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Smart Email Support for Telecom Organisations - Provide automated customer support for emails
Smart email support showcases Watson natural language capabilities to provide a methodology for developing an intelligent customer support system.
Artificial Intelligence
Organisations receive communication, from their customers, through various channels like emails, phone calls, applications etc. It is important for these organisations to act on these communications quickly and accurately. Manually handling this scenario can be time consuming and error prone. Building an automated, intelligent system to handle customer communications is needed for customer satisfaction and speedier resolutions of concerns. Here we provide a methodology for handling emails from customer in an automated and smarter way. The system will understand the intent and content of emails, determine if the email has all the necessary information to process the request, compose an email asking for missing information if any. Watson services on IBM Cloud are used to demonstrate this use case.
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Providing customer support in a timely manner is very important to enhance customer experience. Organisations receive communication, from their customers, through various channels like emails, phone calls, applications etc. Organisations need to understand the intent and content of each of the communication and ask customers for any additional information required to fulfil their requests.
In this code pattern we take an use case of a telecom domain customer support who action on email requests they receive from customer. We will consider request scenarios for enabling a service, disabling a service, changing plan and Adding family member to plan.
As a customer support, one should
- Know intent of email.
- Know information available in these emails.
- Identify information that is missing.
- Auto-compose responses and send emails.
This code pattern provides an automatic and cognitive way of achieving the above requirements. It uses natural language processing of emails, understanding intents of emails, auto composing responses and providing a dashboard with high level summary of intents and emails. While the use case demonstrated here is for a telecom domain, it can be applied to any domain. It integrates with database, which acts as a CRM, to pull customer information to validate emails and requests. It uses Watson Knowledge Studio for custom domain natural language processing, Watson Natural Language Understanding to deploy custom domain model and get entities from emails, Watson Natural Language Classifier to get intent of email, CloudantNoSQL database to store emails and customer data, Node-RED to integrate with emails, Watson services and Cloudant database, Node runtime for user interface application.
After completing this pattern, you will learn how to:
- Build a custom model using Watson Knowledge Studio and deploy it on Watson Natural Language Understanding.
- Build a Node-RED flow that integrates email server, Watson services (NLU and NLC) and Cloudant database.
- Deploy application, send sample customer emails and see the emails being auto processed using a simple UI.
- Deploy custom model, built for telecom use cases using Watson Knowledge Studio, to Watson Natural Language Understanding.
- Node-RED flow gets (polls) for customer emails and retrieves them.
- Customers are validated by matching customer email id and customer data available in Cloudant database for customer records.
- Information (entities like name, phone number) in emails are identified using Watson NLU.
- Intent (or service request type) of email is identified using Watson NLC.
- Email content, entities, intent are saved in Cloudant email database for each request.
- Customer support user accesses nodejs application deployed on IBM cloud.
- Application fetches emails, entities and intents from Cloudant email database.
- Responses to email are auto populated (auto composed) and customer support user can just click the send button to send response to customer.
- Watson Knowledge Studio: Use IBM Watson™ Knowledge Studio to create a machine-learning model that understands the linguistic nuances, meaning, and relationships specific to your industry or to create a rule-based model that finds entities in documents based on rules that you define.
- Watson Natural Language Understanding: A IBM Cloud service that can analyse text to extract meta-data from content such as concepts, entities, keywords, categories, sentiment, emotion, relations, semantic roles, using natural language understanding.
- Watson Natural Language Classifier: The Natural Language Classifier service applies cognitive computing techniques to return the best matching classes for a sentence or phrase.
- Node-RED: Node-RED provides a browser-based flow editor that makes it easy to wire together devices, APIs, and online services by using the wide range of nodes in the palette.
- SDK of Node.js: The Node.js runtime on IBM® Cloud is powered by the sdk-for-nodejs buildpack. The sdk-for-nodejs buildpack provides a complete runtime environment for Node.js apps.
- Natural Language Processing: the ability of a computer program to understand human language as it is spoken. NLP is a component of Artificial Intelligence.
- Artificial Intelligence: Intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans.
Organisations receive communication, from their customers, through various channels like emails, phone calls, applications etc. It is important for these organisations to act on these communications quickly and accurately. Manually handling this scenario can be time consuming and error prone. Building an automated, intelligent system to handle customer communications is needed for customer satisfaction and speedier resolutions of concerns. Here we provide a methodology for handling emails from customer in an automated and smarter way.
In the Code Pattern we take an use case of a telecom domain customer support who action on email requests they receive from customer. We will consider request scenarios for Enabling a service, disabling a service, changing plans and Adding family member to a plan.
As a customer support, one should
- Know intent of email.
- Know information available in these emails.
- Identify information that is missing.
- Auto-compose responses and send emails.
This code pattern provides an automatic and cognitive way of achieving the above requirements. It uses natural language processing for emails, understanding intents of emails, auto composing responses and providing a dashboard with high level summary of intents and emails. While the use case demonstrated here is for a telecom domain, it can be applied to any domain. It integrates with database, which acts as a CRM, to pull customer information to validate emails and requests. It uses Watson Knowledge Studio for custom domain natural language processing, Watson Natural Language Understanding to deploy custom domain model and get entities from emails, Watson Natural Language Classifier to get intent of email, IBM Cloudant database to store emails and customer data, Node-RED to integrate with emails, Watson services and IBM Cloudant database, Node runtime for user interface application.