Unlocking Content Localization With AI Automation and Metric Sharing

For global organizations, the ability to keep pace with content localization needs will emerge as a key competitive differentiator in the coming years. Success in this area will require a new generation of AI-driven automation, as well as improved transparency and communication within organizations. Let’s take a closer look at this growing need and how recent developments in AI, along with organizational changes, will deliver the solution.

Challenges and Opportunities

Here’s the reality today’s enterprises are facing when it comes to content:

  • Not all content is born equal, and smart routing must be a part of every mature pipeline.
  • Automation is key to handling the ever-growing backlog of content while keeping costs under control.
  • Quality must be measured at multiple stages of the localization process, and it can’t rely on humans alone.

It’s one thing to acknowledge the challenges that content proliferation poses to localization teams. But what’s perhaps more important is to acknowledge the business opportunities being lost when organizations can’t manage this proliferation and don’t communicate with customers and prospects in their own language.

Research conducted by Nimdzi Insights found that having an online storefront in their own language can seal the deal for those 70 percent undecided or unwilling to spend, doubling a business’ target market size. Furthermore, 90 percent of survey respondents confirmed they would be more interested in making a purchase if the product was offered in their language. That’s a tremendous amount of money being left on the table.

At the same time, there’s the issue of quality. When a company suddenly needs 45 versions of 25 different landing pages, how can it ensure those landing pages are hitting the quality thresholds needed to deliver on their purpose? The answer lies in emerging automation capabilities.

Quality Performance Scores in Automated Workflows

Within an ideal localization process (see Figure 1), a unified quality framework would provide a quality target that is reflected at all stages of the journey, including human review. Ultimately, such a system would require minimal manual intervention; all the small issues would be fixed through AI, while humans would bring their value to the most critical pieces.

Content localization workflow

Figure 1. An example workflow automation that heavily optimizes content sent to humans for review, and provides active feedback loops for continuous improvement.

Content Tiering

A big part of forthcoming automation capabilities should be directed toward content tiering that acknowledges two key dimensions of any given asset:

  • Shelf life: How long is a piece of content expected to live? Will it continue to be useful and viewable for years to come, or is it a fleeting post expected to disappear within days or weeks?
  • Impact: How impactful is the piece of content? How significant of a role does it play in driving business outcomes and revenue?

Ultimately, organizations aim to allocate the majority of their time and resources to content that will significantly impact their business success. Therefore, future localization solutions must incorporate features for identifying and making decisions about such content within their automated workflows.

LLMs can be integrated throughout the localization pipeline to identify, match, and route assets within the system. At each checkpoint, thresholds to determine content routing could be configured to reflect tolerances of a particular use case, based on the asset’s shelf life and importance to the organization.

Quality Assurance

How do we measure whether our efforts toward incrementally optimized workflows are paying off? We need to work toward a metric-sharing ecosystem. It’s crucial to ensure that content is not only accurate, but also fulfills its intended purpose. This requires a consistent focus on the ultimate objective and the needs of the user in every aspect of our work.

Too often, localization teams think of “ensuring quality” as fixing errors. But “quality” is really about whether a given piece of content is driving the desired action or result with the user. That is the lens that the localization tools of the future need to apply when conducting automated quality checks at the various stages of the journey.

For example, in marketing, A/B testing is a key strategy, and it starts with comparing localized content against non-localized versions to understand customer preferences. By introducing multiple content versions, organizations can identify the specific elements that drive customer engagement, enhancing their marketing effectiveness. In user support, a similar approach is used. This significantly affects user comprehension and reduces support inquiries, particularly regarding feature usage. Localized support material leads to better user experiences and more efficient support operations, as it increases comprehension and reduces ambiguity.

Additional metrics influenced by localization, which warrant close monitoring, include market penetration and growth. For instance, tracking market share and sales growth can offer valuable insights into the effectiveness of localization strategies. Moreover, in multinational companies, the quality of localization in internal communications plays a significant role in determining employee satisfaction within global teams.

Metric Sharing

My recommendation to everyone on the quest to understand the “fit for purpose” of localization is to start by sharing metrics and results across the organization now. We all collect data and evaluate performance, but it’s essential to ensure that both colleagues and vendors are aware of and fully comprehend the bigger picture and implications of their work.

For non-localization teams dealing with localized content, maintaining transparency is key. I recommend openly sharing metrics with extended localization teams, including vendors, to highlight the tangible impact of their contributions within the content lifecycle metrics. This approach fosters a deeper understanding of how localization influences overall business objectives, such as user engagement, market penetration, and customer satisfaction.

Similarly, localization teams should proactively seek information on how their clients, both internal and external, define success. The goal is not merely to adhere to standard localization metrics, but to integrate them with factors that matter to buyers and users. By developing frameworks that merge typical localization metrics with these wider business objectives, we can create a more cohesive and effective strategy.

This enhanced metric-sharing ecosystem fosters a culture of continuous improvement and innovation. Especially when fed back into your automated localization pipeline, they provide contextual guidance for future content. This transforms the localization process from an isolated task to a fundamental, strategic component contributing to the organization’s global narrative of success.

Andrea Tabacchi
Andrea Tabacchi is Chief Customer Officer at Phrase.

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