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Chatbots have become ubiquitous on the Internet. According to VentureBeat, eight out of 10 people say they’ve used a chatbot in the past, and many say they would rather use a chatbot than browse a website to find what they’re looking for.
But while chatbots provide valuable information, they have limitations. Because they are largely rule-based, scripted programs, chatbots are best suited for providing an interaction based solely on the most frequently asked questions. A ‘FAQ’ approach only supports very specific keywords being used.
Conversational AI, or voice AI, on the other hand, absorbs customer feedback and learns in real-time, which can be applied to the same question at a different point of a client’s journey. This eliminates the need for users to repeat key information over and over. Through advancements in speech technology, such as natural language understanding (NLU) and Automatic Speech Recognition (ASR), voice AI can also understand complex queries and compound questions, providing a superior customer experience.
To provide a closer look at the differences between chatbots and conversational AI and describe how each offers strengths based on specific needs, we’ll explore these topics:
The first appearance of chatbots
How chatbots differ from conversational AI
A better customer experience with conversational AI
Why are chatbots still so popular?
While chatbots first started appearing on modern websites roughly 10 years ago, the first chatbot was created in 1966 by an MIT computer scientist, Joseph Weizenbaum, in the form of ELIZA. This chatbot was based on a limited, predetermined flow. Eliza used pattern matching and substitution methodology that gave users an illusion of understanding on the part of the program but had no built-in framework for contextualizing events.
Chatbots have evolved over the years since ELIZA and now also incorporate artificial intelligence and are frequently used in situations in which simple interactions with only a limited range of responses are needed. This can include customer service and marketing applications, where the chatbots can provide answers to questions on topics such as products, services or company policies. Where basic chatbots show their limitations is if they receive a request that has not been previously defined; they will be unable to assist, and spit back a “Sorry, I don’t understand,” response.
18% of consumers use chatbots to find business hours
17% want product information
16% are looking for a nearby business location
16% make a customer service request
12% request technical support
Artificial intelligence chatbots are chatbots trained to have human-like conversations using a process known as natural language processing (NLP). With NLP, the AI chatbot is able to interpret human language as it is written, which enables them to operate more or less on their own.
In other words, AI chatbot software can understand language outside of pre-programmed commands and provide a response based on existing data. This allows site visitors to lead the conversation, voicing their intent in their own words.
Conversational AI, often used in reference to voice AI, uses a voice user interface (VUI) to significantly improve interactions between machines, products, services centers, and people. When used in the context of voice AI, conversational AI is a combination of key voice technologies that enable digital voice assistants to understand natural human speech and respond in kind. Unlike robotic command and control voice interfaces, conversational voice assistants use a combination of Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) technologies to understand speech in real-time and create meaning even as the user is talking.
When implemented on an advanced voice AI platform, these natural-sounding voice interfaces are also context-aware. They remember previous parts of the conversation and eliminate the need for users to repeat key information over and over. Large content domain libraries equip conversational voice assistants with the knowledge they need to respond to natural speech accurately. Multilingual capabilities allow brands to deploy voice assistants for users across the globe.
The result is an incredibly human-like engagement.
Traditional chatbots and more advanced AI chatbots are not the same as conversational AI in the voice AI context. Although chatbots are referred to as conversational AI solutions, they are not able to respond conversationally due to their text-only limitations and lack of advanced machine learning capabilities and reliance on if/then statements and decision trees.
Conversational AI deployed via a voice assistant with machine learning capabilities offers more of the true AI experience, since it responds to a spoken query with a human-sounding voice. With a chatbot, you’d have to be exact with your verbiage in order for the machine to provide the answer you’re searching for based on user inputs.
Consider the scenario of a chatbot used by an e-commerce company. One of the most common questions customers will ask about is the status of their shipment.
If you ask the chatbot, “Where is my package?” then you’ll get an exact answer depending on how the decision tree has been built out. But what if you say something like, “My package status shows delivered but I did not receive it. Was it lost or delivered to the wrong address?” You may run into the problem of the chatbot not having the skillset needed to understand this complex query, and responding to the user with a frustrating error response.
A better customer experience would be a chatbot that is powered by conversational AI that actually learns from the input being given and produces an answer based on analyzing the incoming customer queries and using contextual awareness. Voice AI will be able to understand the intent and sentiment behind customer queries by training on historical data and past customer tickets and won’t require human intervention. This form of a chatbot would understand what is being asked based on the sentiment of the message and not specific keywords that trigger a response.
With the proper AI tools, messages that don’t explicitly say, “Where is my package?” will still be understood to be asking the location of an item. This goes a long way for many scaling customer support teams and enables them to automatically deflect incoming customer queries with artificial intelligence while still maintaining high customer satisfaction.
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