The challenge of measuring quality in high-volume publishing
Publishing content on a daily basis is a logistical and linguistic challenge that tests the limits of traditional localization workflows. Media outlets, e-commerce platforms, and customer support centers operate in an environment where content perishability is high. A news article or a flash sale description can lose value quickly when it sits in a translation queue. However, the pressure to publish immediately often conflicts directly with the need for linguistic precision and brand consistency.
For organizations managing high-volume streams, the definition of “quality” shifts from a static target to a dynamic balance. It is no longer enough to simply ask if a translation is correct. You should ask if the translation was produced efficiently enough to be relevant and accurate enough to protect the brand. While speed is the most visible metric in this equation, relying on it exclusively is dangerous. Without a robust framework to measure linguistic accuracy and cultural relevance, rapid publishing can lead to scalable errors that damage audience trust.
To navigate this environment, localization managers need a composite view of performance. This involves integrating efficiency metrics with deep quality indicators. By tracking data points such as Time to Edit (TTE) and Errors Per Thousand (EPT), businesses can move beyond subjective reviews and build a more predictable, data-driven process for their global content.
Key translation performance metrics you should track
Metrics serve as the control panel for your localization engine. When publishing daily, relying on gut feeling or sporadic feedback does not scale. You need real-time data to understand if your workflow is healthy or if bottlenecks are forming. The following metrics are essential for maintaining the delicate equilibrium between velocity and quality.
Efficiency metrics: measuring speed and workflow health
Turnaround Time (TAT)
Turnaround Time (TAT) measures the total elapsed time as defined by the organization’s workflow, from the moment a translation request is authorized to final delivery. In daily publishing, TAT is often the primary KPI because it directly correlates with time-to-market.
However, treating TAT as a standalone metric can be misleading. A short TAT achieved by skipping quality assurance steps is a liability, not a success. To optimize TAT effectively, organizations should analyze the waiting times between steps rather than just the translation speed itself. Bottlenecks often occur during file handoffs, vendor assignment, or the approval phase. Advanced platforms like TranslationOS address this by automating the administrative friction, allowing the actual translation work to start sooner by reducing administrative friction. This reduces the overall TAT without forcing translators to rush through their linguistic work.
Time to Edit (TTE)
Time to Edit (TTE) is a leading KPI for measuring machine translation quality and human efficiency. It quantifies the average time, in seconds, that a professional human translator spends editing a machine-translated segment to bring it up to human quality.
Unlike purely mechanical scores, TTE captures the cognitive effort required by the linguist. A low TTE indicates that the underlying AI, such as Lara, provides high-quality initial output that requires minimal intervention. A high TTE signals that the machine translation is struggling with context or terminology, forcing the human to rewrite rather than edit. For daily content, tracking TTE is crucial because it predicts cost and capacity. If TTE decreases over time, it proves that your adaptive AI models are learning from human feedback, leading to faster throughput and lower costs in the long run.
Quality metrics: safeguarding accuracy and brand voice
Errors Per Thousand (EPT)
Errors Per Thousand (EPT) is a widely used quality metric used to benchmark linguistic accuracy. It calculates the number of errors defined by a specific LQA framework, often with severity levels (such as mistranslations, grammatical faults, or terminology violations) identified per 1,000 words during a linguistic quality assurance (LQA) process.
For high-volume publishing, reviewing every single word is rarely feasible. Instead, EPT is usually calculated via statistical sampling. This allows managers to keep a pulse on quality without slowing down the publication pipeline. EPT is particularly useful for identifying trends. If EPT spikes for a specific language pair or content type, it triggers an investigation. Perhaps the glossary needs updating, or a specific linguist requires more guidance. By keeping EPT low, you ensure that high velocity does not result in the degradation of brand standards.
Terminology consistency
Brand identity is built on words. When different translators use different terms for the same product feature or concept, the customer experience fractures. Terminology consistency measures the adherence to a pre-defined glossary.
Stratifying content for optimal performance
Not all daily content holds the same value, and treating it all with the same workflow is inefficient. A sophisticated metrics strategy involves segmenting content into tiers and applying different performance targets to each.
Tier 1: High-visibility marketing and UI
This includes homepages, major campaign headlines, and core user interface elements. For this tier, quality metrics like EPT and stylistic consistency are paramount. TAT is important, but should not come at the expense of nuance. The workflow typically involves premium human translation or a hybrid model with significant human oversight to ensure the brand voice is perfectly preserved.
Tier 2: Product descriptions and support articles
This is the bulk of daily content for many e-commerce and tech companies. Here, the balance shifts slightly toward efficiency. The goal is clear, accurate communication. Adaptive machine translation with human post-editing is the ideal approach. TTE becomes the star metric here, as it tracks how efficiently the AI and humans are collaborating to process high volumes of text.
Tier 3: User-generated content and low-traffic pages
Reviews, forum posts, or long-tail inventory descriptions often require immediate translation to be useful. For this tier, raw machine translation might be acceptable, provided the MT engine is trained on domain-specific data. The key metric here is pure speed (TAT) and coverage. Periodic spot checks using EPT can ensure the raw output remains intelligible and safe, but the investment in perfect human quality is generally not justifiable.
From metrics to strategy: balancing speed and quality
Collecting data is only the first step. The competitive advantage comes from how you use that data to refine your strategy. Balancing speed and quality requires a feedback loop where metrics inform workflow adjustments.
For example, if your TAT is lagging, look at your TTE data. Is the slowdown caused by translators spending too much time fixing poor machine translation? If so, the solution is not to pressure translators, but to improve inputs and systems (terminology, source quality, routing, and model adaptation). Conversely, if your EPT scores are perfect but your costs are unsustainable, you might be over-editing Tier 2 content that could be handled with a lighter touch.
How to build a data-driven approach to continuous improvement
Building a data-driven culture does not happen overnight. It requires a commitment to transparency and the right tooling stack.
Centralize your data
You cannot manage what you cannot see. Disparate spreadsheets and email threads hide critical performance data. Consolidating your localization operations into a unified translation management system allows you to visualize TAT, TTE, and EPT across all projects and languages in a single dashboard.
Set realistic baselines
Before you can improve, you must know where you stand. Establish baseline metrics for your current output. What is your average TTE today? What is your acceptable EPT threshold? Once these baselines are set, you can measure the impact of any changes you make, such as switching vendors or integrating a new LLM.
Implement continuous feedback loops
Data should flow in both directions. Linguists need to know if they are meeting EPT targets, and project managers need to know if timelines are realistic. Regular business reviews based on these metrics can help transform the vendor-client relationship into a more strategic partnership. Instead of discussing subjective feelings about style, the conversation focuses on objective trends and actionable optimization.
Conclusion: Turning translation metrics into measurable business impact
In high-volume publishing environments, quality and speed cannot be treated as opposing forces. They must be managed together through clear, reliable metrics that reflect real operational performance. Measuring Turnaround Time (TAT), Time to Edit (TTE), and Errors Per Thousand (EPT) allows organizations to move beyond subjective quality debates and gain concrete visibility into how their localization workflows actually perform at scale. This is where Translated adds distinctive value. By combining purpose-built AI models like Lara, a global network of professional linguists, and an AI-first orchestration layer in TranslationOS, Translated enables companies to operationalize these metrics in real production environments. TTE and EPT are not abstract numbers; they become actionable signals that continuously refine AI output, improve human efficiency, and protect brand quality across thousands of daily content updates.
Rather than forcing teams to choose between speed and accuracy, Translated’s Human–AI Symbiosis turns translation into a measurable, optimizable system. The result is a localization strategy that scales with content volume, adapts to audience expectations, and consistently delivers business-ready translations.