Types of Chatbots. Rule-Based Chatbots vs AI Chatbots
Konstantin Sadekov | 29. September 2020
The global chatbot market is going to reach $9.4 billion by 2024. Hundreds of companies worldwide develop various types of chatbots that mostly intend to help businesses to improve their clients’ experience and decrease customer service costs. A “big market pie” motivates companies to develop new solutions. However, some of these are fairly outdated to serve actual business needs in 2020. This article will help you understand what the different types of chatbots are, what these are used for, and which ones could have the biggest value to you.
Rule-based chatbots vs AI Chatbots
Rule-based chatbots became very popular after Facebook launched its Messenger platform where chatbots allowed businesses to perform automated customer support. Such types of chatbots are used for answering simple questions, for instance, when it comes to booking a table in a restaurant, buying tickets to the cinema, or using online delivery services. Guided by a decision tree, the customer is given a set of predefined options that lead to the desired answer.
Rule-based chatbots are often split into two tracks: a sales track for capturing contact details, setting up a call or a meeting; and a support track for giving generic answers or sending a website link containing the necessary information. In most cases, these types of chatbots are built with a graphical interface reacting to the user pressing a specific button that activates the next layer of the decision tree. Often, these types of chatbots are based on keyword rules, reacting on specific words but these are limited to typos and have a risk of providing wrong answers or causing very frustrating customer experiences.
AI chatbots, on the other hand, use natural language processing (NLP) technologies to understand the intent behind the question and solve the customer’s problem without any human assistance. The biggest difference with the rule-based chatbot is the usage of the machine learning models that significantly increases the functionality of the bot as it is able to identify hundreds of different questions written by a human.
A customer can ask the bot a question by writing it in the same way as if asking a human agent. The bot uses a text classifier to identify the intent and understand the meaning behind the question. The next step involves creating a sequence of additional questions and answers using the dialogue tree that helps to specify the exact issue the customer wants solved and the way it will be done. Besides that, API integration with the back end systems allows the bot to really perform the task for the customer, instead of just providing a link to self-service instructions. NLP models help an AI chatbot to identify hundreds of different questions making it very useful not only for big enterprises in branches like Telecommunication, Finance and Banking sectors, but also the Public sector, as it is able to serve thousands of customers automatically and perform complicated tasks on its own.
Are NLP-based types of AI Chatbots better than rule-based ones?
It is not right to say that rule-based chatbots are worse, they are just different. They are much simpler, have a specific usage and are more affordable. In contrast, AI chatbots are used for more complicated cases to fully resolve customers’ issues. Also, rule-based bots are limited by typos or wrong keywords that people might use. Another problem with rule-based chatbots is that there is a limited amount of questions that such a bot can handle because it uses decision trees; it has a limited amount of questions that a client can navigate through. Clients can get lost when it comes to more complicated services than just ordering a pizza. And finally, NLP technology has become easily accessible for every company independent of its size. This is why further in this article, we are going to discuss the business value of various AI chatbot implications that are based on NLP technology.
The business value of different types of AI chatbots
AI chatbots have a more significant value, as they can automate a bigger number of questions, require less data for training (per each question it is trained to understand) and can solve more complicated issues compared to the rule-based chatbots. However, there are also different implications of the AI bot depending on the complexity of the use case.
The given illustration is based on the actual experience of one of our biggest Telco clients in the Nordics – Elisa, that shared their AI chatbot use case and compared first contact resolution rate (FCR) in different project stages. As for their experience, a basic bot that uses NLP and handles simple requests, provides only 4% of the total business value that an AI chatbot can provide in maximum. Basic type of AI bot understands the meaning behind the questions, answers simple ones and sends informational messages, for example, a link to the FAQ section on the website.
The biggest business value comes from a personalized and transactional type of chatbot that can personalize messages and perform the tasks automatically for the customer. Customer verification and API integration with knowledge bases allow the bot to see client’s historical data and better understand their specific problem. Thus the bot’s response is personalized and it can even perform a task for the client, e.g., activate roaming. Also, using improved NLP algorithms allows the bot to reach 92% text classification accuracy (e.g., the bot classifies correctly 9 out of 10 client questions that the bot has been trained to understand) by confirming the intent with the customer. If a customer asks a question and the bot does not have a high enough confidence level to make a prediction on the client’s intent, the bot asks a specifying question, e.g., “Would you like to change billing information or personal information?”
There are several additional AI models that can be integrated into a chatbot so that it makes the bot much more powerful and intelligent. Experimenting with other models like next-best-offer (NBO) adds a possibility to recommend the customer the best matching products or services based on either previous interactions with products or services, user profiling based on similar customer segments, or behaviour on the website. From experience, this way an AI chatbot gives an additional 10% of business value.
The last step is the implementation of natural language conversations. From our experience, this influences business value the least, as businesses are not ready to pay extra for a feature that customers simply do not need. Clients do not value a natural and friendly conversation with a bot, they simply want their problem to be solved as soon as possible.
According to Businessinsider, 44% of US customers prefer chatbots over humans in customer service. This trend is in line with the habit of instantly finding information by just Googling it. When it comes to customer service, people have the same expectations. As technology continues to improve more and more users will be willing to use chatbots in their everyday life as it helps to save time and solve their issue without delay.
This article aimed to help understand the 2 main types of chatbots: rule-based and AI chatbots. The latter have a much more complicated functionality that requires less training data and that can actually perform the task for the customer without any human assistance. Also, there are different ways for implementing the AI bot depending on the use case. Hopefully, now you know enough and are ready to implement one of the bots in your own business. Book a Demo with us to learn more about our AI chatbot software or download an AI use case from the Telecommunication industry below.
Konstantin has graduated from the Estonian Business School major in economics and finance and is currently doing his MBA degree in the USA. Before joining MindTitan he had an international business management experience for more than 5 years and overall more than 9 years of international B2B sales and marketing experience.
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