What’s the difference between AI and machine learning? What does IVR stand for, and how is it different from IVA?
In the cross-section between AI and customer service, there are many terms and acronyms that may be unfamiliar to you if you’re new to this space. However, these terms are easy to learn once you have a basic understanding of their definitions and context. In this glossary, we’ve compiled some of the most common terms from our industry to explain what they mean and how they apply to the customer experience. We invite you to refer back to this glossary whenever needed as you explore Speakeasy AI’s solutions.
|The Term||What it means||How it applies to customer experience|
|AI||Artificial intelligence: the ability of a computer to perform tasks that are ordinarily associated with human intelligence.||Omits human errors, improves availability, evaluates data at a larger scale to understand customers’ behavior and intent.|
|Artificial Intelligence as a Service (AIaaS)||Off-the-shelf AI tools that allow a company to leverage AI via a third-party provider, rather than developing the AI on their own.||Enables companies to immediately implement and scale AI techniques at a fraction of the cost of a full in-house AI.|
|Automation||Innovation and implementation of technology that can operate automatically (without human involvement).||Enhances the customer and agent experience and increases accuracy.|
|Chatbot||A computer program that simulates and processes human conversation via text chat messages or voice.||Allows customers to message/talk with a bot instead of a human for frequently asked questions or help with self-service solutions.|
|Customer Self-Service||Support/solutions that help customers find answers to their queries or how-to tutorials without the need for a service representative.||Allows customers to get a solution without live agent involvement.|
|Deep Learning||A subset of machine learning and AI where artificial neural networks and algorithms inspired by the human brain learn from large amounts of data.||Empowers virtual assistants and bots to respond better and provide an enhanced experience to customers.|
|First-Line Support||Support that is provided for customers’ most common/basic issues. It collects deep information regarding customers’ issues in order to solve them.||Facilitates customers’ queries (like FAQs) and gathers information to move forward to the second-line support team when complex cases arise (saving customers time).|
|Knowledge Base||A self-serve online library of information about a particular topic, product, or service.||Can assist customers in getting answers to some of the most common questions and help them self-serve when possible.|
|Labeled Data||Data that has been labeled to define its meaning and context, so that a machine-learning model can learn from it.||Can be used to determine actionable insights into customer intent, and can provide users and companies with greater context, quality, and useability.|
|Language Model||A statistical tool to predict words. Language models try to find and recognize patterns in human language.||Improves systems efficiency to understand the way humans write and speak, allowing customers to be understood easily.|
|Machine Learning||The use of computer data and algorithms to imitate intelligent human behavior.||Machine learning models can be used to power AI customer support tools, such as chatbots and IVR systems, to enhance the customer experience.|
|Narrow AI or Artificial Narrow Intelligence (ANI)||AI that is programmed to perform a single task and operate within a predefined, predetermined range.||Bots powered by ANI can be used to automate repetitive service tasks and can help deliver consistency, accuracy, and speed.|
|Natural Language Processing (NLP)||Subfield of AI that can perform sentiment analysis and intelligent messaging.||Interprets customers’ spoken or written language, enabling customers’ needs and intent to be more deeply understood.|
|Unlabeled Data||In machine learning, unlabeled data is used in unsupervised learning, which can help discover new clusters of data.||Unlabeled data is easier to acquire and store. It helps discover new clusters of data, allowing for new categorizations when labeling.|
|Customer Automatic Speech Recognition (ASR)||The use of artificial intelligence technology to process human speech into readable text.||Precise transcription of what your customers say.|
|Contact Center AI (CCAI)||Customer-support operations that are powered in part by AI technologies to deliver human-like conversations via automated systems, such as IVR.||Can reduce call center volume/wait time. Reduces the amount of after-call work and can coach agents in real-time during calls.|
|Conversational Service Automation (CSA)||A subcategory of AI that helps with automated human-to-machine conversations, as well as discussion between customers and contact center agents.||Can retrieve information and customer account details to answer customers’ queries without manual support.|
|Conversational AI Platform (CAIP)||Helps develop and implement solutions for automating customer service, customer engagement and human-computer interactions via natural language understanding and speech generation.||Allows customers to interact with machines faster using natural language processing, whether speaking or writing.|
|IVR||Interactive Voice Response; an automated telephony system that interacts with users via voice.||Enhances scalability of call centers without humans by enabling intelligent,
segmented call routing based on information they collect, allowing for richer call context and faster call resolution.
|IVA||Intelligent Virtual Assistant; an AI system that imitates human interaction to perform specific tasks. Like Siri or Alexa.||Can provide a human-like experience that can help customers to reach a solution while shortening the wait time.|
|Voicebot||A program that can help to have interactions/ conversations with users via voice.
|Allows customers to talk to a bot rather than a human regarding queries or to get assistance with self-service solutions.
|Neural Net||A system that works similar to the tasks performed by neurons of the human brain.||Helps solve complex problems, like pattern/facial recognition and data analysis. It makes the system efficient.|
|CX Journey||The sum of end-to-end interactions that a customer has with a company.||Can help to highlight the areas needed to upgrade customer-support systems to ensure a better experience for your customers.|