When we think of the use cases of web analytics, it’s often in a marketing context, whether the creation of consumer profiles or geotargeting. Zendesk’s strategy is brilliant in that they use the same data to determine how they prioritize their budget allotment for translation. MT works best in conjunction with other types of translation, and this strategy has allowed Zendesk to make use of both MT and MTPE in the best way possible.
With MT and web analytics together, the end result is that Zendesk has been able to save much-needed funds while maintaining quality service where it is needed.
Zendesk uses an MT engine that has been specifically trained uruguay mobile database for their purposes, which is a good practice. Not all MT engines are built the same, or perform well under a different context.
An MT engine trained on data from the manufacturing sector, for example, would be familiar with the terms used in that industry, but would not be well-suited for, say, machine translations for military and defense. And an MT engine trained on generic data tends to perform less well for any industrial purpose than one that has been specifically trained. That’s why you don’t see companies using Google Translate.
The results are clear for Zendesk. Drain mentions a case in which a localization department approached her for help with improving their translation quality. They, too, used an MT engine, but the results were far from satisfactory. Apart from other factors, such as post-editing and customer feedback, what was made clear is that the MT engine used by Drain and her team was better trained.