From Support Calls to Insights - Using Automatic Speech Recognition and Natural Language Processing to Drive Product Roadmaps (2022)

By Jing Qing, Veronica Bloom, Michael Addonisio, Christy Gearheart; Charter Communications

The goal of this project is to utilize artificial intelligence (AI) and machine learning (ML) techniques to gain insights into the drivers of support calls. These insights are used to determine areas of improvements to both processes and products to enhance our customer experience.

In this paper, we discuss the end-to-end automated pipeline to go from call center data to actionable insights utilizing machine learning techniques such as Automatic Speech Recognition (ASR) and Natural Language Processing (NLP). Discussion of the pipeline architecture, use cases of automated call disposition, and call topic trend analysis are also included. Broadly, the pipeline converts recorded call audio conversations to text transcripts using automatic speech recognition, then uses those transcripts to train classification models to predict call disposition. Unsupervised topic modeling extracts new trends and topics from the call-volume data over time to identify new or emerging call drivers. These call drivers subsequently drive new feature development and product roadmaps.

By clicking the "Download Paper" button, you are agreeing to our terms and conditions.

Similar Papers

Scaling IP Advertising Using Manifest Manipulation
By Vipul Patel, Charter Communications; Xavier Denis, CommScope
2019
New Generation Data Governance for Charter Network:1
By Jay Liew, Mark Teflian, Bruce Bacon, Jay Brophy & Randy Pettus, Charter Communications
2019
Composite Quality Metric KPIs - A General Framework for Interpretable Composite Metrics
By Rohan Khatavkar, Ryan Lewis, Veronica Bloom; Charter Communications
2022
Cable and Mobile Convergence: A Vision from the Cable Communities Around the World
By Jennifer Andréoli-Fang, PhD, CableLabs; John T. Chapman, Ian Campbell, & Mark Grayson, Cisco; Ahmed Bencheikh, Praveen Srivastava & Vikas Sarawat, Charter Communications; Drew Davis & Paul Blaser, Cox Communications; Damian Poltz & Dave Morley, Shaw Communications; Eduardo Panciera, Telecom Argentina; Philippe Perron, Sylvain Archambault, Eric Menu, Géraldine Trouillard & David Lagacé, Videotron; Gavin Young & Bruno Cornaglia, Vodafone
2020
Voice Control of Set-Top Box for Customers with Non-Standard Speech
By Adina Halter, Comcast; Sara Smolley, Voiceitt
2022
Moving from Scripted Dialogs to Automation, Omni-Channel and Predictive Analytics
By Marc Bellini, Nokia
2017
A PNM System Using Artificial Intelligence, HFC Network Impairment, Atmospheric and Weather Data to Predict HFC Network Degradation and Avert Customer Impact
By Larry Wolcott, Michael O'Dell, Peter Kuykendall, Vishnu Gopal, Jason Woodrich & Nick Pinckernell, Comcast
2018
How Automatic Content Analysis Enables The Enternaiment Experiences Of The Future
By Jan Neumann, Comcast
2016
Gridmetrics Data Provide Insights and Improve Situational Awareness of the Electric Power Grid
By Robert Cruickshank, Ph.D. & Nicolas Metts, Cable Television Laboratories; Paul Schauer, Comcast Cable Communications; Curtis Snyder
2020
Assuring Data Delivery from Critical IoT Devices: A Method to Create New Services and Mitigate Liability
By Michael Kloberdans & Shlomo Ovadia, Charter Communications
2018
More Results >>