How SYSTRAN helped Adobe to improve multilingual customer satisfaction while lowering call center costs
KEY FIGURES
€5.9
billion revenue
15 000
Employees worldwide
1984
Date if incorporation in San Jose
Case study
Adobe Desafíos
In an effort to reduce costs in low revenue, non-English speaking markets, Adobe made the decision to revert customer service phone calls to English.
The company was not only interested in cutting call center costs while providing superior multilingual customer support, but also in providing ways to empower customers to find user support in their language via self-help and user forums online.
solution
SYSTRAN traducir Servidor
Adobe uses SYSTRAN translate Server to increase translator productivity, and SYSTRAN.io for real-time machine translation.
This improved multilingual self-help support and the translation of online product and service materials.
Benefit
Increase satisfaction for multilingual customers while reducing costs
In non-English markets, bilingual agents are only available for limited hours. Customer service calls arrive round the clock, leading to bilingual agents using callbacks, email, and chat, which couldn’t provide real-time service to all customers.
Hence, the need arose to translate more of Adobe’s content to enhance customer support and satisfaction for multilingual customers. This required making additional help and support content available in languages not covered by live agents, with a focus on speed and budget constraints.
Machine Translation, vital asset for global customer service efficiency
Adobe discovered that Machine Translation is understandable, useful and powerful in enabling multilingual customers to find their answers.
Adobe continues to use SYSTRAN.io to translate product and service content in order to:
- improve customer satisfaction for multilingual customers,
- lower call center costs,
- and enable users to access support in their own language via multilingual help and support content.
4.28/5
Quality Translation
Adobe website visitors who viewed the translated content were asked to rate the quality of the translation. On a 1 to 5 scale, the average answer was a 4.28.
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