Understand Quality Metrics Of Chatbot Training Data: Data Analytics Beyond Training In 2022
A chatbot is a software or a computer program that simulates human communication or "chatter" via text or voice interactions. Chatbot analytics, also known as conversational analytics, chatbot analytics, bot analytics, and chatbot intelligence, is a valuable tool for directing business chatbot trials. This post will help you understand the quality matrix of chatbot training.
What is chatbot analytics?
Users increasingly use chatbot virtual assistants in business-to-consumer and business-to-business environments to conduct simple tasks—chatbot analytics evaluate previous bot conversations to get insights about chatbot performance and customer experience.
The work of a company as a chatbot developer does not finish when their bot goes online. Because of the increased competition in every business, customer experience has become a critical driver in establishing a competitive advantage. After a company introduces a chatbot, it is the right time to monitor how users interact with it.
Understand Quality Metrics Of Chatbot Training Data
Once you recognize how a chatbot works, you can use chatbot analytics and metrics to analyze its success. You can continuously monitor response time, conversion rate, and efficiency enhancement with KPIs to significantly increase it.
Goal Completion Rate: GCR is at the top of our list since it accurately assesses your chatbot's effectiveness by collecting the proportion of successful user interactions with the chatbot.
Engaged Users: These are active users who have daily or weekly discussions with your bot. The active users recognize the value of employing your chatbot. They enjoy utilizing your bot and continue to patronize your company.
Conversation starter messages: Interactions between the consumer and the bot are bidirectional, and the number of times the bot begins the discussion serves as the foundation for the next measure.
Bot Messages: This indicates the total number of messages sent by the bot during a discussion. We want this statistic to be high since it measures the length of the dialogue between the consumer and the bot.
In Messages: This category displays the user's messages. We need to know if the user interacts with the chatbot. We don't need to utilize a chatbot if this category is deficient.
Miss Messages: These are messages that our chatbot is unable to process. This measure may be difficult to compute. The number of times the chatbot misinterprets the input.
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