What language barrier? Upgrade your customer support to be fully multilingual automatically

Crowlingo
3 min readAug 26, 2021

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Today, it’s definitely hard for machines to understand all the complexity of human language. When it comes to customer support, companies build systems to answer more efficiently to customers issues according to the semantic of their requests and this involves a lot of issues.

Customers often rush to get to the bottom of their issue. According to Invoca studies, 53% will wait up to 5 minutes before giving up. The goal is to answer as soon as possible with the right agent to improve customer satisfaction. To do so, companies redirect customers support tickets to the right agents to provide the best solutions and in a short amount of time. If this intelligent routing system is automatic, it will also benefit the service desks. This will speed up their processes and make it possible to reach even more customers. In the meantime, operators can focus their time and effort where it matters most.

Multiple tools already exist to automate your support management and speed up your processes by automatically routing new incoming tickets according to their semantic. But most of companies support desks are limited to English today and abandon some countries. In this article, we will show you how to allow all your customers worldwide to access your support desk like in any other countries. This means, no more tools for France, Germany, China, or India. Every languages will be managed in one final universal solution. Indeed, you will only need one dataset of classification in one language and you will be able to make it work for 100+ languages the same way, with no additional training data.

The goal is to make the customer support management faster and easier to stay up to date. Indeed, what we want to avoid is to make an update for each classification model in each language which costs of course a lot of time, resources and money.

3 tips for providing fully multilingual customer support

  • Unify your text analysis processes in one universal model

Gather documents from different languages according to their topics and semantic similarities. Route support tickets to the right services and speed up your support desk.

  • Understand text meaning with in-depth analysis of semantics and syntax

No more keywords! Extract multilingual concepts from ticket descriptions and handle alternative spellings. Take into consideration the context of the document (sentiment, topics, entities, …).

  • Create or upgrade your system to handle 100+ languages automatically

Don’t waste time and resources to create one new solution for each language! Automatically upgrade your classification models to manage 100+ languages with no additional training data and no translation.

Universal analytics

If your system can understand interaction efficiently no matter the language, you will be able to gather international analytics. This will allow your company to access and analyze customer feedbacks from a variety of different channels worldwide. For instance, you will be able to detect potential customer churn in multiple countries by analyzing the negative experiences they face during interaction.

Everything in one place

Zendesk’s users can now finally integrate this solution to their own support desk. Crowlingo helps companies to unify their processes to be universal with no additional training data and no translation. This solution updates Q&As, support tickets managements and chatbots to manage 100+ languages starting only with one dataset (one language only needed).

The Crowlingo App is already published on Zendesk Marketplace and open to everyone. You can give a try with 300 requests and one custom model for free to make sure it fits your expectations.

How to integrate?

Just follow the instructions on the Zendesk marketplace for the Crowlingo App

If you have any questions, please let us know by email: contact@crowlingo.com

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Crowlingo
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Crowlingo provides Natural Language Processing (NLP) services to find insights and relationships in text data.