chatbot is very accurate, 70% - 100% accuracy

Unlike a human being who may need to search through manuals before they can provide a reasonable reply, bots are programmed and can retrieve information and respond within milliseconds. Customers can now be served in a shorter period of time.

In the dead of the night, when a customer decides to ask about your product, your bot is there to take your place. By doing so, the customer is able to get the information he/she needs without having to wait for a human to respond. Bots have a name so that your customers can identify with them. This touches on the affectionate aspect of the human nature, wherein we feel more connected if there is a name and face that we can match to.

What was a 10-minute interaction between a customer and a service agent, is now a 2-minute conversation between the customer and a chatbot with a name and face. The service agent is now able to focus on other tasks.

All the chat conversations can be saved and analysed. We can find out what customers are frequently asking, and better attend to their needs.

building a chatbot within the dialogflow cloud platform on a web browser

requires minimal coding and is suitable for anyone who can effectively use a computer and web browser

there are existing templates for building chatbot

is powered by google machine learning and has the cognitive ability to understand and reply in the natural language

dialogflow agent needs to be built and trained to reply to conversations

dialogflow agent can handle human language

Customers reach out to chatbot agent to acquire certain information. Two different customers may ask the same thing differently, but their intent is the same. For example, if User A asks the chatbot "What time do you open on Saturday?", and User B asks "What are the opening hours on weekends?", both have the same intent of finding out the opening hours.

An intent categorises the customer's intention for a conversation turn. You can define mutiple intents for a single agent, just like how a call centre agent can decide if you need basic troubleshooting, make an appointment, or redirect your phone calls based on what you say.

What a customer types to the chatbot is referred to as an "end-user expression". Dialogflow matches the end-user expression to the best intent in your agent. Matching an intent is also known as intent classification.