AI chatbots have been around for a while now. In fact, the first ever natural language processing programming language was written back in the 1960s.
Despite their longevity, it’s fair to say that AI chatbots as an accepted mainstream device for customer interactions have become part of the background over the last decade, So how do chatbots work?
AI chatbots using natural language processing are commonly used
From Google Assistant to the chatbots that pop up for website visitors, the idea of a chat interface simulating human conversation is no longer strange for most consumers.
But how do AI chatbots work? What kind of machine learning algorithms power them and how do they really impact customer experiences?
According to the latest data on chatbot technology, the industry is worth around $137.6 million in 2023. Experts predict its value will rise to around $240 million over the next two years.
Why are more businesses implementing an AI chatbot?
As of January 2023, the most funded conversational AI is ASAPP and there are more than three million chatbots on Facebook messenger alone. They are geared towards the target audience and answer questions using close to natural language.
A typical customer communication handled by a human conversation takes far more time than chatbots. This is obviously one of the primary benefits for businesses using chatbots to answer user queries.
A human agent can typically be expected to handle around 17 customer support interactions in a working day. It’s estimated that chatbots can save businesses around 2.5 billion hours compared with human agents.
How do chatbots work and how do they use artificial intelligence?
AI chatbots is a programme that uses natural language processing to have a conversation with a human being.
Chatbots work by simulating human conversation and enable the programming language to provide relevant answers to a user’s query.
Chatbot technology is designed to work without any human intervention, so customer queries are answered by AI chatbots as if it were a real person. In order to do this, the AI chatbot uses a combination of machine learning algorithms and specifically programmed scripts of appropriate responses.
Rules based chatbot technology
AI chatbots have natural language understanding so that they can answer via human like conversations, When a user asks a question of an AI chatbot, it will use a vast database of previously input knowledge.
The whole idea is that, because the AI chatbot uses human language, it can answer even complex queries, depending on how much data it’s working from. Statistics show that many customers prefer using virtual agents as it speeds up their customer journey.
Should the chatbots come up against concepts or questions that they have no previous experience with, then they pass over to a human agent. Using structured data, AI chatbots learn from each interaction and are able to predict user intent during future interactions.
This all means that, while chatbots follow an initial set of data and knowledge base, the user’s input allows the chatbot to expand its reach and capabilities via the AI algorithm.
The two AI chatbot categories
Different sectors have different reasons for using an AI chatbot with natural language processing. For example, a retail or ecommerce website generally wants a chatbot to be able to help customers place an order while a communications business will need an AI chatbot to respond to specific customer service queries.
In order to facilitate these different requirements, businesses will choose from the two main categories of web-based chatbots;
An artificial intelligence-based chatbot comes pre-loaded with its own artificial artificial neural network or intelligence.
Chatbots work by being trained using machine learning algorithms and is able to understand the language used as well as the order itself. And, as the artificial intelligence markup language allows it to develop and improve from each customer support interaction, it gets better and better. AI based chatbots can identify context, intent and language and then choose how to react using natural language processing (NLP).
Rule based chatbots
Rule based chatbots don’t have the complex neural networks and deep learning abilities of AI chatbots. Instead, they work from a limited range of predefined rules and data. This means these kinds of computer programs are simpler to build.
Rule based bots hold conversations that are tightly controlled by the pre-defined knowledge base. This means there is no way for it the chatbot to demonstrate the deep learning that comes with an AI chatbot.
Rather than machine learning, rules based chatbots use these predefined rules to answer simple questions. Without the artificial neural networks inherent in AI and machine learning chatbots, it’s a much simpler approach.
Customers may find that a standard virtual assistant on an ecommerce site will be a rules based chatbot, which can only simulate human conversation in the most basic terms. These are commonly used for customer support and lead generation and in messaging applications.
Chatbots and chatbot architecture
The chatbot architecture is what allows it to work, and while there are different types depending on why a company wants to build chatbots, the main flow of the way the chatbot works is largely the same.
The first component of a chatbot is a question and answer (Q&A) system. This uses training data to answer FAQs from customers. In this way it’s ideal for basic customer support and can work alongside a support team.
When the question is asked, the way the chatbot replies depend on its knowledge base, which consists of automated training and manual training. The latter is where the person building the computer program (a human operator), sends policy documents and data to the chatbot and then programs it to train itself.
From the input sentences, the chatbot generates its own list of questions and answers and uses those. The former (manual training) is where the programmer compiles a list of Q&As and enables the chatbot to identify which it needs at every query.
Natural language processing (NLP)
The NLP Engine is central to the chatbot work, as it interprets the queries that are being submitted to the chatbot. After interpreting the input it performs sentiment analysis so that it can organise it into something the chatbot can go ahead and process. The NLP uses advanced machine learning to do this. It can also be programmed to include a feedback mechanism, which allows for better deep learning.
What does the front-end of a chatbot look like?
The platform that allows the user to interact with the chatbot is always client facing. It could be Facebook Messenger, a website, a mobile app, Apple’s Siri or WhatsApp, for example.
The business use cases for chatbots are clear. Chatbots help businesses by automating systems and acting as virtual assistants.
Chatbots can help to acquire new leads, communicate with existing clients, answer self service FAQs, and ask qualifying questions of users. Their other main functionality is within customer support, as the chatbot can provide answers immediately.
More complex queries are then passed on by the chatbot to a human customer service agent.
Benefits of chatbots and conversational AI chatbots for businesses
There are a number of reasons why businesses are increasingly utilising chatbots:
- Chatbots deliver myriad financial benefits to businesses.
- Chatbots can be scaled to interact with limitless numbers of customers and users.
- Chatbots can automate functions that take up a lot of time for human teams. For example, a chatbot can be used as a marketing automation tool.
- Chatbots are fully customisable and can offer bespoke customer service using human language.
- Chatbots are extremely efficient and, those that use conversational AI can create two-way dialogue.
The majority of businesses in 2023 have a website, social media presence and other online platforms. Custom designed chatbots can really elevate their online presence in order to communicate with their target demographic.
We’re likely to see the use of chatbots continue to increase across multiple platforms and for multiple business uses. As the technology continues to improve, they are likely to become ever more sophisticated.