Different
Types of Chat Bots
Contextual chatbots
Contextual chatbots can be used to personalize
customer interactions and deliver the right message at the right time. These
chatbots are often connected to a centralized database, such as a customer
relationship management system (CRM) or a customer data platform. These systems
allow contextual chatbots to retrieve vital information about a user, including
purchase history.
These bots can learn from previous conversations
and make relevant suggestions to the user's query. This helps enterprises
establish a healthy client-customer relationship and loyal customer base.
Contextual chatbots can also retain information on user intent across multiple
channels and platforms. This means that contextual chatbots can personalize
interactions for each consumer at each touchpoint.
Contextual chatbots use artificial intelligence to
understand the context of a conversation. This technology can target website
visitors based on their preferences and search history, and increase the
conversion rate for businesses. They can also store information about a user
and share interactive content with them.
Artificial intelligence-based
chatbots
AI-based chatbots can be useful in a number of
industries, from customer service to marketing. They can handle repetitive
questions quickly and accurately, reducing overall workload. In addition, AI
chatbots are able to answer the vast majority of FAQs. In addition, they can
learn a lot about the customer, allowing for easier marketing and sales.
When implemented on a website, AI-based chatbots
can simulate human interactions with customers. For example, a customer may ask
a question while browsing a website, and the chatbot can pass them onto a live
agent for further assistance. AI-based chatbots can also collect information,
such as customer details, and initiate a conversation.
Many larger organizations have begun developing
AI-based chatbots. These virtual assistants process dynamic user inputs and
deliver customized responses based on the data. AI-based chatbots use machine
learning to become smarter and learn from the data they collect.
Drag-and-drop builder
A drag-and-drop chat bot builder allows you to
create interactive chat bots for various platforms, including Facebook
Messenger and SMS. These chatbots can help your customers and prospects get
answers quickly and easily. You can even connect them with other apps, such as
email, to keep them engaged.
Drag-and-drop chat bot builder tools can be useful
for both newbies and advanced developers. While they are ideal for building
simple FAQ bots, drag-and-drop builders can be used to create more complex
chatbots. However, companies who want more control should consider a
conversational platform where they can pick prebuilt elements and integrations
to customize their chatbot.
A drag-and-drop chat bot builder allows you to
quickly create a chatbot and launch it live. It comes with industry-specific
templates, allowing you to create a custom chatbot with a click of the button.
You can even customize your bot with powerful actions and dynamic responses.
You can also create and test scenarios before launching it live.
Machine learning-based
chatbots
Machine learning-based chatbots are gaining ground
in the business world. These bots use training data to make decisions and are
fast to setup. They draw on concrete queries collected in operational use to
build their knowledge. However, they are limited in their ability to answer
unknown queries. For this reason, companies need to gather a large volume of
training data.
Chatbots have the potential to play an increasingly
human role in businesses, but there are some limitations. While machine
learning networks can automate basic business processes, human interaction is
still required to interpret data and respond appropriately to feedback. Also,
chatbots cannot ask extra questions or perform complex tasks without data,
which limits their usefulness. Furthermore, regulations and compliance issues
may make personalisation difficult.
One of the challenges of machine learning-based
chatbots is that their answers are not predictable. The bots may produce
answers that are incorrect or deformed and may not be helpful. Ideally,
developers should use NLP-based approaches to train their chatbots.
Chat Bots
