Improving LQE, Quality Data Collection, Analysis, and Refinement with the Chillistore Language Ownership Program

Just how bad can the results from flawed or partial data analysis be? Let’s start with a history lesson.

The objective data for one of the most infamous business decisions in history was, in fact, conclusive: Back in 1985, Coca-Cola’s recently reformulated recipe was the hands-down winner of nearly 200,000 blind taste tests, beating both Pepsi and the original Coca-Cola formula.

Assured by the analysis of this objectively secured data, Coca-Cola pushed forward with a splashy and ambitious advertising campaign to introduce America to New Coke.

And it flopped.

As the executives at Coca-Cola pondered where they went wrong, they realized something: They had missed a vital piece of data collection – the cultural attachment customers had to the idea of the original Coke flavor. It had been such a part of their memories of good times and experiences that even if the new formula tasted better objectively, the “classic” flavor meant more emotionally.

Had both sets of data been collected, the analysis would have yielded a far different recommendation.

Bringing Teams Together to Collect and Analyze All the Relevant Data Points

Just as the above story illustrates, the best results come from thorough collection and analysis of all the relevant data. This is why Chillistore’s Language Ownership Program puts special emphasis on this process to yield the most insightful LQE results: Collecting and analyzing relevant data from query management systems and outlining it back in the form of actionable reports and dashboards gives you objective building blocks to continuously improve quality.

The key here is working end-to-end with you (the customer), translators, and other stakeholders to help each team see how this collaborative approach optimizes translation workflows and ensures linguistic accuracy and contextual relevance.

Once localization teams see we can quantify the quality of translation efforts they begin to see LQE not as a pass/fail step but as a tool for constant improvement.

Data Collection and Analysis: The Dynamic Duo for your LQE Program

Analysis is impossible without first collecting multifaceted data sets that take all the areas of LQE into consideration. This encompasses the source content, translation memory, glossaries, and existing style guides. As the collaboration progresses, accumulated data forms a robust foundation – a well-structured database of quality potential, if you will.

However, the true power of this partnership emerges when the collected data undergoes a battery of data analytics. Leveraging modern analytical tools, we extract relevant insights from the amassed information. In-depth data analytics is used to build dashboards for fast, easy data visualizations. These insights are translated into actionable strategies.

Emphasizing Collaboration at Every Step

We sit down with stakeholders at each stage of the project to gather the data needed for thorough LQE.

1. SME Review Yields Industry-Infused Data: Regular meetings with your Subject Matter Experts (SMEs) build a feedback pathway with the translation team so data can flow both ways. SMEs bring product usage data, feedback from end-users, and market surveys to the table to inform the goals of the translation. This symbiotic relationship yields a treasure trove of industry-specific terminology, cultural nuances, and more so that translations resonate authentically with the target audience.
2. In-Context Review Provides Market-relevant Insights: The collaboration mindset strengthens and improves context reviews as it begins with an already enriched data pool. Having a true understanding of the intended contexts through the medium, demographics, and desired emotional tone means the translated content can be evaluated against this standard to resonate with users on a deeper level.
3. Linguistic Testing and Analysis Perfects Formatting and Data Alignment: Because more information has been gathered on intent and market data, our linguistic testing is evaluating the translated content within the native environment. This ensures that not only linguistic nuances but also formatting and design align with the target language. This step seamlessly integrates design-related data collection and analysis, resulting in a visually appealing and linguistically accurate end product.
4. Final Verification and Iterative Refinement Closes the Loop on Feedback: The collaborative partnership culminates in the final verification stage where data analysis drives inspection of grammar, punctuation, and language mechanics. Any findings contribute to iterative refinement and a truly thorough LQE.

Building Responsive Feedback for a Collaborative Landscape

While it might sound complicated or purely technical, the real magic of a Language Ownership Program is human-to-human partnership. This approach captures insights from every perspective to enrich the data analysis process and build constant refinement:

• Feedback flows in various formats, enabling a dynamic and adaptive approach to translation.
• Reports and dashboards generated from data analysis serve as guiding documents during discussions.
• Feedback is structured through the Client’s systems, facilitating direct input on specific segments.

This structured approach to language ownership guarantees feedback is grounded in the very data that drives the translation process.

And we would, of course, love to put Chillistore Language Ownership Program to work for you.