AI Use Case

Improve Interactive Voice Response (IVR) effectiveness

Improve IVR effectiveness through deploying voice to text and natural language processing (NLP) to better capture and enable response to customer queries. Understanding customer pre and in-call intent helps reduce time to serve and potential customer problems. This leverages Natural Language Processing (NLP) and machine learning to estimate and manage customer's intent on calls. In-call assessment enables multiple functionalities: e.g. call routing, issue triage, automated responses.

Function

Customer Service

Contact Centre

Benefits

Cost - Job automation,Operational - Customer wait times,Revenue - Customer engagement

Case Studies

Acer~Acer America improves service by decreasing repeat caller rate by 15% with responses powered by natural language speech recognition.,Marks & Spencer~Marks & Spencer plans to automate all customer call routing with 90% accuracy using machine learning

Industry

Data Sets

Audio,Structured / Semi-structured

AI Technologies

Machine Learning (ML),Product - Customer Service,Product Type - Speech - Recognition,Product Type - NLP - Topic Modeling,Product Type - NLP - Text Classification

Potential Vendors

Nuance,Twilio