This is a tip for maximizing the efficiency of translation for user facing content within your apps. In most applications, the 90/10 rule applies in that a small percentage of your user facing prompts (strings) account for the majority of views. Most companies use instrumentation to understand how users are interacting with their services, but typically don’t extend this to monitoring the relative visibility of user facing prompts.

This is worth doing because without that information, translators and localization managers don’t know which prompts are highly visible, and which ones are buried “beneath the fold”.

We generally recommend that clients implement a fix forward workflow, where MT|AI translation is used as a placeholder and then reviewed and post-edited as needed by professional translators or copywriters.

With good instrumentation, you can focus their attention on the high priority/high visibility strings first, since those are more likely to be seen, before proceeding to the lower visibility strings. This insures that if you have a capacity constraint on human oversight, which can happen if review is done by in-house or freelance language leads, the higher visibility strings are prioritized.

A good way to do this is to emit log events within the function that is called to display user facing strings. See the article below for a coding pattern to follow.

Future Proofing Your Codebase For Global Readiness

Within this function, you will call another function to log this event. Since you are interested in relative, not absolute, views, you can use a randomization factor so this function is called one in X views, to avoid generating an excessive number of logging events that can degrade performance.

Then, you’ll need to write a small program or script that runs daily that performs the following tasks:

1. Queries the logging system or db for a list of string display events for the past N days
2. Group and count display events by string keyname and locale
3. Filter out non-English locales to focus on source strings
4. Sort by view count from highest to lowest and normalize
5. Assign each unique string a label such as P1, P2 or P3 based on its relative visibility (e.g. P1 = top 10%, P2 = top 10-25%, P3 = everything else). These criteria are arbitrary so adjust as you see fit.

Then do the following with this ranked list. Your TMS should provide an API that allows you to add and remove tags from strings. So for each string in this list, call the TMS API to do the following:

1. Remove the tags P1, P2 and P3 from the string
2. Add the current P value tag to the string