Every organization provides services to customers before, during and after a purchase.  For organizations whose customers are spread all over the world, the customer care team has to handle requests in different languages.  Meeting the customer satisfaction SLA for a global multi-lingual customer base without breaking the bank is a significant challenge.   How can you enable our customer care team to respond to inquiries in different languages?  Is it feasible for organizations to handle customer inquiries from across the globe efficiently without compromising on quality?

With Amazon’s introduction of AWS Translate + ELK  + Skedler, you now can!

In this two-part blog post, we are going to present a system architecture to translate customer inquiries in different languages with AWS Translate, index this information in Elasticsearch 6.2.3 for fast search, visualize the data with Kibana 6.2.3, and automate reporting and alerting using Skedler.  In Part I, we will discuss the key components, architecture, and common use cases. In Part II, we will dive into the details on how to implement this architecture.

Let us begin by breaking down the business requirement into use cases:

  • Enable customer care teams (based in the US or other English language countries) to respond to tickets/questions from customers all over the world, automatically translated, across multiple channels such as email, chat
  • Build a searchable index of tickets/questions/responses/translations/customer satisfaction score to measure (such as key topics, customer satisfaction, identify topics for automation – auto-reply via chatbots or knowledgebase)
  • Use Skedler reporting and alerting to generate KPIs on the above and alert if customer satisfaction score falls below threshold levels

The components that we need are the following:

  • AWS API Gateway
  • AWS Lambda
  • AWS Translate
  • Elasticsearch 6.2.3
  • Kibana 6.2.3
  • Skedler Reports and Alerts

System architecture:

architecture

A Bit about AWS Translate

At the re:invent2017 conference, Amazon Web Services presented Amazon Translate, a new machine learning – natural language processing – service.

aws translate

Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Neural machine translation is a form of language translation automation that uses deep learning models to deliver more accurate and more natural sounding translation than traditional statistical and rule-based translation algorithms. Amazon Translate allows you to localize content – such as websites and applications – for international users, and to easily translate large volumes of text efficiently.

Alternatives to AWS Translate include Google Cloud Translation API and Azure Translator Text.

You can find more details about AWS Translate in the following links.

> AWS official documentation: What is Amazon Translate?
> Blog post: Amazon Translate Now Generally Available
> Blog post: Introducing Amazon Translate – Real-time Language Translation
> AWS Machine Learning blog: Amazon Translate

Conclusion

In this post we presented a system architecture that performs the following:

  • Text Translation with AWS Translate
  • Index and fast search – Elasticsearch
  • Dashboard visualization – Kibana
  • Automated Customizable Reporting and Alerting – Skedler Reports and Alerts

AWS Translate+ELK+Skedler is a robust solution in helping you to handle multi-lingual customer support inquiries in a high-quality and cost-efficient way.

Excited and ready to dive into the details?  In the next post (Part 2 of 2), you can see how to implement the described architecture.

May 28, 2018
Combine Amazon Translate with Elasticsearch and Skedler to build a cost-efficient multi-lingual omnichannel customer care – Part 1

Combine Amazon Translate with Elasticsearch and Skedler to build a cost-efficient multi-lingual omnichannel customer care – Part 1

Every organization provides services to customers before, during and after a purchase.  For organizations whose customers are spread all over the world, the customer care team […]
March 12, 2018
aws transcribe comprehend elasticsearch architecture

Extract business insights from audio using AWS Transcribe, AWS Comprehend and Elasticsearch – Part 1

Many businesses struggle to gain actionable insights from customer recordings because they are locked in voice and audio files that can’t be analyzed. They have a […]
December 19, 2017
Document Text Analytics using Amazon(AWS) Comprehend - Elasticsearch 6.0

How to Combine Text Analytics and Search using AWS Comprehend and Elasticsearch 6.0

How to automatically extract metadata from documents? How to index them and perform fast searches? In this post, we are going to see how to automatically […]
November 27, 2017
Elasticsearch with AWS Rekognition + Skedler

Machine learning with Amazon Rekognition and Elasticsearch

In this post we are going to see how to build a machine learning system to perform the image recognition task and use Elasticsearch as search engine to […]