By The term “chatbot” refers to a computer program that simulates and interprets human interaction (spoken or written), enabling users to converse with digital gadgets as if talking to real people. A chatbot can be as simple as one-line software that answers simple queries or as complicated as a digital assistant that learns and grows over time to offer services that are ever more customized as they gather and analyze more data.
The AI and data that power chatbots contain both advantages and limitations. Chatbots can help users quickly discover the required information by replying to their inquiries and requests through text input, audio input, or both without human intervention.Nowadays, chatbot technology is practically ubiquitous, from home smart speakers to business messaging platforms. Modern AI chatbots are frequently called “virtual assistants” or “virtual agents.” They can communicate with you via text messages or voice assistants like Apple’s Siri, Google Assistant, and Amazon Alexa. In either case, you can talk with the chatbot to ask it questions about what you need.What are the different types of chatbots?
- Menu/button-based chatbots
- Linguistic Based (Rule-Based Chatbots)
- Keyword recognition-based chatbots
- Machine Learning chatbots
- The hybrid model
- Voice bots
1. Menu/button-based chatbots
The most basic form of chatbots now used on the market are menu/button-based. These chatbots are typically just fancy decision tree hierarchies that appear to the user as buttons. These chatbots demand the user to make several decisions to delve deeper and get at the ultimate solution, much like the automated phone menus we all deal with almost daily.
While these chatbots are adequate for handling FAQs, which account for 80% of support requests, they need to catch up in more complex situations where there are too many variables or too much information at stake to forecast how users should arrive at certain responses confidently. Additionally, it’s important to note that menu- and button-based chatbots are the slowest when receiving user values.
2. Linguistic Based (Rule-Based Chatbots)
A multilingual chatbot might be your answer if you anticipate your client’s inquiries. Conversational automation flows are created by linguistic or rule-based chatbots employing if/then logic. You must first specify the language requirements for your chatbots. Conditions can be constructed to evaluate the words, their placement in a sentence, synonyms, and other factors. Your consumers can quickly get the help they need if the incoming inquiry meets the criteria set by your chatbot.
However, it is your responsibility to ensure that every variation and pairing of every question is defined; otherwise, the chatbot won will need to help your customers are saying. Because of this, language models, despite being quite widespread, might take time to develop. These chatbots require precision and stiffness.
3. Keyword recognition-based chatbots
In contrast to chatbots that employ menus, those that use keyword recognition can hear what users are typing and answer appropriately. These chatbots decide how to respond to the user using programmable keywords and an AI tool called Natural Language Processing (NLP).
When asked a lot of identical queries, these chatbots struggle. Chatbots will start to falter when there are keyword overlaps across numerous related questions.
Examples of chatbots that combine menu/button-based and keyword recognition-based functionality are frequently seen. Suppose the keyword recognition functionality produces subpar results or the user needs assistance finding the answer. In that case, these chatbots allow users to ask their inquiries directly or use the chatbot’s menu buttons.
4. Machine Learning chatbots
You may be wondering what a contextual chatbot is. A contextual chatbot is far more sophisticated than the previous three chatbots. These chatbots employ artificial intelligence (AI) and machine learning (ML) to recall discussions with particular users to learn and develop over time. In contrast to keyword recognition-based bots, chatbots with contextual awareness are intelligent enough to improve themselves based on users’ questions and how they ask them.
Consider a contextual chatbot that enables users to order food; the chatbot will learn the user’s preferences by storing the data from each discussion. As a result, whenever a user speaks with this chatbot over time, it will remember their most frequent order, delivery address, payment information, and other details.
Even if this example of buying food is simple, it is still clear how effective conversation context can be when used with AI and ML. Any chatbot’s ultimate objective should be to deliver a better user experience than the status quo alternative. One of the easiest ways to speed up operations like these with a chatbot is to use conversation context.
5. The hybrid model
Businesses adore the sophistication of AI chatbots, but they sometimes need more skills and massive amounts of data to support them. They choose the hybrid design as a result. The hybrid chatbot paradigm combines the simplicity of rules-based chatbots with the complexity of AI bots to provide the best of both worlds.
6. Voice bots
Businesses are already adopting voice-based chatbots or voice bots to make conversational interfaces even more casual. Voice bots have become increasingly popular over the past few years, with virtual assistants like Apple’s Siri and Amazon’s Alexa leading the way. But why? owing to the convenience they offer. For a consumer, speaking is far simpler than typing. Direct access to frictionless experiences is provided through a voice-activated chatbot.
Why AI and data matter when it comes to chatbots
The AI and data that power chatbots contain both their advantages and disadvantages.
AI considerations: AI is excellent at automating tedious and repetitive tasks. Often, a chatbot performs well when AI is used for these jobs. A chatbot may need help if a request goes beyond what it can handle or makes the work more difficult, which is bad for businesses and customers. Chatbots may only sometimes be able to respond to or handle certain inquiries or problems, such as complex service problems with numerous variables.
Developers can get around these restrictions by including a contingency in their chatbot program that directs the user to another resource (like a live agent) or asks the user for a different inquiry or problem. Some chatbots can easily switch from being a chatbot to a live agent and back again. As AI technology and implementation advance, chatbots and digital assistants will be more smoothly integrated into our daily lives.
Data considerations: Data is accessed from various sources and used by all chatbots. The data will be a chatbot enabler if it is high quality and properly designed. The chatbot’s functionality will be constrained if the data quality is subpar. Additionally, even if the data quality is high, if the chatbot’s machine learning (ML) training was improperly modeled or unsupervised, the chatbot may behave poorly, or at the very least, surprisingly.
The Future of Chatbots
Where is the Future of chatbot development going? Like other AI tools, chatbots will improve human capacities and free people to be more inventive and creative while spending more time on strategic rather than tactical tasks.
Businesses, employees, and customers will benefit from improved chatbot capabilities shortly when AI is paired with the development of 5G technology, such as quicker recommendations and predictions and simple access to high-definition video conferencing from within a discussion. These and other possibilities are still under investigation, but as internet connectivity, AI, NLP, and ML develop, they will improve swiftly. By the time everyone has a fully functional personal assistant in their pocket,
