Many localization leaders fall into the trap of assuming that a new workflow or technology is successful simply because the team has adopted it. However, implementation is not the same as impact. Without a structured validation framework, it is impossible to accurately measure whether process changes actually reduced errors or merely shifted them to a different stage of production.
Key takeaways
- Objective baselining is mandatory. You cannot validate process improvements without a stable, historical data set derived from centralized management tools.
- Prioritize efficiency with TTE. Time to Edit (TTE) serves as the most reliable indicator of how well your machine translation engine and linguists are working together.
- Isolate one variable at a time. To accurately measure impact, testing must control for content type and language pair, ensuring that results are directly attributable to a specific process change.
- Failures drive future optimization. When historical analytics indicate a change has not worked, use EPT and TTE metrics to diagnose the bottleneck and refine your data curation strategy.
Why “We Made a Change” Isn’t the Same as “It Worked”
Proving that a process change actually worked requires moving from subjective impressions to objective, quantifiable data. When teams report that a new engine “feels better” or that a workflow “seems faster,” they are offering perceptions, not proof. To build a scalable localization engine, you must anchor your strategy in metrics that demonstrate a measurable reduction in effort and an increase in accuracy.
The goal is to move toward a model of human-AI symbiosis where every adjustment is verified against established benchmarks. This data-driven approach ensures that your resources are focused on optimizations that deliver genuine ROI, rather than chasing marginal gains based on temporary trends.
Setting a baseline before you change anything
You cannot measure progress if you do not know where you started. Establishing a rigorous baseline is the most critical step in any quality improvement initiative. This baseline must consist of historical data that reflects your typical performance levels across specific language pairs, content types, and turnaround times.
Defining your core quality metrics
To quantify the success of a process change, you need a balanced set of KPIs that address both efficiency and accuracy. At Translated, we prioritize Time to Edit (TTE) as the primary standard for quality. TTE measures the average time, in seconds, a professional linguist spends refining a machine-translated segment to reach human-grade quality.
In addition to TTE, we use Errors Per Thousand (EPT) to track linguistic precision. EPT represents the number of errors identified in a linguistic quality evaluation for every 1,000 words. Together, these metrics provide a comprehensive view of how effectively your AI tools and human experts are working in tandem.
Using TranslationOS for centralized visibility
Capturing this data consistently across global projects requires a centralized management hub. TranslationOS serves as this foundational ecosystem, synchronizing global assets to prevent brand drift and providing a single source of truth for quality analytics. By centralizing your workflows, you can automatically aggregate the metrics needed to build a reliable performance baseline.
Unlike fragmented toolsets, TranslationOS allows you to view historical EPT and TTE data in one place. This visibility is essential for identifying which projects are meeting quality standards and which require intervention. Without this centralized oversight, your baseline data will remain scattered, making it impossible to perform a meaningful comparison after a change is implemented.
Isolating the effect of one change from other factors
The biggest challenge in localization testing is the sheer number of variables involved. If you update your translation engine while simultaneously changing your review guidelines, you will never know which adjustment drove the result. True validation requires a disciplined approach where only one variable is altered at a time.
Controlling for content type and language pair
Localization performance varies significantly depending on the complexity of the source text. A change that improves the quality of technical manuals might have no impact on creative marketing copy. To ensure your results are valid, you must test process changes using consistent content buckets and language pairs.
Comparing “apples to apples” means using similar document types with comparable difficulty levels. If you notice a drop in EPT after a change, verify that the content was not simply easier than previous batches. By maintaining strict control over your test sets, you ensure that any fluctuations in quality are directly attributable to the process change itself.
Leveraging Lara for consistent output baseline
Using a purpose-built translation engine is another way to minimize noise in your data. Lara is an LLM-based translation service designed to understand full-document context, delivering faster and more accurate outcomes than generic models. Because Lara is optimized specifically for professional localization, it provides a highly stable foundation for testing.
When you use Lara as your core translation engine, you eliminate the volatility often associated with generic AI tools. This stability allows you to measure how external process changes, such as new quality gates or updated glossaries, affect the final output. By starting with a contextually accurate baseline, you can more precisely identify the ROI of your process optimizations.
How long to wait before judging the result
Patience is a prerequisite for reliable data. In high-volume localization environments, it is tempting to judge a change after just a few days. However, early results are often skewed by outliers or small sample sizes. To achieve statistical significance, you must allow enough time for a diverse range of content to pass through the new process.
Most teams find that a window of four to six weeks is necessary to capture a representative data set. This timeframe allows you to account for different project types and individual translator performance. As seen in our work with global brands like Cricut, focusing on long-term efficiency gains rather than short-term spikes leads to more sustainable process improvements. By waiting for the data to stabilize, you can be confident that the improvements in TTE or EPT are genuine and repeatable.
What to do when the data says the change didn’t help
Not every process change will be a success, and that is a valuable outcome in itself. When your baseline metrics indicate that a change failed to reduce EPT or improve TTE, it provides a clear signal to revert or refine. This objective feedback loop prevents your team from becoming tethered to ineffective workflows simply because of the time already invested in them.
A “negative” result often points toward a need for better data curation or more specific training. Instead of abandoning the goal, use the metrics to pinpoint exactly where the process failed. This iterative, data-centric approach is what allows enterprises like Asana to maintain exceptional linguistic quality at scale. Every failed test is an opportunity to learn more about your specific content requirements and to refine your strategy for the next optimization cycle.
Conclusion: Don’t settle for generic. Demand an enterprise-grade solution.
Successfully measuring process changes requires a commitment to technical precision and professional oversight. By anchoring your localization strategy in measurable metrics like TTE and EPT, you transform quality management from a guessing game into a strategic business function.
A truly effective localization engine is built on the symbiosis of human expertise and purpose-built technology. Generic tools cannot provide the depth of insight needed to drive continuous improvement. To achieve global reach without sacrificing nuance, you need a centralized ecosystem like TranslationOS and a context-aware engine like Lara. These tools don’t just perform tasks, they provide the data you need to prove that your localization strategy is actually working.
Engage an experienced strategic partner for localization with the right technology-and-resources stack and the metrics to prove success. Start the conversation with Translated today.
Frequently asked questions
What is the difference between EPT and TTE?
Errors Per Thousand (EPT) is a linguistic accuracy metric that counts specific errors in a translated sample, whereas Time to Edit (TTE) measures the efficiency of the translation process. TTE represents the time a professional editor takes to bring a machine-translated segment to human quality. While EPT tells you how many mistakes were made, TTE tells you how much effort was required to fix them, making it a superior metric for measuring localization ROI.
Why does TranslationOS not display TTE directly to users?
TranslationOS is an AI-first localization platform designed for workflow management and global asset synchronization. While it captures the data necessary to calculate productivity and quality metrics, TTE is primarily used as an internal and professional standard to optimize the performance of engines like Lara. TranslationOS focuses on providing visibility into project status, spend, and linguistic quality through aggregated analytics.
How much content is needed for a valid test?
For a process change to be statistically significant, we recommend testing a minimum volume of 10,000 to 20,000 words across a specific language pair and content type. This volume ensures that the results are not skewed by a single difficult document or an exceptionally fast editor. Smaller samples can be used for initial pilots, but they should be validated against larger batches before making a permanent change to your localization strategy.
How does Lara help in reducing translation errors?
Lara is an LLM-based translation service that leverages full-document context to produce more accurate and fluent translations than traditional neural machine translation (MT) engines. By understanding the relationships between sentences and adhering to professional standards, Lara reduces the number of errors that reach the editing stage. This naturally leads to lower EPT scores and significantly reduced TTE for professional linguists.
Can I use these metrics for creative transcreation?
While EPT and TTE are highly effective for technical, legal, and financial content, creative transcreation often requires a more qualitative assessment. Because transcreation involves significant cultural adaptation and stylistic choices, the “time to edit” may not be a fair reflection of quality. For creative projects, we recommend combining these quantitative metrics with human-led brand voice evaluations.
