IntelliRMA – Multi Channel Response Automation

IntellimRA

Customer preference for digital channels has increased usage of mobile, website, and email for customer service requests and complaints. Typically, 60-70% of requests are covered by the top 5 request categories. However, expensive and scarce service agent bandwidth is wasted in reviewing and responding to each email manually.

Multichannel Response Automation (MRA) uses the cognitive capabilities of Natural Language Processing to identify the customer service intent, extract key elements (like Account #, name, email address, and phone number) as well as understand customer sentiment.

The extracted information is then utilized to prioritize requests based on category and sentiment. The solution also integrates directly with the customer relation management (CRM) system to ensure that all requests are tagged in CRM and tracked to closure.

Additionally, rules can be configured for automated responses to frequent service categories with instructions to resolve the problem with self-help tools with a specific context of the service request type.

By close looping the entire process based on Cognitive insights from unstructured data, your business can achieve complete Cognitive Process Automation.

Key Features

Integration with email data and data from other digital channels like Twitter, Website as well as text notes in the CRM system
Custom Model to extract business-specific entities (e.g. Account Number, Make/Model, Claim Number)
Ability to extract intent of communication (e.g. Request for a document copy, Changes in Account, Complaint, Escalation etc.)
Ability to extract key elements like Name, Email, Phone Number
Extract customer sentiment, and emotions from
Perform analysis of service categories for root cause analysis, identify peak requests and capacity planning
Analyze product/service with maximum requests and/or complaints and optimize for better customer experience
Integration with customer-facing as well as downstream systems for end-to-end automation
Ability to train the machine learning model to understand business context and terms
Real-time integration to close loop the entire process flow

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What benefit will it provide?
Better Customer Experience by enabling quicker response time. Productivity gains for service agents. Reduced overall servicing cost.

Why is it required?
Customer Service teams have limited capacity and digital service interactions are increasing by the day. Manual identification, classification, and response to all interactions are inefficient, time-consuming, and error-prone. It also leads to poor customer experience.

What does it do?
The solution extracts intents from unstructured data communication for service requests like email, customer service notes, and social channel messages and converts the intents into actions.