No two days are ever the same in product localization. When it comes to checking linguistic quality, your product might churn up a variety of unexpected issues—some acute, others more chronic and harmful to your reputation, yet all seemingly unique to your company.
But don’t worry. Issues in the linguistic quality assurance (LQA) process are more common than you think. Let’s look at seven of the peskiest LQA pain points most localization teams encounter and what you can do about them.
1. Untapped data potential
In our experience, many companies collect data from isolated quality activities
. Chances are you’ve collected a goldmine of LQA data that can be used to guide your localization decision-making. But without a clear linguistic quality dashboard
, it can be difficult to make sense of and share data internally—especially without first deciding which metrics to track and why.
2. Linguistic bugs
Have you included linguistic testing
as a final quality check before release? If so, you might be all too familiar with the frustration of finding bugs in the final product—caused by out-of-context translation, for example—that could have easily been prevented early on.
3. High support demands
A disproportionate surge in support calls and requests in one or more markets might be a sign of trouble, regardless of whether you localized for that region or not.
4. Quality inconsistencies
Large companies with many product lines often grapple with inconsistencies in linguistic quality over time or across languages. You might also face resistance from various stakeholders to submit to company-wide standards.
5. Low user acceptance
Even after localization, you might notice your target audience didn’t receive your product as expected. Translation quality isn’t always to blame, but it is worth checking.
6. High bounce rates
Again, numerous factors come into play here, but you might want to review translation quality if you notice users quickly leaving your site at a higher rate on localized sites than on others.
7. Multiple quality frameworks
This, unfortunately, is unavoidable in companies that grow inorganically—for example, after an acquisition. To get everyone on the same page means aligning the acquired company’s translation quality metrics with yours—an arduous task, but critical.
So, how do you go about solving these?
How to solve pain points in linguistic quality assurance
While you might already have your linguistic quality and review processes set up, it’s important to stay abreast of how LQA standards evolve with market trends, technologies and audiences. Even your own quality demands can change as your localization processes mature.
So, here are the solutions and steps you’ll want to take to fine-tune and troubleshoot as you go.
Look for recurring problems.
Patterns usually indicate you need to dig deep into problems to tackle them at the source
. Could they be related to source quality content or bad/uncleaned data from translation memories? Or is there something wrong with the LQA process itself?
Refresh language assets.
Your translation memories
, style guides and glossaries are your go-to language assets, but they can get out of date. Try creating an inventory of content you can reuse from existing translation memories and review how much of it is still relevant to your business. Also consider implementing automatic language checkers to help enforce consistency and make glossary adherence more efficient.
It helps to clearly define stylistic preferences
such as voice, tone, grammar and punctuation at the outset, even if that means developing more than one style guide to suit different audiences and content types (marketing content, customer support documentation and so on).
Build a dedicated reviewer program.
Strengthen your review process by hiring exclusively for review, either through internal channels or outsourcing
. Train them on quality expectations—for example, what constitutes preferential changes and how much to stay true to source content—and be sure to provide regular feedback
Aggregate program-wide quality data.
If you want to achieve brand voice and terminology consistency
across all products, you’ll need to centralize LQA data collection across your company for each team responsible for translation.
Monitor and analyze performance.
Without this step, all your efforts will be in vain. Use your data
to understand what’s working and what’s not—for particular types of content, vendors or languages—and optimize performance accordingly.
Finally, think of linguistic quality assurance as an ecosystem with many interrelated parts. It always makes sense to start by prioritizing what needs immediate attention, of course, but try to learn from the ecosystem as a whole to iterate changes that bring lasting results.
Have you experienced any problems we didn’t address above? Drop us a line
and let us see if we can help!