The chatbot for banking would be completely different from one on an e-commerce website. Chatbots differ from one another in look and activity much like we do in our personalities and skills.

Here are the different types of chatbots:

  • Menu/button-based chatbots

The most fundamental form of chatbots now used on the market is menu/button-based ones. These chatbots are typically just complicated decision tree hierarchies that appear to the user as buttons. These chatbots demand the user to make a number of decisions in order to dig deeper and arrive at the ultimate answer, much like the automated phone menus we all engage with on an almost daily basis.

While these chatbots are adequate for handling FAQs, which account for 80% of support requests, they fall short in more complex situations where there are too many variables or too much information at stake to confidently forecast how users should arrive at certain responses. Additionally, it’s important to note that menu- and button-based chatbots are the slowest in guiding users to their desired values.

  • Linguistic Based (Rule-Based Chatbots)

A multilingual chatbot might be the answer for you if you can anticipate the kinds of inquiries your clients might ask. 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. To evaluate the words, their placement in a sentence, synonyms, and other factors, conditions can be constructed. Your consumers can quickly get the help they need if the incoming enquiry meets the criteria set by your chatbot.

However, it is your responsibility to make sure that every variation and pairing of every question is defined; otherwise, the chatbot won’t be able to grasp what your customers are saying. Because of this, language models, despite being quite widespread, might take a time to develop. These chatbots require precision and stiffness.

  • Keyword recognition-based chatbots

In contrast to chatbots that use menus, those that use keyword recognition can hear what users are typing and answer appropriately. These chatbots decide how to respond to the user by using programmable keywords and an AI tool called Natural Language Processing (NLP).

When asked a lot of queries that are identical, these chatbots struggle. When there are keyword overlaps across numerous related inquiries, NLP chatbots will start to fail.

Examples of chatbots that combine menu/button-based and keyword recognition-based functionality are frequently seen. These chatbots give users the option to try asking their queries directly or use the menu buttons if the keyword recognition functionality is not working well or if they need some assistance finding the answer.

  • Machine Learning chatbots

You may be wondering what a contextual chatbot is. In comparison to the previous three chatbots, a contextual chatbot is far more sophisticated. These chatbots employ artificial intelligence (AI) and machine learning (ML) to recall discussions with particular users in order to learn and develop over time. Chatbots with contextual awareness, in contrast to keyword recognition-based bots, are intelligent enough to improve themselves based on the questions that users ask and the manner in which 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, over time, whenever a user speaks with this chatbot, it will eventually remember their most typical order, their delivery location, and their payment details and will only prompt them to repeat this transaction. The user only needs to say “Yes” to start the food, rather than answering a number of questions.

Even if this example of buying food is simple, it is still clear how effective conversation context can be when used in conjunction with AI and ML. Any chatbot’s ultimate objective should be to deliver a better user experience than the alternative of the status quo. One of the easiest ways to speed up operations like these with a chatbot is to use conversation context.

  • Voice bots

Businesses are already starting to adopt 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. The end user has seamless experiences thanks to a voice-activated chatbot.

  • The hybrid model

Businesses adore the sophistication of AI-chatbots, but they sometimes lack the skills and massive amounts of data needed 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.

Which kind of chatbot is best for you, then?

Put yourself in your users’ perspective and consider the value they are attempting to obtain while determining whether a chatbot is the appropriate choice for you. Will the context of the dialogue have a big effect on this value? If not, it probably isn’t now worth the effort and money to implement.