Chatbots are software-based assistants that converse via voice or text. They’re made to sound like genuine people and allow consumers to engage with them in real-time. Chatbots, for example, can answer frequently asked questions, assist with sales and customer service, assist with the processing of an insurance claim, or ask you health-related queries. By automating typical, repetitive operations in support, training, and education, chatbots provide immediate benefits to businesses.
Conversational AI, which entails leveraging chatbots, speech-based assistants, and instant messaging to create tailored, automated consumer experiences, includes chatbots as a significant component. In this blog, we have discussed different types of Chatbots and how they have played a role in many industries with live examples.
Types of AI-Based Chatbots
We can divide chatbots into two types based on how they are programmed: Rule-Based (dumb bots) and Self-Learning (smart bots).
Decision-tree bots or rule-based chatbots respond to questions based on what they have been taught. They follow a set of rules, as the name suggests. These guidelines serve as the foundation for the types of problems that the chatbot is familiar with and can solve.
Rule-based chatbots plot out talks like a flowchart. They do this in advance of a customer’s question and how the chatbot should answer.
Simple or complex rules can be used in rule-based chatbots. Rule-based chatbots perform and work well in the scenarios for which you have prepared them.
To speak with consumers, these bots use Artificial Intelligence (AI) and Machine Learning (MI) technology. Self-learning Retrieval-based and Generative chatbots are the two types of chatbots.
Retrieval-based bots are programmed to rank the best response from a limited selection of options. The responses are either manually entered or based on a knowledge base of previously collected data.
For example, what are your store hours?
9 a.m. to 5 p.m.
These systems can also be extended to work with third-party systems.
For example, where is my order?
Answer: It’s on its way to you and should arrive in 10 minutes.
The most prevalent types of chatbots that you see nowadays are retrieval-based bots. They enable bot developers and UX designers to take control of the experience and tailor it to clients’ needs. They’re great for customer service, lead generation, and feedback bots with specific goals. We can set the bot’s tone and create the experience while keeping the customer’s brand and reputation in mind.
These chatbots don’t have pre-programmed responses. Instead, they are taught from a vast number of past discussions, from which they develop responses to the user. To train, they need a vast volume of conversational data.
For conversational chatbots with whom the user merely wants to have a good time, generative models are ideal. These models will almost always be able to provide you with a response.
Intellectually Independent Chatbots
Bots that are built on machine learning are known as intellectually autonomous chatbots. These rely on feeding a neural network with thousands, if not millions, of examples of what the bot needs to be capable of understanding in order to teach it to “think” for itself. They get better with time and are primarily utilized for entertainment and science.
Chatbots driven by machine learning is designed to comprehend consumers’ requests and inputs completely (with some training in the beginning). These bots learn on their own over time by detecting similar terms and become less reliant on training. This is why they’re termed “intellectually independent chatbots”.
Contextual chatbots are a step up from standard chatbots. Their main goal is to figure out what the user is trying to accomplish, such as in what sense or proportion the user is asking a query or performing random things on the website. These chatbots remember what a user has already requested or done in the past and provide a more intelligent response as a result.
For example, the chatbot may inquire about a certain food preference. The user then selects a food type or requests a recommendation. The data from the first input, namely the type of restaurant, is preserved as a context in both circumstances.
When a user places an order for pizza, another type of contextual chatbot is used. The bot will not ask the user the same basic inquiry if he has already provided his location and preferences. It will just ask for confirmation, and that’s all there is to it! The order has been placed and is being delivered.
Contextual chatbots require a large amount of data and a broad knowledge base for training in order to be more versatile. These bots learn as much as they can from the user’s utterances and previous user journeys on the website or mobile app during the discussion. It becomes capable of predicting possible actions to do in real-time after going through all of these.
Benefits of Chatbots in various industries
According to GlobeNewswire, by 2027, the market for healthcare chatbots is estimated to be worth $624.4 million.
In healthcare, a chatbot or digital personal assistant assists physicians, nurses, patients, and their families. Patients can use healthcare chatbots to connect with professionals for diagnosis or treatment. Furthermore, in the future, patients are likely to turn to chatbots for answers to their health-related questions, as these will relieve patients by removing the need for them to visit nurses or other medical professionals.
Woebot has raised $90 million as one of the only apps in the multibillion-dollar mental health sector to incorporate artificial intelligence. According to research, its therapeutic chatbot can establish a true rapport with its 36,000 users in as little as three to five days.
With the launch of H&M’s chatbot on Kik, a big new trend emerged in 2016. The recommendations and user experience were well received by a large number of customers. Customers could choose from a variety of recommendations, filters, and style preferences as part of the experience. They might use the Kik app to share product sites and favorite goods with their contacts.
Southwest Airlines bot can answer inquiries about open opportunities and interview candidates about their qualifications, wage rates, and talents. It used to take the airline up to 45 days after posting a job opening to offer a candidate a job. The recruiting department needs assistance with over 2,000 job opportunities. The chatbot can help shorten the time it takes to hire someone in half. The bot has interacted with over 1.2 million job candidates since its introduction, which would have taken human recruiters 18,000–92,000 hours to process.
In 2017, Elisa, a renowned Northern European telecoms operator, launched Annika, the first customer service bot that integrated product management with customer support.
This artificial intelligence-based chatbot has helped the organization resolve more customer encounters, meet evolving customer expectations, and create synergies between product management and customer care.
The chatbot does not replace human employees; rather, it frees them up to focus on higher-value client interactions by removing routine requests and boring, repetitive activities.
The future chatbot will be more than simply a customer service representative; it will also serve as an advanced helper for both businesses and consumers.
Humans generally don’t like doing tedious, repetitive chores. As a result, Chatbot adoption has risen in the last two years. With the increase of AI Chatbots, businesses can use their human resources for more innovative projects. As a result, businesses are improving employee and customer satisfaction thus resulting in expected business outgrowth.