AI can help Call Centers reduce call volumes, decrease call times, and boost customer satisfaction at scale in ways never before possible. By providing in-depth real-time analysis of call data to support the agent and improve overall performance, recommending best resolutions, predicting the reasons customers are calling for, smart routing to best agents, and post-call analysis, AI is transforming the modern call center.
Understand the Use-case under 5 minutes
Video (2.5 minutes)
Transform your call center via intelligent chatbots, intelligent voicebots, smart routing, real-time voice analytics with sentiments analysis, agent assist with next best action, and post-call analytics
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Get to know more Business and Technical details about the use-case (15-30 minutes)
More detailed introduction covering business and technical aspects
Video (watch until the 19:00)
Amazon Connect is an Al-based omnichannel cloud contact center service. The video expands on it's features, shares case studies with great ROI, shows different example workflows, and more
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Video (6 minutes)
Expand on Google Cloud's Contact Center AI technology and how it works, and details about the components of Contact Center AI and how they fit together.
WatchCase studies, Organizational Aspects, Return on Investment examples
White Paper (12 minutes)
It follows a "tell a story" approach to show how AI improves call centers' performance and customer satisfaction in each step in the customer service call. Mentions specific ROI figures per each step
Read
Case Study (6 minutes)
A Bank's achievements after using AI-Call Center: +75% calls that were zero touch, 0% wait time with intelligent routing, +80% faster customer service agent responses, +50% improvement in first call resolution rates.
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Case Study (10 minutes)
DNB improved its digital journeys with virtual assistants and artificial intelligence (AI)-powered routing. It also used chatbots to achieve a 30% reduction in call volumes and resolve 90% of inquiries
ReadMore details on the technical aspects of the use-case
Article (12 minutes)
Illustrates the speech-to-text transcription process of the recorder calls. Imagine if every service representative could identify the caller's intent and engage in a conversation that aligns with that intent?
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Video (10 minutes)
Call centers challenges and areas where AI can help, then the components of an intelligent call center built on Azure AI. Expands on components and how they can be used to improve call center performance
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Video (28 minutes)
Introduction to Amazon Connect, how Contact Lens for Amazon Connect enables contact center supervisors to understand the sentiment of customer conversations, identify call drivers, & evaluate compliance
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Technical resources that will help you implement the use-case (notebooks, tutorials..)
Github Repo
Sample code for processing recorded customer calls using Azure Cognitive Services Text Analytics APIs.
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Github Repo
DeepSpeech is an open-source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier.
VisitGithub Repo
It shows how Azure AI services could be used both in real-time and batch scenarios for an Intelligent Contact Center.
VisitGithub Repo
Includes many Amazon Connect repos and code samples such as real-time transcription, speech analytics, contact lens, and more
VisitGithub Repo
Call center "first contact" app that demonstrates speech-to-text, language detection, translation, emotion detection, and parsing for key phrases
VisitGithub Repo
A collection of components and code from Google Cloud that you can use to transcribe audio files to extract insights from customer conversations with call center agents
VisitGithub Repo
Sample code for processing recorded customer calls using Azure Cognitive Services Text Analytics APIs.
VisitGithub Repo
TTS is a library for advanced Text-to-Speech generation. TTS comes with pre-trained models, tools for measuring dataset quality and is already used in 20+ languages for products and research projects.
VisitGithub Repo
TTS is a library for advanced Text-to-Speech generation. TTS comes with pre-trained models, tools for measuring dataset quality and is already used in 20+ languages for products and research projects.
Visit
Data Sets you can use to build Demos, POCs, or test Algorithms
Over 2 Terabytess of labeled audio datasets publicly available and parseable ... This database has led to a publication for the 2020 Speech Prosody
An audio dataset that consists of a unique MP3 and corresponding text file (9,283 recorded hours). It also includes demographic metadata like age, sex, and accent. The dataset consists of 7,335 validated hours in 60 languages.
Summa Linguae has a catalog of off-the-shelf call center data sets available in a variety of languages. And if we don’t already have the data you need, we can collect it for you.
Off-the-Shelf Products using AI for Streamlining Call Center Operations
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