More than ever, chatbot technology is part of our daily lives, from customer support to product purchases to routine tasks. Siri, Alexa, and Google Assistant are chatbots that many of us have used on our phones or in our homes. You may have interacted with these chatbots via SMS, social media, or workplace messenger apps.
Chatbots make our lives easier by answering questions quickly without waiting for a human. In this blog, we’ll discuss chatbots of different technological sophistication and which ones are best for your business. Start with the basics before answering these questions.
Chatbots instructed
A chatbot mimics human conversations by understanding customer questions and responding automatically. Some chatbots have simple menus, as shown below. However, advanced chatbots can use AI and NLP to understand user input and navigate complex human conversations.
What are chatbot types?
1. Menu or button based chatbots
Menu based chatbots are the simplest, where users click on the button from a scripted menu that suits them. The simple chatbot may prompt more options based on what the user clicks on until they find the best one. The chatbot is a decision tree.
These chatbots can answer simple questions, but their pre-defined answer options may limit their ability to handle complex requests. First, if the user must click several menu buttons before choosing the final option, this chatbot may take longer to understand their needs. Second, if a user’s need isn’t on the menu, this chatbot is useless since it doesn’t have a free text input field.
2. Rules based chatbots
Based on the menu-based chatbot’s simple decision tree functionality, the rules-based chatbot automates conversations using conditional if/then logic. Rule-based bots are interactive FAQs where a conversation designer programs predefined question and answer combinations to understand and respond to user input.
Basic keyword detection chatbots train easily and answer pre-defined questions. Like rigid, menu based chatbots, these struggle with complex queries. Developer-programmed content prevents these chatbots from answering questions the conversation designer didn’t anticipate.
The conversation designer can’t predict and pre-program all user queries, so limited, rules based chatbots often get stuck because they can’t understand the user’s request. The chatbot misses important details and asks users to repeat when it doesn’t understand their request. This frustrates users and often sends them to live support via the chatbot. Sometimes transfer to a human agent isn’t enabled, making the chatbot a gatekeeper and frustrating the user.
3. AI-powered chatbots
Rules-based chatbots only support predefined questions and answer options, but AI chatbots can understand any question. AI and NLU allow the AI bot to quickly detect all relevant contextual information shared by the user, making the conversation more natural. If the AI-powered chatbot doesn’t understand a request and finds multiple actions, it can ask clarification questions. It can also show a list of actions from which the user can choose.
AI chatbots use machine learning algorithms to learn and improve their question and response databases from user interactions. Deep learning allows a longer-running AI chatbot to understand user needs and provide more accurate, detailed responses than a newer one.
Talkative AI chatbots remember and use user conversations. Chatbots can complete tasks with RPA. The restaurant’s chatbot can recognize returning customers, greet them by name, remember their “regular” order, and use their saved delivery address and credit card when ordering pizza. Because it is deeply integrated with business systems, the AI chatbot can pull customer order history from multiple sources and streamline the ordering process.
If a user is unhappy, transferring to a human agent is seamless. The live support agent can see chatbot conversation history and start the call after transfer.
Depending on the technology stack and development tools, the chatbot’s complexity, desired features, data availability, and whether it needs to integrate with other systems, databases, or platforms, building an AI chatbot takes time. User-friendly, no-code/low-code platforms build AI chatbots faster.
Watsonx Assistant trains chatbots on little data to understand users and search content to answer questions beyond what the conversation designer programmed.
- IBM Watsonx Assistant speeds virtual agent deployment:
- Advanced NLP/NLU and large LLMs improve intent recognition.
- Features include built-in search, starter kits, and integrations with third-party apps, business systems, and Contact Center as a Service, such as Nice CXone.
The 2023 Forrester Study The Total Economic Impac of IBM Watson Assistant empowers non-technical employees to learn conversational AI skills through a low-code/no-code interface. Creating skills 20% faster than from scratch increased productivity for the composite organization.
4. Voice chatbots
Users can speak to a voice chatbot instead of typing. Some voice chatbots are simple. IVR technology can frustrate users, especially when it can’t retrieve information from pre-programmed menu options and puts them on hold. AI is changing this system.
Voice chatbots use voice channels and text-to-speech and speech-to-text technology to perform the same functions as AI chatbots. NLP and computer and telephony technologies allow voice chatbots to understand spoken questions, analyze business needs, and respond in a conversational manner. These factors reduce wait times, improve call resolution, customer engagement, and agent satisfaction.
They both identify user needs and provide helpful responses, but voice chatbots can provide real-time answers without typing or clicking through drop-down menus, making them faster and more convenient.
5. Create AI chatbots
Next generation chatbots with generative AI can understand common language, adapt to a user’s conversation style, and answer questions with empathy. Conversational AI chatbots process user input and respond like humans, but generative AI chatbots create new content. This new content may be high-quality text, images, and sound from their LLM training. Generative AI chatbots can recognize, summarize, translate, predict, and create content for user queries without human interaction.
Which chatbot is best for your business?
Choose a chatbot for your business with your end user in mind. Your customers’ goals, expectations, and chatbot user experience preferences? Would they prefer a button menu or open-ended dialogue for complex questions?
Assess your business and chatbot use cases like lead generation, e-commerce, and customer or employee support. If your startup has few active users and few frequently asked questions that your chatbot conversation designers must pre-program, a rules or keyword recognition-based chatbot may satisfy customers without much effort.
For medium to large companies with lots of user data to self-learn from, an AI chatbot could provide detailed, accurate responses and improve customer experiences.
Consider how generative AI in chatbots can provide creative, conversational responses and when this technology makes sense for your business and customers.
[…] Healthcare chatbots and virtual assistants with generative AI This new preview capability in Azure AI Health Bot provides out-of-the-box healthcare intelligence that can be customized and integrated into existing workflows, using answers from a healthcare organization’s content sources and generative AI to provide answers from credible sources like the National Institutes of Health and the FDA. […]