71% of online shoppers spend more when addressed in their native language. Here's how to scale customer support across 12+ languages without scaling your team.
71% of online shoppers spend more when addressed in their native language. Yet most Shopify stores still operate a single-language support inbox, relying on customers to write in English. Here's how to scale customer support across 12+ languages without scaling your team.
Why language matters more than ever
Cross-border e-commerce is growing at 25% annually. Shopify merchants increasingly ship to customers in Germany, France, Poland, the UAE, and beyond — but their support team often speaks only English. A reply in the wrong language, or an obvious machine-translation, signals that the customer is an afterthought.
Automatic language detection
The first step is knowing what language the customer wrote in. Modern NLP models (including the one powering Retenza) detect language with over 98% accuracy from a single sentence, including mixed-language emails and regional dialects. Once detected, the language is stored on the conversation and used to generate a matching reply.
Generating replies in 40+ languages
Claude, the AI model behind Retenza's reply generation, was trained on multilingual data and produces fluent, natural-sounding text in French, German, Polish, Spanish, Arabic, Italian, Portuguese, Dutch, and many more — without any extra configuration. The system prompt instructs the model to match the customer's language exactly.
Building a multilingual knowledge base
AI quality improves significantly when it has access to your store's specific policies and product information in the customer's language. Upload translated FAQ documents, return policies, and product guides to your Retenza knowledge base to give the AI the context it needs.
Measuring quality across languages
Track CSAT scores and escalation rates per language. In our data, stores that localise their AI prompts and knowledge base see 18% higher CSAT in non-English markets compared to those relying on generic English prompts.