Types of Chatbots - IBM
With the help of NLP and through integrating with computer and telephony technologies, voice chatbots can now understand spoken questions, ...
Types of Chatbots \| IBM




5 types of chatbot and how to choose the right one for your business





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Artificial Intelligence





5 September 2023





6min read

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Now, more than ever, different types of chatbot technology plays an increasingly prevalent role in our lives, from how we receive customer support or decide to purchase a product to how we handle our routine tasks. Many of us have interacted with these chatbots or virtual assistants on our phones or through devices in our homes—such as Apple’s Siri, Amazon Alexa and Google Assistant. You may have interacted with these chatbots via SMS text messaging, social media or with messenger applications in the workplace.

Chatbots have made our lives easier by providing timely answers to our questions without the hassle of waiting to speak with a human agent. In this blog, we’ll touch on different types of chatbots with various degrees of technological sophistication and discuss which makes the most sense for your business. Before addressing these questions, we’ll start with the basics.

Chatbots explained

A [chatbot](https://www.ibm.com/topics/chatbots) is a conversational tool that seeks to understand customer queries and respond automatically, simulating written or spoken human conversations. As you’ll discover below, some chatbots are rudimentary, presenting simple menu options for users to click on. However, more advanced chatbots can leverage [artificial intelligence](https://www.ibm.com/topics/artificial-intelligence) (AI) and [natural language processing](https://www.ibm.com/topics/natural-language-processing) (NLP) to understand a user’s input and navigate complex human conversations with ease.

What are the different types of chatbot?

### 1\. Menu or button-based chatbots

Menu-based or button-based chatbots are the most basic kind of chatbot where users can interact with them by clicking on the button option from a scripted menu that best represents their needs. Depending on what the user clicks on, the simple chatbot may prompt another set of options for the user to choose until reaching the most suitable, specific option. Essentially, these chatbots operate like a decision tree.

Although these chatbots offer simple functionality and can be helpful for answering users’ repetitive, straight-forward questions, these chatbots may struggle when faced with more nuanced requests because they are limited to pre-defined answer options. First, this kind of chatbot may take longer to understand the customers’ needs, especially if the user must go through several iterations of menu buttons before narrowing down to the final option. Second, if a user’s need is not included as a menu option, the chatbot will be useless since this chatbot doesn’t offer a free text input field.

### 2\. Rules-based chatbots

Building upon the menu-based chatbot’s simple decision tree functionality, the rules-based chatbot employs conditional if/then logic to develop conversation automation flows. The rule-based bots essentially act as interactive FAQs where a conversation designer programs predefined combinations of question-and-answer options so the chatbot can understand the user’s input and respond accurately.

Operating on basic keyword detection, these kinds of chatbots are relatively easy to train and work well when asked pre-defined questions. However, like the rigid, menu-based chatbots, these chatbots fall short when faced with complex queries. These chatbots struggle to answer questions that haven’t been predicted by the conversation designer, as their output is dependent on the pre-written content programmed by the chatbot’s developers.

Because it’s impossible for the conversation designer to predict and pre-program the chatbot for all types of user queries, the limited, rules-based chatbots often gets stuck because they can’t grasp the user’s request. When the chatbot can’t understand the user’s request, it misses important details and asks the user to repeat information that was already shared. This results in a frustrating user experience and often leads the chatbot to transfer the user to a live support agent. In some cases, transfer to a human agent isn’t enabled, causing the chatbot to act as a gatekeeper and further frustrating the user.

### 3. AI-powered chatbots

While the rules-based chatbot’s conversational flow only supports predefined questions and answer options, AI chatbots can understand user’s questions, no matter how they’re phrased. With AI and natural language understanding (NLU) capabilities, the AI bot can quickly detect all relevant contextual information shared by the user, allowing the conversation to progress more smoothly and conversationally. When the AI-powered chatbot is unsure of what a person is asking and finds more than one action that could fulfill a request, it can ask clarifying questions. Further, it can show a list of possible actions from which the user can select the option that aligns with their needs.

The [machine learning](https://www.ibm.com/topics/machine-learning) algorithms underpinning AI chatbots allow it to self-learn and develop an increasingly intelligent knowledge base of questions and responses that are based on user interactions. With [deep learning](https://www.ibm.com/topics/deep-learning), the longer an AI chatbot has been in operation, the better it can understand what the user wants to accomplish and provide more detailed, accurate responses, as compared to a chatbot with a recently integrated algorithm-based knowledge.

[Conversational AI](https://www.ibm.com/topics/conversational-ai) chatbots can remember conversations with users and incorporate this context into their interactions. When combined with automation capabilities like [robotic process automation](https://www.ibm.com/topics/rpa) (RPA), users can accomplish tasks through the chatbot experience. For example, when ordering pizza, the restaurant’s chatbot can recognize a loyal customer returning to place an order, greet them by their name, remember their “regular” order, and use their saved delivery address and credit card to complete the order. Being deeply integrated with the business systems, the AI chatbot can pull information from multiple sources that contain customer order history and create a streamlined ordering process.

Additionally, if a user is unhappy and needs to speak to a human agent, the transfer can happen seamlessly. Upon transfer, the live support agent can get the chatbot conversation history and be able to start the call informed.

The time it takes to build an AI chatbot can vary based on factors such as the technology stack and development tools you are using; the complexity of the chatbot; the desired features; the data availability; and whether you need it to integrate with other systems, databases or platforms. With a user friendly, no-code/low-code platform you can build AI chatbots faster.

With [watsonx Assistant](https://www.ibm.com/products/watsonx-assistant), chatbots can be trained on little data to correctly understand the user, and they can be enhanced with search capabilities to sift through existing content and provide answers that address questions beyond what was initially programmed by the chatbot conversation designer.

IBM watsonx Assistant accelerates the deployment of virtual agents by providing:

- Improved intent recognition from using large language models (LLMs) and advanced NLP and NLU
- Built-in search capabilities
- Starter kits or built-in integrations with channels, third-party apps, business systems or Contact Center as a Service platforms, such as Nice CXone

According to the [2023 Forrester Study](https://www.ibm.com/account/reg/signup?formid=urx-43183) _The Total Economic Impact™ Of IBM Watson Assistant_, IBM’s low-code/no-code interface enables a new group of non-technical employees to create and improve conversational AI skills. The composite organization experienced productivity gains by creating skills 20% faster than if done from scratch.

### 4. Voice chatbots

A voice chatbot is another conversation tool that allows users to interact with the bot by speaking to it, rather than typing. Some voice chatbots can be more rudimentary. Some users may be frustrated by the [Interactive Voice Response (IVR)](https://www.ibm.com/topics/interactive-voice-response) technology they’ve encountered, especially when the system can’t retrieve the information a user is looking for from the pre-programmed menu options and puts the user on hold. However, this system is evolving with artificial intelligence.

AI-powered voice chatbots can offer the same advanced functionalities as AI chatbots, but they are deployed on voice channels and use text to speech and speech to text technology. With the help of NLP and through integrating with computer and telephony technologies, voice chatbots can now understand spoken questions, analyze users’ business needs and provide relevant responses in a conversational tone. These elements can increase customer engagement and human agent satisfaction, improve call resolution rates and reduce wait times.

While chat and voice bots both aim to identify the needs of users and provide helpful responses, voice chatbots can offer a quicker and more convenient communication method, as it’s easier to get a real-time answer without typing or clicking through drop-down menu options.

### 5. Generative AI chatbots

The next generation of chatbots with [generative AI](https://research.ibm.com/blog/what-is-generative-AI) capabilities can offer even more enhanced functionality through their fluency in understanding common language, their ability to adapt to a user’s style of conversation and their use of empathy when answering users’ questions. While conversational AI chatbots can digest a users’ questions or comments and generate a human-like response, generative AI chatbots can take this a step further by generating new content as the output. This new content could look like high-quality text, images and sound based on LLMs they are trained on. Chatbot interfaces with generative AI can recognize, summarize, translate, predict and create content in response to a user’s query without the need for human interaction.

What is the right type of chatbot for your business?

When assessing the various types of chatbots and which could work best for your business, remember to place your end user at the center of this decision. What are your users’ goals and their expectations from your business, and what are their user experience preferences for a chatbot? Would they prefer to select from a simple menu of buttons, or would they need the option to correspond in open-ended dialogue for nuanced questions?

Also, consider the state of your business and the use cases through which you’d deploy a chatbot, whether it’d be a lead generation, e-commerce or customer or employee support chatbot. If you’re working for a smaller company, such as a startup, with limited active users and minimal amounts of frequently asked questions that your chatbot conversation designers would need to pre-program, a rules or keyword recognition-based chatbot may sufficiently address your business needs and satisfy customers without much lift.

However, for medium to larger sized companies that house vast amounts of user data that a chatbot could self-learn from, an AI chatbot could be an advantageous solution to provide detailed, accurate responses to users and enhanced customer experiences.

When thinking about generative AI’s impact on chatbots, think about how your business can take advantage of creative, conversational responses and when this technology makes the most sense for your business objectives and the needs of your customers.




Author





Bella ChurchWeb Strategist, IBM watsonx, IBM Blog

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