6 types of chatbots and how to choose the right one for your business

Different types of chatbot technology play an increasingly prevalent role in our lives today, from how we receive customer support or purchase a product to how we handle our routine tasks. Many of us have interacted with these chatbots or virtual assistants on our phones, messaging apps or through home devices—such as Apple’s Siri, Amazon’s Alexa and Google Assistant. You might also encounter them through SMS text messaging, social media or workplace messenger applications.

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 touch on different types of chatbots with various degrees of technological sophistication and discuss which makes the most sense for your business and chatbot solutions. Before addressing these questions, let’s start with the basics of how chatbots work.

A chatbot is a conversational tool that seeks to streamline customer queries and respond automatically, simulating written or spoken human conversations. Some chatbots are rudimentary, presenting simple menu options for users to click. However, more advanced chatbots can use artificial intelligence (AI) and natural language processing (NLP) to understand a user’s input and manage complex human conversations with ease. 

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

These chatbots offer simple functionality and are useful for answering repetitive, straightforward questions, but they struggle with more nuanced requests due to their limited predefined answer options. They might take longer to understand customer needs, especially if users must go through several iterations of menu buttons before reaching the final option. Also, if a user’s need isn’t listed as a menu option, the chatbot will be useless because it lacks a free text input field.

Building upon the menu-based chatbot’s simple decision tree functionality, the rule-based chatbot employs conditional “if, then” logic to develop conversation automation flows. The rule-based bots essentially act as interactive frequently asked questions (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 predefined common questions, such as about pricing or features. However, like the rigid menu-based chatbots, these chatbots fall short when faced with complex queries. They struggle to answer questions that haven’t been predicted by the conversation designer, as their output depends on the prewritten content programmed by the chatbot’s developers.

Because conversation designers can’t preprogram chatbots for every possible query, the limited rule-based chatbots often get stuck when they can’t understand the user’s request. It then misses important details and asks the user to repeat previously shared information, resulting in a frustrating user experience. Often, the chatbot transfers the user to a live support agent, but if that transfer isn’t enabled, the chatbot ends up acting as a gatekeeper, further frustrating the user.

While the rule-based chatbot’s conversational flow supports only 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 algorithms underpinning AI chatbots allow them to self-learn and develop an increasingly intelligent knowledge base of questions and responses that are based on user interactions. With deep learning, the longer an AI chatbot operates, the better it can understand user goals and provide more detailed, accurate responses, as compared to a chatbot with recently integrated algorithm-based knowledge.

Conversational AI chatbots can remember conversations with users and incorporate this context into their interactions. When combined with automation capabilities such as robotic process automation (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. The chatbot can greet them by 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. 

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 several factors. These factors include your technology stack and development tools, the complexity of the chatbot, the wanted features, the data availability and whether it needs to integrate with other systems, databases or platforms. With a user-friendly, no-code or low-code platform, you can build AI chatbots faster. 

With IBM® watsonx Assistant™, chatbots can be trained on little data to correctly understand the user. 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, The Total Economic Impact™ Of IBM Watson Assistant, IBM’s low-code or no-code interface enables a new group of nontechnical employees to create and improve conversational AI skills. The composite organization experienced productivity gains by creating skills 20% faster than if done from scratch.

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 might find the interactive voice response (IVR) technology frustrating, especially when it fails to retrieve the requested information from the preprogrammed menu options and puts the user on hold. However, this system is evolving with artificial intelligence and improving customer satisfaction.

AI-driven 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 integration 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.

Chat and voice bots both aim to identify user needs and provide helpful responses. However, 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.

The next generation of chatbots with generative AI capabilities can offer even more enhanced functionality. They are fluent in understanding common language, can adapt to a user’s style of conversation and use empathy when answering users’ questions. While conversational AI chatbots can digest a user’s questions or comments and generate a human-like response, generative AI chatbots can go a step further by generating new content as the output.

This new content could look like high-quality text, images and sound that are based on LLMs they are trained on. New chatbot software can provide personalized experiences to users and help support teams reach more customers quickly. 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.

A hybrid chatbot is a conversational AI system that combines rule-based logic with machine learning capabilities. The combination of both can deliver a versatile user experience that handles a range of tasks varying in difficulty due to the integration of AI technology.

Rule-based chatbots function on a set of predefined rules and scripts and therefore provide structure, while AI has learning potential for more complex interactions. The hybrid chatbot provides the best of both systems in one single system and delivers a vast user experience that is straightforward and personalized.

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 dialog for nuanced questions? 

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 or a start-up, with limited active users and a few FAQs, a simpler chatbot might suffice. In this case, a rule-based or keyword recognition-based chatbot can address your business needs and satisfy customers without requiring significant effort.

However, for medium to large companies with extensive user data that a chatbot could self-learn from, an AI chatbot can provide detailed, accurate responses to users and enhance customer experiences. One example is its use in the healthcare and pharmaceuticals industry, where it can help patients schedule appointments and manage prescription pickups.

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