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Chatbots - Coggle Diagram
Chatbots
Advantages
Chatbots help to retrieve and provide information to customers quickly
Chat conversations can be captured and used for data analysis to boost sales subsequently
Chatbots, manpower can be re-allocated to areas of work which cannot be automated
Chatbots allow organisations to interact with customers 24/7
Chatbots Can Relieve Workload of Human Agents
Chatbot Data can be Analysed
Chatbots can be built through different platforms
Google Dialogflow
Uses natural language processing (NLP)
NLP is how we can train computers to understand and process the natural human language.
Benefits
There are existing templates for building your chatbot
Dialogflow is powered by Google's machine learning and has the cognitive ability to understand and reply in the natural language
Dialogflow requires minimal coding and is suitable for anyone who can effectively use a computer and web broswer
You can build a chatbot all within the Dialogflow cloud platform on a web browser
Agent
virtual agent with the mission to handle conversations with your customers. Once a user sends a text message to the Dialogflow agent, Google uses artificial intelligence to translate the message into structured data with the aid of NLP
Dialogflow agent need to be trained to handle conversational scenarios. Training does not need to be overly explicit.
Intent
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
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..
Entity
When an intent is matched, Dialogflow can extract specific information from the end-user expression. The extracted data is called a parameter or entity type. Parameters and entity types are structured data that can help us make better sense of what the user is trying to find out.
Entities (parameters) are defined data which we wish to pick up from end-user expressions.
We can have more than one Agent in Dialogflow
IBM Watson Assistant
we should more than 4 training phrases per intent
We can have a follow-up intent to a follow-up intent