How to Build Conversational Bot with Zendesk Live Chat?

A customer is a king. We all truly agree to this. A happy customer adds value to your brand, while the journey of turning an unhappy customer into a happy customer can be hard.

Every time a customer and your agent strikes a conversation it generates that most valuable resource that is data. The right use of data can work wonders otherwise it just remains a user and agent conversation. One of the right ways of using your live chat data/customer data is to convert it into a conversational AI bot. This blog will help you understand how to a conversational bot is made using Zendesk live chat data.

First, let us start by understanding what is a conversational AI?

Conversational AI allows the chatbot to interact with the users in a natural language and help them to learn from conversations. It is the ability of the machine to let the bot learn and perform the activity.

With conversational AI the bot reads every user query. If it is a similar query that has triggered in the past then the reply goes accordingly or if it is a new query the reply is as per user’s requirement. Through conversational AI bot learns every query in detail to get accuracy.

How does the data turn into a bot?

A chatbot is able to hit the nail on the right head by providing the user the convenience of getting instant replies and solution to their queries. What makes the bot effective is the data that you feed. Your data helps the chatbot to understand what are your users saying/asking (their intent) and understand what should be the chatbot’s response to that particular intent.

When you develop a chatbot using our platform (Floatbot), we train the bot under the conversational engine with your data. You can read more about conversational engine by clicking on the link https://bit.ly/2uYCrNx.

The data of your Zendesk live chat that you provide us is mostly in form of zip, text form or pdf. This raw data of yours is fine-tuned and converted into an excel sheet as per the conversation that happened between the user and the agent.

The sheet gives us a clear idea about what was the user’s query and what was the solution given by the agent. This understanding builds a roadmap for training the bot with the live chat data. And then we start to train the chatbot with intents, entities, actions, and conversations the major key elements of building a conversational bot.

Take a look at how we train the bot using each user intent.

This activity helps you in

• Reducing live support- A conversational bot lessens the burden of your live support by answering the repetitive queries effectively saving your time.
• Handling Capacity- At one go your bot can trigger to multiple users with no compromise in quality of output.
• Gain insight- The conversational AI help to read between the lines about user behaviour and pattern.

Allow yourself to explore the possibilities of what your data and a chatbot can do for when they come together. Have your data ready, so are we to build a conversational bot for you. Let us connect and bring to reality. You can write to us at connect@floatbot.ai.