Top 10 Different Types Of Chatbots And How They Work

To the casual observer, all chatbots are similar to each other. They all respond to human inputs and generate helpful outputs.

However, when you delve into the subject in more detail, you find something quite interesting: chatbots are not the same. There’s a whole ecosystem, and they all work differently.

This post explains the different types of chatbots, what they do, and how they might be helpful to your company.

Support Chatbots

Support chatbots provide your customers with helpful information on your website to move them closer to sales. They may offer instructions, marketing materials, or online documentation to help website visitors. 67 per cent of global consumers had at least one interaction with a support chatbot over the last 12 months.

Learning how to add online chat to your website is relatively straightforward. Usually, it requires a small segment of code added to the website HTML. Then, once it’s up and running, site visitors can ask questions at their leisure.

Transactional Bots

Transactional bots have a slightly different focus. Instead of offering generic assistance to customers, these aim to make sales directly.

Transactional bots operate at all levels of the sales funnel including

• Appointment scheduling
• Lead generation
• Outreach and marketing
• Payment collection

Unlike regular sales reps, they’re available twenty-four-seven to drive sales. And because they integrate with other website tools, they streamline transactions and improve the user experience.

AI-Powered Chatbots

In the past, chatbots always gave users pre-programmed responses. However, that’s changing with the advent of AI-powered natural language processing. Now bots can understand people in a similar way to humans.

Practically speaking, this feature means that bots can use the sort of language people use. Rather than feeding the bot pre-programmed words and phrases, customers can ask questions or make statements in many different ways and the chatbot will still understand.

AI-powered chatbots are critical components of customer relationship management (CRM) tools. Natural language processing lets these bots collect prospects’ details more seamlessly and feeds them into databases for reps and marketers. Agents can then see the users’ purchase history to provide them with better advice.

Rules-Based Chatbots

Rules-based chatbots are essentially the opposite. These get customers to ask specific questions (usually using a tree-decision sequence) and then give replies accordingly.

With that said, some of these bots are more sophisticated. For instance, many can evaluate words and sentences and provide customers with stock responses.

Keyword-Based Chatbots

Keyword-based chatbots use a slightly different system based on keywords. They watch out for specific phrases to trigger particular responses, usually directing customers to the most suitable area of the website.

These chatbots work well because they combine natural language processing with keyword recognition. However, they can falter if the same keyword has the potential to generate more than one response.

Fortunately, if they do fail to provide sensible direction, the customer can take over. Most chatbots offer users the option of entering commands directly. They can then access the content they want via clickable buttons.

Skills Chatbots

A skills chatbot is a type of chatbot that can perform a range of tasks on behalf of the user. Coders build these capabilities into their software, letting them do things that standard chatbots can’t.

You can think of a skills chatbot as being a bit like a personal assistant. Just ask it to do something and it’ll follow your instructions. For instance, skills chatbots can tell you what the weather will be like today, order shopping online or switch off home appliances.

The skill of these bots depends entirely on the source code. Some ecosystems have tremendous functionality while others are more basic.

Voice Bots

The vast majority of chatbots use text. However, some also incorporate voice. Here, bots speak to users instead of texting their responses.

Again, artificial intelligence, natural language understanding and machine learning technologies help with this. The software recognises incoming speech and then uses statistical models and neural networks to generate the optimal response. Both speech-to-speech and text-to-speech are available.

Voice bot technology improves all the time. Early on, artificial voices sounded odd and, sometimes, coarse. But today, the software is so sophisticated that most people struggle to determine between AI-generated sounds and regular people.

Hybrid Chatbots

Hybrid chatbots are essentially customer service bots paired with human representatives. Bots answer easy questions and troubleshoot issues while regular agents wait in the wings to deal with more complex and nuanced issues.

Hybrid bots, though, aren’t dummies. Like many other bots, they use AI to determine customers’ problems and solve them. They will continue to provide the customer with instructions until they fix the issue. If they get stuck, they can notify a human member of the team to take over the conversation.

Social Media Chatbots

Chatbots aren’t limited to company apps and websites. You can also deploy them on social media.

The market for such services is growing. Eighty per cent of consumers, for instance, now use social media to contact brands and there are now more than 300,000 commercial chatbots on Facebook Messenger alone.

The main benefit of social media chatbots is their ability to assist customers at any time of the day, regardless of opening hours. They also help you build authentic relationships with customers by addressing their emotional concerns and collecting information that human reps can then use to solve their problems.

No-Code Chatbots

Lastly, we have so-called “no code” chatbots, among the most advanced currently available. Traditionally, all chatbots needed manual coding, even those using machine learning. They couldn’t develop responses to customer questions from scratch.

Today, though, things are different. No- and low-code chatbots make it easy for organisations to deploy chatbots and put them to work immediately.

No-code deployments are possible for two reasons:

1. Third-party vendors already prepare them for operation
2. Deep learning negates some of the hand-holding required for regular learning

This type of bot is particularly appealing to organisations that need to get chatbot services off the ground quickly. Developers can then refine these bots using customer data.