The localization team is now a hub of knowledge, and education is fast becoming one of their primary responsibilities.

While it may seem we’re reaching a saturation point with AI, in practice we are far from it, and knowledge is often very unevenly distributed across the business. Localization managers are perfectly positioned to inform and educate decision-makers on the capabilities and limitations of AI.

This may seem like yet more work being heaped on to localization managers’ plates, but luckily, there are standard approaches that can help here. 

Organizing training programs and ‘lunch and learn’ sessions for internal teams, working with marketing to develop more informative assets for clients, and sharing reporting to help set reasonable, contextual expectations for AI performance, backed up by solid numbers.

This increased engagement with the broader business can also help localization teams emphasize just how important it is to keep human oversight and use it in conjunction with AI to get better results. After all, if you are the ones sharing all the knowledge, how can you be replaced?

Beyond being the go-to source of knowledge, there are also ways localization teams can showcase their value during the initial set-up and ongoing running of AI technologies.

Three ways localization teams can make sure AI is used ethically

Build ethical guidelines

Every new technology needs a set of robust guidelines before rollout, and while a good vendor will have these in place, it pays to pay close attention and plot out clear guidance for areas like data quality, bias reduction, and translation training to accommodate cultural nuance.

Strong frameworks should always include the need for human oversight. While this oversight will need to be more robust for materials and translations where more nuance is required, it’s important to have regular checks and balances in place wherever you employ AI to scale translations. In the past, there have been several high-profile examples of seemingly simple applications going rogue, or being open to interference from outside actors. Always remember that AI is never a ‘set and forget’ solution, but an active and evolving system. 

By setting clear rules, localization managers can ensure that AI supports, rather than undermines, industry ethics.

Ongoing monitoring and refinement 

Speaking of continuous involvement, remember to plan in regular audits of your AI input and outputs. These are absolutely key to spotting and addressing biases and errors that can creep in. As more information is provided to an LLM, so the risk of bias increases, so make sure you are performing regular checks. 

Localization teams can use both AI and human experts to review translation processes regularly. Automated tools can flag issues in AI-generated translations, while human experts fine-tune for accuracy and cultural fit. This approach will also keep improving AI, making it more reliable over time.

Transparency in AI 

Finally, let’s think about transparency in more detail.

From a customer-facing perspective, companies and organizations need to be clear about how and where they use AI, and to be honest about the potential for bias and errors in results. As in other areas of the business, being open about risks and the steps being taken to limit them is a key part of building trust with stakeholders and customers. This also provides an opportunity where the localization team can showcase their expertise in both the benefits and risks of AI—key strategic areas where the team should have a voice.

As an example, including being transparent about where AI is used can help set clear expectations for users. It’s equally important to communicate  internally about which data is used and how it has been prepared before it is used to train AI systems. 

Clean data is an imperative, and while it means more work up front, it will always lead to better results. By focusing on transparency, localization teams can promote a more ethical and responsible use of AI.

Ways Phrase can help address ethical issues

One of the main drivers behind Phrase’s own Quality Performance Scores (QPS) is to make translation quality more transparent. By breaking down how scores are calculated using the MQM 2.0 framework, it gives localization managers greater insight into the inner workings of AI translations, and is an opportunity to spot changes in quality, accuracy and bias.

Similarly, Auto LQA acts like automatic pre-translation, handling the initial quality check so that linguists can focus on refining and validating the results—ultimately saving time and effort.

For dispersed localization teams, centralizing translation tasks through a single platform like Phrase can help promote transparency, helping teams across different locations stay aligned and informed. This visibility reduces errors and miscommunication, ensuring projects remain consistent and high-quality.

Conclusion

The integration of AI and large language models (LLMs) into localization has brought both exciting opportunities and important ethical questions. As AI reshapes high-volume translation tasks, it’s crucial to keep standards high in areas like data quality, transparency, and cultural sensitivity. Localization managers now play a bigger role, moving from day-to-day tasks to overseeing how AI is used responsibly and effectively.

Key ethical issues—like bias in AI training data, the need for human oversight, and the importance of being transparent—are critical for the ethical use of AI in localization. Using AI responsibly means recognizing that it should enhance human expertise, not replace it. AI should be a tool that supports localization, without compromising quality or cultural respect.

Localization managers are leading this change. Their active role in shaping AI practices, educating teams, and setting ethical standards will ensure that AI remains a helpful resource in the industry. By taking these steps, they’ll help create a future where AI-powered localization not only meets the needs of global communication but also respects the diverse cultures and languages involved.

Artificial intelligence (AI) and machine learning (ML) are playing an increasingly pivotal role, enabling more efficient handling of large volumes of text and complex game narratives.

Localization technology also extends to voice recognition and dubbing, offering more immersive and authentic experiences to players worldwide.

By harnessing these technological solutions, developers can significantly reduce time-to-market and overhead costs, ensuring that their games are not only linguistically but also culturally aligned with their target audiences.

This technological edge is what enables games to resonate deeply with a global audience, making them not just playable but relatable across different cultures and languages.

Ultimately, teams need access to solutions that enable them to optimize their processes, control costs, and reduce time to market. In that regard, there are key consideration that need to be addressed within an organization’s tech stack:

• CMS: A strong content management system is essential to enabling strong localization processes and the transparency needed to enable efficient turnarounds.

• TMS: The right translation management system should enable collaboration, multiple layers of review, quick exchange of data, automated set ups and workflows, and more.

• Automation: AI and ML should be leveraged to maximum effect to implement different automated workflows for different types of content. 

Key Areas of Focus for Effective Video Game Localization

Adapting a video game for global audiences isn’t a simple matter of translation, nor is the process limited to the games themselves.

Game localization is about adapting games, and the materials and experiences that surround them, to the culture and language of players in a target market.

Elements addressed during this process include spoken dialogues and instructions, characters, game manuals, marketing assets, and many more. 

When it comes to different types of games (PC vs. mobile vs. console, etc.), different platforms have different localization guidelines and requirements.

This is especially true for console games (with major players including Microsoft, Sony and Nintendo), but it’s also the case with mobile games and related content, such as app store assets.

If content is published on multiple platforms, it frequently needs to be duplicated and adapted.

Likewise, games also undergo regular updates—sometimes even daily in the mobile space—and these quick deliveries require an agile and low-touch continuous localization process.

Regardless of platform, the following game elements typically vary and need to be addressed from region to region: 

• Language
• Cultural and historical references
• Local tastes, preferences and expectation  
• Local legislation, including laws governing sensitive content

Game Localization: A Step-By-Step Guide

While the process of localizing a game and its related materials will vary from project to project, there are key steps that should be included in the process every time. 

• Define requirements: A localization strategy is a long-term plan to ensure your game and its marketing are a fit for your target audiences.

  Out of the gate, you should consider your game genre, your target audience, your target countries and your budget. Concurrently, you’ll want to collect and assess the source content of your game to identify which text and non-text in-game content needs to be translated or adapted.

  Early on, you’ll also want to decide whether localization efforts will take place in parallel to game development or after initial development, and who will be responsible for managing and executing the localization process.

  If it won’t be handled in-house, you’ll need to decide which partners and related technology are best suited to meeting your needs. 

• Prepare your code: Preparing your game code for localization will save you time and effort during the rest of the localization process. Game text should be separated from the codebase, so it can be easily translated.

• Create localization documentation: Localization documentation conveys the guidelines of your strategy to your translators and your broader localization team, including multiple linguistics roles and localization testers.

  In formulating this documentation, be sure to include relevant information about your game’s story, characters, tone, design, key demographics and other details. A localization kit will minimize questions from translators and localization QA testers throughout the process

• Translate localized assets: This step can be a lengthy process and often requires a number of review cycles between the localization and development teams.

  During the translation process, translators should use your localization kit to remain consistent with the tone of your game. This part of the process also involves translating audio content, such as dialogue between characters and subtitles. 

• Assemble and test: Finally, your team should pull all adapted parts back into the game and have native-speaking game testers review the localized versions of your game. Allocating time for this step is important.

When taking a video game global, the stakes are high—but a strong translation and localization strategy can minimize risks and help games be immediately well-received in new markets.

To ensure success, be sure to take localization into consideration as early as possible and lay the groundwork for strong communication and smooth workflows.

Get it right, and you’re not just another game in the app store; you’re the game that “gets” your players. And that’s a win.

Case Study: Strategic Localization of Gaming in India

A case study from Nimdzi highlights that a strategic focus on localization is critical for penetrating different markets, creating engaging user experiences, and fueling strategic growth.

A leading Finnish game developer that operates globally with five games in over 100 countries and over a million players wanted to localize its existing game offering for the Indian market. 

The Indian gaming industry is unique due to its rich cultural and language diversity; success in this market requires hyper-localization.

Translation into Hindi is merely the starting point, given that India is home to various languages, cultures, and dialects. Hyper-localization that takes into consideration local culture, festivals, and customers is also paramount for creating engaging customer experiences that resonate with the local audience. 

The Finland-based game developer has an existing portfolio in English but aims to foster deeper connections in India to expand its existing user base.

The initial focus was on localization in Hindi, but the range of different languages, cultures, and local dialects introduced additional complexity. 

Initially, Nimdzi undertook extensive research, including benchmarking user behavior to analyze engagement.

Comprehensive analysis enabled the game developer to understand the nuances of audience behavior and how it varies across different regions and groups.

The insights enabled the gaming company to develop its content and marketing strategy in response to user demand, a valuable input into core strategic decision-making.

A comprehensive, research-led strategy meant the organization was better equipped to localize and customize its approach for different Hindi markets.

Of the multiple benefits, it is noteworthy that a boost in CX results in a growth in performance on the bottom line.

The campaign resulted in deeper user engagement, better market penetration, and higher revenue streams. 

Measuring the Impact of Localization

A basic approach to localization recognizes the fluency, accuracy, and appropriateness of translated content.

However, this does not go far enough in terms of gauging success. Organizations must look at the bigger picture and measure success through customer-centric metrics. 

A strategic approach to localization scrutinizes the gains in efficiency, scalability, and overall improvement in customer engagement.

The effectiveness must be results-driven, with the impact measured through metrics such as a boost in customer engagement, a higher conversion rate, and an uplift in customer loyalty and retention. 

The KPIs will depend on organizational goals and objectives, but some of the strategic metrics to measure success include: 

• Time to market – boosting the speed and efficiency of localization enables companies to remain competitive and agile, minimizing time to market to outpace competitors. 
• Customer engagement – organizations will have their own set of brand-specific KPIs, but the level of interaction with the intended audience is a good indicator of success. 
• Search engine performance – depending on the target market, localization can enable brands to optimize the visibility of their website and increase SERP ranking across different regions. 
• Customer loyalty/satisfaction – in the long term, companies should measure how customer satisfaction varies globally, including loyalty, acquisition, and retention across different regions. 

The bottom line is that effective localization is about achieving core business goals and creating content that resonates with your target audience. Organizations must employ a multi-faceted strategic approach by integrating localization into their CX strategy.

Companies focusing on prioritizing localization will reap the benefits of faster time-to-market and greater user engagement and stay ahead of their competitors. 

Key challenges in game localization

A video game’s localization often poses some challenges. To help you anticipate these pitfalls and plan accordingly, here are some  of the usual hurdles.

• Inefficient procedures and lack of understanding
  The process is challenging to streamline because of constant back-and-forth between stakeholders. For example, game developers have to answer a large number of questions—often leading to communication delays. Plus, translators often don’t understand the context well, causing translation mistakes.

• Acquiring and retaining talent
  Finding and retaining talent is especially important in the context of mobile games. Unlike console games, which represent a finalized product, mobile game development is an ongoing process. To sustain a consistent level of quality, it’s crucial to keep a dedicated group of linguists on board.

• Cutting language quality assurance (LQA) time short
  As the LQA process consists of checking the translations for mistakes, it happens at the end—right before the release. That’s when the pressure on the release is the highest. Cutting the LQA time is risky as it causes mistakes in the localized game.

• Little consideration for localization
  It’s common that companies see localization as an afterthought compared to the design and development of the game. Therefore, you might be tempted to buy game localization services at low rates. This would be a risky choice as the video game localizer might not accurately translate. This is a particularly sensitive point in game localization because there are usually a lot of technical terms in video games. Plus, if there are delays during game design and development, it might seem like an easy solution to cut the time allocated to localization.

• Timing
  You can implement game localization parallel to the general game development, or after. Both options have pros and cons. On the one hand, localizing during game development takes a lot of time and resources, and requires certainty about the markets to target. Moreover, it might be inefficient if game development takes place in a fast-paced and stressful environment.
  On the other hand, it’s a good approach if your game is released on an international scale. Companies that have limited financial resources often choose the option to localize after the initial game release. However, it might be more difficult to adapt a fully finished and released game because it’s not possible to localize some of its parts.
  

Addressing Challenges

Overcoming challenges such as timing, talent acquisition, consideration for localization and quality assurance is key to successful game localization. Starting localization early avoids it being an afterthought and tackles potential code or user interface issues. Acquiring and retaining the right talent, especially those with a passion for language and openness to feedback, is crucial. Implementing efficient query management strategies, like direct stakeholder communication and using platforms for discussion, streamlines the process.

Best practices for quality game localization

Game localization is a critical element that ensures your video game resonates with players across different cultures. By implementing best practices and addressing common challenges early on, developers can significantly enhance the quality and appeal of their games globally.

• Incorporate Localization Strategy Early
  To achieve high-quality game localization, it’s essential to plan localization from the beginning of the game development process. This involves separating content from code and making your game adaptable to longer character lengths, various fonts, and formats. Early planning also facilitates stakeholder involvement and allows for the inclusion of technical requirements that accommodate different languages and cultural nuances.

• Professional Linguists and Team Involvement
  Utilizing professional linguists who may not necessarily have a background in gaming but understand your game and are committed to the project is crucial. Involvement in game testing and feedback is encouraged to ensure high-quality translations. Building a strong team atmosphere, whether through in-house teams, freelancers, or language service providers, supports quality localization. It’s important that all team members, including developers, artists, and marketers, understand the localization workflow and their role within it.

• Open Communication and Query Management
  Simplifying the communication process by allowing stakeholders to directly interact with each other can reduce response times to linguists’ queries and make the localization process more efficient. Integrating query management into localization technology platforms helps manage queries effectively, ensuring they become a routine part of the workflow. Direct communication among developers, in-house translators, and external providers can optimize this process.

• Leverage Technology
  Next generation localization technologies, such as the AI-powered Phrase Localization Platform, play a pivotal role. These technologies offer solutions for managing localization projects, tracking translation progress, and controlling localization costs. Features like translation vendor management and workflow automation capabilities are instrumental in adapting video games quickly and effortlessly.

Effective game localization enhances a game’s global appeal and player experience. It should be an integral part of the game strategy from the start, given equal importance as graphics, sound, and gameplay. By addressing the challenges of timing, talent, and query management, and leveraging modern technologies, developers can ensure that their game localization efforts are not just an afterthought but a cornerstone of their game’s success.

Designed with the whole team in mind, the Phrase Localization Platform can make your game localization painless from start to finish. The complete localization solution is able to seamlessly integrate with the continuous development process of agile development teams. Among other game localization features, Phrase offers translation vendor management and workflow automation capabilities to make it simpler for teams of any size to adapt video games quickly and effortlessly. 

Strategic planning is the key to effective game localization 

When it comes to taking a video game global, the stakes are high due to the rapid rise of the global video game industry. Customizing video games for local markets is often necessary due to the numerous local specificities.

Since game localization can improve the game adoption by local players, it often brings an increase in revenue and in the number of long-term customers. However, it can become a complicated process with many stakeholders involved.

To make the process a success, it’s essential to take localization into consideration as early as possible and ensure direct and quick communication between everyone involved. In that regard, relying on modern localization technology, like the Phrase Localization Platform, can help you make the most of game localization in the long run.

The setup

After installing fastlane, with the help of the setup instructions, we’re ready for some automation. Let’s open our project folder from the command line and start fastlane with the following command:

The command presents a prompt that asks us how we will start using fastlane. Let’s choose option 3, Automate App Store distribution, for this tutorial.

🗒️ Note » Choosing one option doesn’t lock us in. We can always add more automation tasks later.

Fastlane will present a series of prompts about our developer account and app details. Once we’re done answering all the prompts, we should see some new files that fastlane generated: A fastlane folder containing all we need to automate our App Store listing. Notice that fastlane localizes store data by default, placing stubs in our app’s base language, e.g. under fastlane/metadata/en-US.

If we don’t see the fastlane/metadata folder, we can use the following command to generate it:

Localizing App Store details

The fastlane subfolders and file names are self-explanatory and descriptive. They often map one-to-one with the info we see in App Store Connect. For example, fastlane’s first_name.txt file corresponds to the app contact’s First name field in App Store Connect. We will highlight all the mandatory files that need to be populated before fastlane can do its job.

🗒️ Note » We will use the term “lane” moving forward. A lane is a set of actions you want fastlane to take. Without a lane, fastlane can’t work so always make sure there is at least one lane. The generated Fastfile comes with a lane already provided and that is what we’ll be using throughout this tutorial.

• Appfile: stores useful information that is used across all fastlane tools, like our Apple ID or the application Bundle Identifier. This info is used to deploy our lanes faster and is tailored to our project needs. [Official Appfile documentation]
• Fastfile: stores the automation configuration that is used to run fastlane. [Official Fastfile documentation].
• copyright.txt: the copyright of the app e.g. Phrase inc @ 2023
• primary_category.txt: the primary category of our application e.g. LIFESTYLE
• review_information: found in the metadata folder, its information Apple’s testers will use to access our application. The required files in the review information related to your team’s point of contact for testers: email_address.txt, frist_name.txt, last_name.txt, and phone_number.txt. Fastlane requires that these files have data before you can push updates to the App Store.
• name.txt: the name of our app.
• subtitle.txt: the subtitle of our application. This is normally a tagline or something short to describe our application.

🗒️ Note » Keep in mind that name.txt and subtitle.txt have a thirty (30) character limit each.

Let’s update the required fastlane files:

🗒️ Note » The initial files generated are based on the default language of our App Store listing, which is en-US in this case.

With the required metadata entered, let’s run the deliver command to upload this data to our App Store page:

This should use the info in fastlane/metadata to update our App Store listing.

🗒️ Note » The command has other important features that we won’t cover in the scope of this tutorial. Check out the documentation to learn everything fastlane deliver has to offer.

Adding other locales

There are 2 main ways to add locales when working with fastlane. We can duplicate the metadata folder of our base language, e.g. en-US, and rename it to the locale we want to support, e.g. fr-FR for French – France. After that, we can enter French translations in the new fr-FR folder. The next time we run fastlane deliver, fastlane will automatically add the locale to our supported locales in our App Store listing.

Alternatively, we can add another locale in App Store Connect and run the fastlane deliver download_metadata command. This will create all the metadata in our local project for all supported locales. We can then edit the localized files and run fastlane deliver to upload our changes.

An example of localizing subtitles for English and French would be:

Automating screenshot localization

Manually taking screenshots and uploading them individually for all locales can be a pain. Fastlane has a screenshot automation tool called snapshot to make this a lot easier. Screenshots are generated by integrating fastlane into the automated UI tests of our project. It takes a few steps to set up, but once it’s done, it’s smooth sailing.

Setting up UI tests

The backbone of automating screenshots is using UI Tests. You can skip this section if you already have UI Tests set up for your project. Fastlane will take snapshots of our UI tests and use that as screenshots of your App Store listing. We will create a UI Test target that will be used to run our UI tests.

The first step is to create a UI Test Target. Click File ➞ New ➞ Target.

In the Template sheet that appears, search “UI Test” ➞ Select UI Testing Bundle ➞ Click the Next button.

On the Create Target sheet that appears make any edits if necessary and click the Finish button.

If all went well, Xcode will create a folder with two files using the Product Name from the Create Target sheet.

Next, we will verify that our target is ready to run our tests. For our target to be able to run UI Tests, it has to be present in the Test scheme. Click Product ➞ Scheme ➞ Edit Scheme.

Select Test on the left panel. This will show relevant information related to testing. The target we created should be shown and should be checked ☑️.

All set! Now we can confidently run our UI tests. In the newly created folder with our UI Tests, select the one that ends in UITests, in our case Motivate_MeUITests.

To run the UI Test, click the Run icon next to a function, or a class to run all its test functions. After a successful test, a green checkmark ✅ will appear next to each passed test.

Setting up snapshot

With our UI tests properly set up, we can introduce fastlane’s snapshot to help us with generating localized screenshots.

Run fastlane snapshot init in your project folder. This command will create 2 files under the .fastlane folder, Snapfile and SnapshotHelper.swift. The Snapfile configures configuration snapshot. SnapshotHelper has functions needed for fastlane to capture screenshots during testing.

Now you need to add the newly created SnapshotHelper.swift to your UI Test target. Click on the UI Test Target folder, in our case Motivate MeUITests ➞ Click Add files to “Project Name..”. On the sheet that appears, locate the SnapshotHelper.swift and select it. Check UI Test target if it’s not checked ➞ click Add. The SnapshotHelper.swift should be visible in your UI Test Target folder.

🗒️ Note » (Objective C only) Add the bridging header to your test class. The bridging header is named after your test target with -Swift.h appended.

The most important function for capturing screenshots for fastlane is snapshot(IndexNameOfScreen). It’s what tells fastlane to take a screenshot of the current state of our UI test. Here are the signatures for Swift and Objective C:

• Swift: snapshot("01YourScreen")
• Objective C: [Snapshot snapshot:@"01YourScreen" timeWaitingForIdle:10];

Let’s update our test to make use of snapshot. In the code below we take a snapshot every time a button is pressed and this tells fastlane when to capture a screenshot.

🗒️ Note » snapshot(IndexNameOfScreen) is the recommended naming convention to follow; it makes it easy to identify screenshots.

Click the Run icon next to the test function(i.e testGenerateRandomQuote() in our case), or the Run icon next to the class(i.e Motivate_MeUITests in our case) to run all its test functions. After a successful test, a green checkmark ✅ will appear next to each passed test.

Remember our Motivate Me application? This is what we’ll be using to demonstrate screenshot automation.

Generating screenshots

In our fastlane folder, we should notice there has been an additional file added.

🗒️ Note » If you don’t see this file, run fastlane snapshot init.

The Snapfile has configuration details for snapshot capturing. Here is the sample Snapfile for our test application:

The most important things here are the devices to generate the screenshots on, the locales supported, and the scheme used to run the tests. Devices are particularly important because the generated images should match Apple’s resolution guidelines. You can use this guideline to help you choose.

🗒️ Note » There’s a lot more fastlane can do with screenshots, such as framing them. Visit fastlane’s documentation on snapshot to learn more.

Run this command to generate snapshots:

You’ll have to wait for a while for fastlane to do its magic. After everything is successful, fastlane will open a report of all the generated screenshots. You should also see a screenshots folder at the location specified in your Snapfile.

Deploying to the App Store

Now that we have all we need to fully localize our App Store, it’s time to deploy our changes.

In our Fastfile, we should add the action to capture and upload our generated screenshots in our lane.

Run the deliver command to upload your screenshots and App Store details.

After completion, you should see a screen similar to this. Fastlane does a good job of pointing out errors in case you have any.

And if you visit App Store Connect, you should see the screenshots fastlane uploaded.

🔗 Resource » fastlane deliver updates your App Store details so you can submit them for review if everything looks right. Fastlane can also deploy to your application and even submit it for review. Read all about fastlane deployment in the official documentation.

Well done!

We hope that this tutorial has shown you that localizing your App Store page is not too hard, and for all super-optimizers, we hope our coverage of fastlane has shown you a good way to automate the process. With a localized App Store page, you can increase app downloads—resulting in greater revenue.

To make the most of it, consider streamlining your app translation process with a specialized software localization tool such as Phrase Strings. Designed to automate manual and repetitive tasks, it can help you efficiently manage translation strings for your mobile app and continually release updated translations over the air.

With a strong API and native integrations with GitHub, GitLab, Bitbucket, and other tools, Phrase Strings provides a comprehensive strings editor that enables translators to access all the content you’ve pushed for translation from one location. Once the translation is complete, you can effortlessly retrieve the translated content and incorporate it back into your project.

As your software product grows, you can seamlessly connect Phrase Strings with a cutting-edge translation management system (TMS) to unlock the full power of traditional CAT tools and machine translation capabilities.

Check out all Phrase features for developers and see for yourself how they can streamline your software localization workflows from day one.