When we think of localization data, we often focus on words — counts, costs, and deadlines. But in essence, it isn’t just about tracking words or jobs. It’s about seeing the bigger picture. 

With Phrase Data, you unlock a layer of intelligence that transforms your localization process from reactive to strategic: how cost, quality, and turnaround time intersect across content types, vendors, and markets.

Here’s a few examples of how strategic teams are turning Phrase Data into actionable insights that drive impact. 

Is your Translation Memory still pulling its weight?

Over time, translation memories (TMs) can quietly accumulate bloat. Segments that once served a purpose can no longer reflect current terminology, tone, or expectations. For many localization teams, it’s unclear whether their TMs are actually helping.

By analyzing segment-level data, you can track when a TM was last used, how often its matches are applied, and how much editing they still require. That insight reveals which TMs are doing the heavy lifting and which ones may be slowing things down.

 This is a deliberate strategic step, not just a routine clean-up. Retiring outdated TMs reduces maintenance costs, prioritizing efforts on high-performing ones, and helping teams fine-tune their TM thresholds for maximum impact. The result? More auto-confirmed segments, less editing, and smarter reuse.

Which Machine Translation (MT) engines are actually the best fit for you?

Effective MT engine selection focuses on matching each engine to the content it’s best suited to. Yet many companies still rely on static setups without truly knowing which engine performs best where. MT shouldn’t be one-size-fits-all and your data can prove it.

By comparing quality scores and editing time across engines, languages, and domains, teams can see how each MT engine actually performs across each content type.

Maybe one engine excels at UI strings in German but underperforms in French marketing copy. Or you may discover that Portuguese MT output requires 2x the editing effort compared to Spanish for your support content. Alternatively, you may realize that you can reduce post-editing effort for support articles by 30%. 

That’s a clear signal to either adjust your Phrase QPS threshold, switch engines for that language pair, or apply post-editing more selectively. When you quantify MT effectiveness by content type and locale, you make smarter decisions on where to automate, where to review, and where to invest.

Which vendors give you the best quality per dollar spent?

Vendor rates tell only part of the story. What if one vendor charges less, but requires double the editing effort?

With segment-level metrics like average QPS, editing time, and word volume per document, you can compare true vendor performance. See which vendors are delivering high-quality content efficiently and which ones are driving up your internal review costs.

What’s slowing you down and how do you fix it before it costs you?

Where are your bottlenecks? Is it a specific vendor, language pair, or step in the workflow?

Your data can answer that. By measuring the editing time across languages, vendors, content types, and workflow steps, you can isolate the friction points.

Having this kind of visibility allows you to make proactive planning and avoid instances like missed SLAs for demanding content. 

Which content is ready to skip human review without sacrificing quality?

Phrase Data allows you to identify which content is consistently high-quality and untouched, by analyzing segments with high quality scores (QPS) and zero edit time.

Essentially, this is content that’s effectively production-ready as soon as it’s translated by MT engines. These might be help center articles, templated UI text, or repeated support content.

The payoff is massive and turnaround time is cut drastically. You free up linguists for creative or sensitive content. Ultimately, you reduce cost without touching your quality baseline. This shouldn’t be risky but should be considered as responsible automation, guided by data.

But, what is the ROI of a 1% reduction in human review, you might ask?

One percent sounds small, but in localization it adds up fast. Let’s say your team localizes 2 million words per month. If just 1% of that volume could skip human review thanks to high QPS and no edits, you’d shift 20,000 words/month into no-touch territory.

Depending on the region or vendor being used for translation, a business paying $0.05 – $0.10 per word could quickly see annual savings of $12,000–$24,000.

If we reasonably assume review edit times of 10 – 30 seconds for a segment, it also saves roughly 8 hours of linguist time every month. That’s 1–2 full workdays freed up without compromising quality because these segments were already “good enough”.

You can find technical details about the use cases in our Help Center article, along with some sample queries to help get you started!  

As you can see, the power of Phrase Data isn’t in tracking how many words were translated. It’s in showing which words mattered, which processes worked, and where your next opportunity lies.

When your data is structured and surfaced the right way, you don’t just report on what happened. You act on what should happen next.

At the end of the day, these insights are tools for making smarter, faster, and more defensible decisions across your entire localization strategy, from budgeting and vendor management to automation and optimal content processes.